Parallel human genome analysis: microarray-based expression monitoring of 1000 genes.

PubMed Central

Schena, M; Shalon, D; Heller, R; Chai, A; Brown, P O; Davis, R W

1996-01-01

Microarrays containing 1046 human cDNAs of unknown sequence were printed on glass with high-speed robotics. These 1.0-cm2 DNA "chips" were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. Array elements that displayed differential expression patterns under given experimental conditions were characterized by sequencing. The identification of known and novel heat shock and phorbol ester-regulated genes in human T cells demonstrates the sensitivity of the assay. Parallel gene analysis with microarrays provides a rapid and efficient method for large-scale human gene discovery. Images Fig. 1 Fig. 2 Fig. 3 PMID:8855227

Clustering-based spot segmentation of cDNA microarray images.

PubMed

Uslan, Volkan; Bucak, Ihsan Ömür

2010-01-01

Microarrays are utilized as that they provide useful information about thousands of gene expressions simultaneously. In this study segmentation step of microarray image processing has been implemented. Clustering-based methods, fuzzy c-means and k-means, have been applied for the segmentation step that separates the spots from the background. The experiments show that fuzzy c-means have segmented spots of the microarray image more accurately than the k-means.

Bacterial identification and subtyping using DNA microarray and DNA sequencing.

PubMed

Al-Khaldi, Sufian F; Mossoba, Magdi M; Allard, Marc M; Lienau, E Kurt; Brown, Eric D

2012-01-01

The era of fast and accurate discovery of biological sequence motifs in prokaryotic and eukaryotic cells is here. The co-evolution of direct genome sequencing and DNA microarray strategies not only will identify, isotype, and serotype pathogenic bacteria, but also it will aid in the discovery of new gene functions by detecting gene expressions in different diseases and environmental conditions. Microarray bacterial identification has made great advances in working with pure and mixed bacterial samples. The technological advances have moved beyond bacterial gene expression to include bacterial identification and isotyping. Application of new tools such as mid-infrared chemical imaging improves detection of hybridization in DNA microarrays. The research in this field is promising and future work will reveal the potential of infrared technology in bacterial identification. On the other hand, DNA sequencing by using 454 pyrosequencing is so cost effective that the promise of $1,000 per bacterial genome sequence is becoming a reality. Pyrosequencing technology is a simple to use technique that can produce accurate and quantitative analysis of DNA sequences with a great speed. The deposition of massive amounts of bacterial genomic information in databanks is creating fingerprint phylogenetic analysis that will ultimately replace several technologies such as Pulsed Field Gel Electrophoresis. In this chapter, we will review (1) the use of DNA microarray using fluorescence and infrared imaging detection for identification of pathogenic bacteria, and (2) use of pyrosequencing in DNA cluster analysis to fingerprint bacterial phylogenetic trees.

High-density fiber-optic DNA random microsphere array.

PubMed

Ferguson, J A; Steemers, F J; Walt, D R

2000-11-15

A high-density fiber-optic DNA microarray sensor was developed to monitor multiple DNA sequences in parallel. Microarrays were prepared by randomly distributing DNA probe-functionalized 3.1-microm-diameter microspheres in an array of wells etched in a 500-microm-diameter optical imaging fiber. Registration of the microspheres was performed using an optical encoding scheme and a custom-built imaging system. Hybridization was visualized using fluorescent-labeled DNA targets with a detection limit of 10 fM. Hybridization times of seconds are required for nanomolar target concentrations, and analysis is performed in minutes.

A Java-based tool for the design of classification microarrays.

PubMed

Meng, Da; Broschat, Shira L; Call, Douglas R

2008-08-04

Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for analysis of subsequent experimental data. Additionally, PLASMID can be used to construct virtual microarrays with genomes from public databases, which can then be used to identify an optimal set of probes.

Feasibility Analysis and Demonstration of High-Speed Digital Imaging Using Micro-Arrays of Vertical Cavity Surface-Emitting Lasers

DTIC Science & Technology

2011-04-01

Proceedings, Bristol, UK (2006). 5. M. A. Mentzer, Applied Optics Fundamentals and Device Applications: Nano, MOEMS , and Biotechnology, CRC Taylor...ballistic sensing, flash x-ray cineradiography, digital image correlation, image processing al- gorithms, and applications of MOEMS to nano- and

Estimating gene function with least squares nonnegative matrix factorization.

PubMed

Wang, Guoli; Ochs, Michael F

2007-01-01

Nonnegative matrix factorization is a machine learning algorithm that has extracted information from data in a number of fields, including imaging and spectral analysis, text mining, and microarray data analysis. One limitation with the method for linking genes through microarray data in order to estimate gene function is the high variance observed in transcription levels between different genes. Least squares nonnegative matrix factorization uses estimates of the uncertainties on the mRNA levels for each gene in each condition, to guide the algorithm to a local minimum in normalized chi2, rather than a Euclidean distance or divergence between the reconstructed data and the data itself. Herein, application of this method to microarray data is demonstrated in order to predict gene function.

The analysis of image feature robustness using cometcloud

PubMed Central

Qi, Xin; Kim, Hyunjoo; Xing, Fuyong; Parashar, Manish; Foran, David J.; Yang, Lin

2012-01-01

The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval. PMID:23248759

Scanning microarray slides.

PubMed

Ares, Manuel

2014-02-01

Here we describe some practical concerns surrounding the scanning of microarray slides that have been hybridized with fluorescent dyes. We use a laser scanner that has two lasers, each set to excite a different fluor, and separate detectors to capture emission from each fluor. The laser passes over an address (position on the scanned surface) and the detectors capture photons emitted from each address. Two superimposed image files are written that carry intensities for each channel for each pixel of the image scan. These are the raw data. Image analysis software is used to identify and summarize the intensities of the pixels that make up each spot. After comparison to background pixels, the processed intensity levels representing the gene expression measurements are associated with the identity of each spot.

An Advanced Approach to Simultaneous Monitoring of Multiple Bacteria in Space

NASA Technical Reports Server (NTRS)

Eggers, M.

1998-01-01

The utility of a novel microarray-based microbial analyzer was demonstrated by the rapid detection, imaging, and identification of a mixture of microorganisms found in a waste water sample from the Lunar-Mars Life Support Test Project through the synergistic combination of: (1) judicious RNA probe selection via algorithms developed by University of Houston scientists; (2) tuned surface chemistries developed by Baylor College of Medicine scientists to facilitate hybridization of rRNA targets to DNA probes under very low salt conditions, thereby minimizing secondary structure; and (3) integration of the microarray printing and detection/imaging instrumentation by Genometrix to complete the quantitative analysis of microorganism mixtures.

Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium

PubMed Central

Howat, William J; Blows, Fiona M; Provenzano, Elena; Brook, Mark N; Morris, Lorna; Gazinska, Patrycja; Johnson, Nicola; McDuffus, Leigh‐Anne; Miller, Jodi; Sawyer, Elinor J; Pinder, Sarah; van Deurzen, Carolien H M; Jones, Louise; Sironen, Reijo; Visscher, Daniel; Caldas, Carlos; Daley, Frances; Coulson, Penny; Broeks, Annegien; Sanders, Joyce; Wesseling, Jelle; Nevanlinna, Heli; Fagerholm, Rainer; Blomqvist, Carl; Heikkilä, Päivi; Ali, H Raza; Dawson, Sarah‐Jane; Figueroa, Jonine; Lissowska, Jolanta; Brinton, Louise; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli‐Matti; Cox, Angela; Brock, Ian W; Cross, Simon S; Reed, Malcolm W; Couch, Fergus J; Olson, Janet E; Devillee, Peter; Mesker, Wilma E; Seyaneve, Caroline M; Hollestelle, Antoinette; Benitez, Javier; Perez, Jose Ignacio Arias; Menéndez, Primitiva; Bolla, Manjeet K; Easton, Douglas F; Schmidt, Marjanka K; Pharoah, Paul D; Sherman, Mark E

2014-01-01

Abstract Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large‐scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65–70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose‐response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96–98%), but yielded many false positives (positive predictive value = 30–32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large‐scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker‐specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results. PMID:27499890

Crossword: A Fully Automated Algorithm for the Segmentation and Quality Control of Protein Microarray Images

PubMed Central

2015-01-01

Biological assays formatted as microarrays have become a critical tool for the generation of the comprehensive data sets required for systems-level understanding of biological processes. Manual annotation of data extracted from images of microarrays, however, remains a significant bottleneck, particularly for protein microarrays due to the sensitivity of this technology to weak artifact signal. In order to automate the extraction and curation of data from protein microarrays, we describe an algorithm called Crossword that logically combines information from multiple approaches to fully automate microarray segmentation. Automated artifact removal is also accomplished by segregating structured pixels from the background noise using iterative clustering and pixel connectivity. Correlation of the location of structured pixels across image channels is used to identify and remove artifact pixels from the image prior to data extraction. This component improves the accuracy of data sets while reducing the requirement for time-consuming visual inspection of the data. Crossword enables a fully automated protocol that is robust to significant spatial and intensity aberrations. Overall, the average amount of user intervention is reduced by an order of magnitude and the data quality is increased through artifact removal and reduced user variability. The increase in throughput should aid the further implementation of microarray technologies in clinical studies. PMID:24417579

BioconductorBuntu: a Linux distribution that implements a web-based DNA microarray analysis server.

PubMed

Geeleher, Paul; Morris, Dermot; Hinde, John P; Golden, Aaron

2009-06-01

BioconductorBuntu is a custom distribution of Ubuntu Linux that automatically installs a server-side microarray processing environment, providing a user-friendly web-based GUI to many of the tools developed by the Bioconductor Project, accessible locally or across a network. System installation is via booting off a CD image or by using a Debian package provided to upgrade an existing Ubuntu installation. In its current version, several microarray analysis pipelines are supported including oligonucleotide, dual-or single-dye experiments, including post-processing with Gene Set Enrichment Analysis. BioconductorBuntu is designed to be extensible, by server-side integration of further relevant Bioconductor modules as required, facilitated by its straightforward underlying Python-based infrastructure. BioconductorBuntu offers an ideal environment for the development of processing procedures to facilitate the analysis of next-generation sequencing datasets. BioconductorBuntu is available for download under a creative commons license along with additional documentation and a tutorial from (http://bioinf.nuigalway.ie).

Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB.

PubMed

Chatziioannou, Aristotelis; Moulos, Panagiotis; Kolisis, Fragiskos N

2009-10-27

The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods. We describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime. Gene ARMADA provides a highly adaptable, integrative, yet flexible tool which can be used for automated quality control, analysis, annotation and visualization of microarray data, constituting a starting point for further data interpretation and integration with numerous other tools.

Single molecule fluorescence microscopy for ultra-sensitive RNA expression profiling

NASA Astrophysics Data System (ADS)

Hesse, Jan; Jacak, Jaroslaw; Regl, Gerhard; Eichberger, Thomas; Aberger, Fritz; Schlapak, Robert; Howorka, Stefan; Muresan, Leila; Frischauf, Anna-Maria; Schütz, Gerhard J.

2007-02-01

We developed a microarray analysis platform for ultra-sensitive RNA expression profiling of minute samples. It utilizes a novel scanning system for single molecule fluorescence detection on cm2 size samples in combination with specialized biochips, optimized for low autofluorescence and weak unspecific adsorption. 20 μg total RNA was extracted from 10 6 cells of a human keratinocyte cell line (HaCaT) and reversely transcribed in the presence of Alexa647-aha-dUTP. 1% of the resulting labeled cDNA was used for complex hybridization to a custom-made oligonucleotide microarray representing a set of 125 different genes. For low abundant genes, individual cDNA molecules hybridized to the microarray spots could be resolved. Single cDNA molecules hybridized to the chip surface appeared as diffraction limited features in the fluorescence images. The à trous wavelet method was utilized for localization and counting of the separated cDNA signals. Subsequently, the degree of labeling of the localized cDNA molecules was determined by brightness analysis for the different genes. Variations by factors up to 6 were found, which in conventional microarray analysis would result in a misrepresentation of the relative abundance of mRNAs.

EDGE3: A web-based solution for management and analysis of Agilent two color microarray experiments

PubMed Central

Vollrath, Aaron L; Smith, Adam A; Craven, Mark; Bradfield, Christopher A

2009-01-01

Background The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE3 was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. Results EDGE3 has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE3 has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE3 is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Conclusion Here, we present EDGE3, an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE3 provides a means for managing RNA samples and arrays during the hybridization process. EDGE3 is freely available for download at . PMID:19732451

EDGE(3): a web-based solution for management and analysis of Agilent two color microarray experiments.

PubMed

Vollrath, Aaron L; Smith, Adam A; Craven, Mark; Bradfield, Christopher A

2009-09-04

The ability to generate transcriptional data on the scale of entire genomes has been a boon both in the improvement of biological understanding and in the amount of data generated. The latter, the amount of data generated, has implications when it comes to effective storage, analysis and sharing of these data. A number of software tools have been developed to store, analyze, and share microarray data. However, a majority of these tools do not offer all of these features nor do they specifically target the commonly used two color Agilent DNA microarray platform. Thus, the motivating factor for the development of EDGE(3) was to incorporate the storage, analysis and sharing of microarray data in a manner that would provide a means for research groups to collaborate on Agilent-based microarray experiments without a large investment in software-related expenditures or extensive training of end-users. EDGE(3) has been developed with two major functions in mind. The first function is to provide a workflow process for the generation of microarray data by a research laboratory or a microarray facility. The second is to store, analyze, and share microarray data in a manner that doesn't require complicated software. To satisfy the first function, EDGE3 has been developed as a means to establish a well defined experimental workflow and information system for microarray generation. To satisfy the second function, the software application utilized as the user interface of EDGE(3) is a web browser. Within the web browser, a user is able to access the entire functionality, including, but not limited to, the ability to perform a number of bioinformatics based analyses, collaborate between research groups through a user-based security model, and access to the raw data files and quality control files generated by the software used to extract the signals from an array image. Here, we present EDGE(3), an open-source, web-based application that allows for the storage, analysis, and controlled sharing of transcription-based microarray data generated on the Agilent DNA platform. In addition, EDGE(3) provides a means for managing RNA samples and arrays during the hybridization process. EDGE(3) is freely available for download at http://edge.oncology.wisc.edu/.

Microarray Я US: a user-friendly graphical interface to Bioconductor tools that enables accurate microarray data analysis and expedites comprehensive functional analysis of microarray results.

PubMed

Dai, Yilin; Guo, Ling; Li, Meng; Chen, Yi-Bu

2012-06-08

Microarray data analysis presents a significant challenge to researchers who are unable to use the powerful Bioconductor and its numerous tools due to their lack of knowledge of R language. Among the few existing software programs that offer a graphic user interface to Bioconductor packages, none have implemented a comprehensive strategy to address the accuracy and reliability issue of microarray data analysis due to the well known probe design problems associated with many widely used microarray chips. There is also a lack of tools that would expedite the functional analysis of microarray results. We present Microarray Я US, an R-based graphical user interface that implements over a dozen popular Bioconductor packages to offer researchers a streamlined workflow for routine differential microarray expression data analysis without the need to learn R language. In order to enable a more accurate analysis and interpretation of microarray data, we incorporated the latest custom probe re-definition and re-annotation for Affymetrix and Illumina chips. A versatile microarray results output utility tool was also implemented for easy and fast generation of input files for over 20 of the most widely used functional analysis software programs. Coupled with a well-designed user interface, Microarray Я US leverages cutting edge Bioconductor packages for researchers with no knowledge in R language. It also enables a more reliable and accurate microarray data analysis and expedites downstream functional analysis of microarray results.

On-Chip Synthesis of Protein Microarrays from DNA Microarrays Via Coupled In Vitro Transcription and Translation for Surface Plasmon Resonance Imaging Biosensor Applications

PubMed Central

Seefeld, Ting H.; Halpern, Aaron R.; Corn, Robert M.

2012-01-01

Protein microarrays are fabricated from double-stranded DNA (dsDNA) microarrays by a one-step, multiplexed enzymatic synthesis in an on-chip microfluidic format and then employed for antibody biosensing measurements with surface plasmon resonance imaging (SPRI). A microarray of dsDNA elements (denoted as generator elements) that encode either a His-tagged green fluorescent protein (GFP) or a His-tagged luciferase protein is utilized to create multiple copies of messenger RNA (mRNA) in a surface RNA polymerase reaction; the mRNA transcripts are then translated into proteins by cell-free protein synthesis in a microfluidic format. The His-tagged proteins diffuse to adjacent Cu(II)-NTA microarray elements (denoted as detector elements) and are specifically adsorbed. The net result is the on-chip, cell-free synthesis of a protein microarray that can be used immediately for SPRI protein biosensing. The dual element format greatly reduces any interference from the nonspecific adsorption of enzyme or proteins. SPRI measurements for the detection of the antibodies anti-GFP and anti-luciferase were used to verify the formation of the protein microarray. This convenient on-chip protein microarray fabrication method can be implemented for multiplexed SPRI biosensing measurements in both clinical and research applications. PMID:22793370

A hybrid approach to device integration on a genetic analysis platform

NASA Astrophysics Data System (ADS)

Brennan, Des; Jary, Dorothee; Kurg, Ants; Berik, Evgeny; Justice, John; Aherne, Margaret; Macek, Milan; Galvin, Paul

2012-10-01

Point-of-care (POC) systems require significant component integration to implement biochemical protocols associated with molecular diagnostic assays. Hybrid platforms where discrete components are combined in a single platform are a suitable approach to integration, where combining multiple device fabrication steps on a single substrate is not possible due to incompatible or costly fabrication steps. We integrate three devices each with a specific system functionality: (i) a silicon electro-wetting-on-dielectric (EWOD) device to move and mix sample and reagent droplets in an oil phase, (ii) a polymer microfluidic chip containing channels and reservoirs and (iii) an aqueous phase glass microarray for fluorescence microarray hybridization detection. The EWOD device offers the possibility of fully integrating on-chip sample preparation using nanolitre sample and reagent volumes. A key challenge is sample transfer from the oil phase EWOD device to the aqueous phase microarray for hybridization detection. The EWOD device, waveguide performance and functionality are maintained during the integration process. An on-chip biochemical protocol for arrayed primer extension (APEX) was implemented for single nucleotide polymorphism (SNiP) analysis. The prepared sample is aspirated from the EWOD oil phase to the aqueous phase microarray for hybridization. A bench-top instrumentation system was also developed around the integrated platform to drive the EWOD electrodes, implement APEX sample heating and image the microarray after hybridization.

Practical considerations of image analysis and quantification of signal transduction IHC staining.

PubMed

Grunkin, Michael; Raundahl, Jakob; Foged, Niels T

2011-01-01

The dramatic increase in computer processing power in combination with the availability of high-quality digital cameras during the last 10 years has fertilized the grounds for quantitative microscopy based on digital image analysis. With the present introduction of robust scanners for whole slide imaging in both research and routine, the benefits of automation and objectivity in the analysis of tissue sections will be even more obvious. For in situ studies of signal transduction, the combination of tissue microarrays, immunohistochemistry, digital imaging, and quantitative image analysis will be central operations. However, immunohistochemistry is a multistep procedure including a lot of technical pitfalls leading to intra- and interlaboratory variability of its outcome. The resulting variations in staining intensity and disruption of original morphology are an extra challenge for the image analysis software, which therefore preferably should be dedicated to the detection and quantification of histomorphometrical end points.

Calibration and assessment of channel-specific biases in microarray data with extended dynamical range.

PubMed

Bengtsson, Henrik; Jönsson, Göran; Vallon-Christersson, Johan

2004-11-12

Non-linearities in observed log-ratios of gene expressions, also known as intensity dependent log-ratios, can often be accounted for by global biases in the two channels being compared. Any step in a microarray process may introduce such offsets and in this article we study the biases introduced by the microarray scanner and the image analysis software. By scanning the same spotted oligonucleotide microarray at different photomultiplier tube (PMT) gains, we have identified a channel-specific bias present in two-channel microarray data. For the scanners analyzed it was in the range of 15-25 (out of 65,535). The observed bias was very stable between subsequent scans of the same array although the PMT gain was greatly adjusted. This indicates that the bias does not originate from a step preceding the scanner detector parts. The bias varies slightly between arrays. When comparing estimates based on data from the same array, but from different scanners, we have found that different scanners introduce different amounts of bias. So do various image analysis methods. We propose a scanning protocol and a constrained affine model that allows us to identify and estimate the bias in each channel. Backward transformation removes the bias and brings the channels to the same scale. The result is that systematic effects such as intensity dependent log-ratios are removed, but also that signal densities become much more similar. The average scan, which has a larger dynamical range and greater signal-to-noise ratio than individual scans, can then be obtained. The study shows that microarray scanners may introduce a significant bias in each channel. Such biases have to be calibrated for, otherwise systematic effects such as intensity dependent log-ratios will be observed. The proposed scanning protocol and calibration method is simple to use and is useful for evaluating scanner biases or for obtaining calibrated measurements with extended dynamical range and better precision. The cross-platform R package aroma, which implements all described methods, is available for free from http://www.maths.lth.se/bioinformatics/.

The tissue microarray data exchange specification: Extending TMA DES to provide flexible scoring and incorporate virtual slides

PubMed Central

Wright, Alexander; Lyttleton, Oliver; Lewis, Paul; Quirke, Philip; Treanor, Darren

2011-01-01

Background: Tissue MicroArrays (TMAs) are a high throughput technology for rapid analysis of protein expression across hundreds of patient samples. Often, data relating to TMAs is specific to the clinical trial or experiment it is being used for, and not interoperable. The Tissue Microarray Data Exchange Specification (TMA DES) is a set of eXtensible Markup Language (XML)-based protocols for storing and sharing digitized Tissue Microarray data. XML data are enclosed by named tags which serve as identifiers. These tag names can be Common Data Elements (CDEs), which have a predefined meaning or semantics. By using this specification in a laboratory setting with increasing demands for digital pathology integration, we found that the data structure lacked the ability to cope with digital slide imaging in respect to web-enabled digital pathology systems and advanced scoring techniques. Materials and Methods: By employing user centric design, and observing behavior in relation to TMA scoring and associated data, the TMA DES format was extended to accommodate the current limitations. This was done with specific focus on developing a generic tool for handling any given scoring system, and utilizing data for multiple observations and observers. Results: DTDs were created to validate the extensions of the TMA DES protocol, and a test set of data containing scores for 6,708 TMA core images was generated. The XML was then read into an image processing algorithm to utilize the digital pathology data extensions, and scoring results were easily stored alongside the existing multiple pathologist scores. Conclusions: By extending the TMA DES format to include digital pathology data and customizable scoring systems for TMAs, the new system facilitates the collaboration between pathologists and organizations, and can be used in automatic or manual data analysis. This allows complying systems to effectively communicate complex and varied scoring data. PMID:21572508

Multiplexed fluorescent microarray for human salivary protein analysis using polymer microspheres and fiber-optic bundles.

PubMed

Nie, Shuai; Benito-Peña, Elena; Zhang, Huaibin; Wu, Yue; Walt, David R

2013-10-10

Herein, we describe a protocol for simultaneously measuring six proteins in saliva using a fiber-optic microsphere-based antibody array. The immuno-array technology employed combines the advantages of microsphere-based suspension array fabrication with the use of fluorescence microscopy. As described in the video protocol, commercially available 4.5 μm polymer microspheres were encoded into seven different types, differentiated by the concentration of two fluorescent dyes physically trapped inside the microspheres. The encoded microspheres containing surface carboxyl groups were modified with monoclonal capture antibodies through EDC/NHS coupling chemistry. To assemble the protein microarray, the different types of encoded and functionalized microspheres were mixed and randomly deposited in 4.5 μm microwells, which were chemically etched at the proximal end of a fiber-optic bundle. The fiber-optic bundle was used as both a carrier and for imaging the microspheres. Once assembled, the microarray was used to capture proteins in the saliva supernatant collected from the clinic. The detection was based on a sandwich immunoassay using a mixture of biotinylated detection antibodies for different analytes with a streptavidin-conjugated fluorescent probe, R-phycoerythrin. The microarray was imaged by fluorescence microscopy in three different channels, two for microsphere registration and one for the assay signal. The fluorescence micrographs were then decoded and analyzed using a homemade algorithm in MATLAB.

Integrated Microfluidic Devices for Automated Microarray-Based Gene Expression and Genotyping Analysis

NASA Astrophysics Data System (ADS)

Liu, Robin H.; Lodes, Mike; Fuji, H. Sho; Danley, David; McShea, Andrew

Microarray assays typically involve multistage sample processing and fluidic handling, which are generally labor-intensive and time-consuming. Automation of these processes would improve robustness, reduce run-to-run and operator-to-operator variation, and reduce costs. In this chapter, a fully integrated and self-contained microfluidic biochip device that has been developed to automate the fluidic handling steps for microarray-based gene expression or genotyping analysis is presented. The device consists of a semiconductor-based CustomArray® chip with 12,000 features and a microfluidic cartridge. The CustomArray was manufactured using a semiconductor-based in situ synthesis technology. The micro-fluidic cartridge consists of microfluidic pumps, mixers, valves, fluid channels, and reagent storage chambers. Microarray hybridization and subsequent fluidic handling and reactions (including a number of washing and labeling steps) were performed in this fully automated and miniature device before fluorescent image scanning of the microarray chip. Electrochemical micropumps were integrated in the cartridge to provide pumping of liquid solutions. A micromixing technique based on gas bubbling generated by electrochemical micropumps was developed. Low-cost check valves were implemented in the cartridge to prevent cross-talk of the stored reagents. Gene expression study of the human leukemia cell line (K562) and genotyping detection and sequencing of influenza A subtypes have been demonstrated using this integrated biochip platform. For gene expression assays, the microfluidic CustomArray device detected sample RNAs with a concentration as low as 0.375 pM. Detection was quantitative over more than three orders of magnitude. Experiment also showed that chip-to-chip variability was low indicating that the integrated microfluidic devices eliminate manual fluidic handling steps that can be a significant source of variability in genomic analysis. The genotyping results showed that the device identified influenza A hemagglutinin and neuraminidase subtypes and sequenced portions of both genes, demonstrating the potential of integrated microfluidic and microarray technology for multiple virus detection. The device provides a cost-effective solution to eliminate labor-intensive and time-consuming fluidic handling steps and allows microarray-based DNA analysis in a rapid and automated fashion.

An interferometric imaging biosensor using weighted spectrum analysis to confirm DNA monolayer films with attogram sensitivity.

PubMed

Fu, Rongxin; Li, Qi; Wang, Ruliang; Xue, Ning; Lin, Xue; Su, Ya; Jiang, Kai; Jin, Xiangyu; Lin, Rongzan; Gan, Wupeng; Lu, Ying; Huang, Guoliang

2018-05-01

Interferometric imaging biosensors are powerful and convenient tools for confirming the existence of DNA monolayer films on silicon microarray platforms. However, their accuracy and sensitivity need further improvement because DNA molecules contribute to an inconspicuous interferometric signal both in thickness and size. Such weaknesses result in poor performance of these biosensors for low DNA content analyses and point mutation tests. In this paper, an interferometric imaging biosensor with weighted spectrum analysis is presented to confirm DNA monolayer films. The interferometric signal of DNA molecules can be extracted and then quantitative detection results for DNA microarrays can be reconstructed. With the proposed strategy, the relative error of thickness detection was reduced from 88.94% to merely 4.15%. The mass sensitivity per unit area of the proposed biosensor reached 20 attograms (ag). Therefore, the sample consumption per unit area of the target DNA content was only 62.5 zeptomoles (zm), with the volume of 0.25 picolitres (pL). Compared with the fluorescence resonance energy transfer (FRET), the measurement veracity of the interferometric imaging biosensor with weighted spectrum analysis is free to the changes in spotting concentration and DNA length. The detection range was more than 1µm. Moreover, single nucleotide mismatch could be pointed out combined with specific DNA ligation. A mutation experiment for lung cancer detection proved the high selectivity and accurate analysis capability of the presented biosensor. Copyright © 2017 Elsevier B.V. All rights reserved.

Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

NASA Astrophysics Data System (ADS)

Bychkov, Dmitrii; Turkki, Riku; Haglund, Caj; Linder, Nina; Lundin, Johan

2016-03-01

Recent advances in computer vision enable increasingly accurate automated pattern classification. In the current study we evaluate whether a convolutional neural network (CNN) can be trained to predict disease outcome in patients with colorectal cancer based on images of tumor tissue microarray samples. We compare the prognostic accuracy of CNN features extracted from the whole, unsegmented tissue microarray spot image, with that of CNN features extracted from the epithelial and non-epithelial compartments, respectively. The prognostic accuracy of visually assessed histologic grade is used as a reference. The image data set consists of digitized hematoxylin-eosin (H and E) stained tissue microarray samples obtained from 180 patients with colorectal cancer. The patient samples represent a variety of histological grades, have data available on a series of clinicopathological variables including long-term outcome and ground truth annotations performed by experts. The CNN features extracted from images of the epithelial tissue compartment significantly predicted outcome (hazard ratio (HR) 2.08; CI95% 1.04-4.16; area under the curve (AUC) 0.66) in a test set of 60 patients, as compared to the CNN features extracted from unsegmented images (HR 1.67; CI95% 0.84-3.31, AUC 0.57) and visually assessed histologic grade (HR 1.96; CI95% 0.99-3.88, AUC 0.61). As a conclusion, a deep-learning classifier can be trained to predict outcome of colorectal cancer based on images of H and E stained tissue microarray samples and the CNN features extracted from the epithelial compartment only resulted in a prognostic discrimination comparable to that of visually determined histologic grade.

Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE).

PubMed

Deutsch, Eric W; Ball, Catherine A; Berman, Jules J; Bova, G Steven; Brazma, Alvis; Bumgarner, Roger E; Campbell, David; Causton, Helen C; Christiansen, Jeffrey H; Daian, Fabrice; Dauga, Delphine; Davidson, Duncan R; Gimenez, Gregory; Goo, Young Ah; Grimmond, Sean; Henrich, Thorsten; Herrmann, Bernhard G; Johnson, Michael H; Korb, Martin; Mills, Jason C; Oudes, Asa J; Parkinson, Helen E; Pascal, Laura E; Pollet, Nicolas; Quackenbush, John; Ramialison, Mirana; Ringwald, Martin; Salgado, David; Sansone, Susanna-Assunta; Sherlock, Gavin; Stoeckert, Christian J; Swedlow, Jason; Taylor, Ronald C; Walashek, Laura; Warford, Anthony; Wilkinson, David G; Zhou, Yi; Zon, Leonard I; Liu, Alvin Y; True, Lawrence D

2008-03-01

One purpose of the biomedical literature is to report results in sufficient detail that the methods of data collection and analysis can be independently replicated and verified. Here we present reporting guidelines for gene expression localization experiments: the minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE). MISFISHIE is modeled after the Minimum Information About a Microarray Experiment (MIAME) specification for microarray experiments. Both guidelines define what information should be reported without dictating a format for encoding that information. MISFISHIE describes six types of information to be provided for each experiment: experimental design, biomaterials and treatments, reporters, staining, imaging data and image characterizations. This specification has benefited the consortium within which it was developed and is expected to benefit the wider research community. We welcome feedback from the scientific community to help improve our proposal.

A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients

PubMed Central

Zhong, Qing; Guo, Tiannan; Rechsteiner, Markus; Rüschoff, Jan H.; Rupp, Niels; Fankhauser, Christian; Saba, Karim; Mortezavi, Ashkan; Poyet, Cédric; Hermanns, Thomas; Zhu, Yi; Moch, Holger; Aebersold, Ruedi; Wild, Peter J.

2017-01-01

Microscopy image data of human cancers provide detailed phenotypes of spatially and morphologically intact tissues at single-cell resolution, thus complementing large-scale molecular analyses, e.g., next generation sequencing or proteomic profiling. Here we describe a high-resolution tissue microarray (TMA) image dataset from a cohort of 71 prostate tissue samples, which was hybridized with bright-field dual colour chromogenic and silver in situ hybridization probes for the tumour suppressor gene PTEN. These tissue samples were digitized and supplemented with expert annotations, clinical information, statistical models of PTEN genetic status, and computer source codes. For validation, we constructed an additional TMA dataset for 424 prostate tissues, hybridized with FISH probes for PTEN, and performed survival analysis on a subset of 339 radical prostatectomy specimens with overall, disease-specific and recurrence-free survival (maximum 167 months). For application, we further produced 6,036 image patches derived from two whole slides. Our curated collection of prostate cancer data sets provides reuse potential for both biomedical and computational studies. PMID:28291248

Mixed Membership Distributions with Applications to Modeling Multiple Strategy Usage

ERIC Educational Resources Information Center

Galyardt, April

2012-01-01

This dissertation examines two related questions. "How do mixed membership models work?" and "Can mixed membership be used to model how students use multiple strategies to solve problems?". Mixed membership models have been used in thousands of applications from text and image processing to genetic microarray analysis. Yet…

GP3: GENEPIX POST-PROCESSING SCRIPT FOR THE AUTOMATED ANALYSIS OF RAW MICROARRAY IMAGE OUTPUT FILES. (R827402)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Reverse phase protein microarrays: fluorometric and colorimetric detection.

PubMed

Gallagher, Rosa I; Silvestri, Alessandra; Petricoin, Emanuel F; Liotta, Lance A; Espina, Virginia

2011-01-01

The Reverse Phase Protein Microarray (RPMA) is an array platform used to quantitate proteins and their posttranslationally modified forms. RPMAs are applicable for profiling key cellular signaling pathways and protein networks, allowing direct comparison of the activation state of proteins from multiple samples within the same array. The RPMA format consists of proteins immobilized directly on a nitrocellulose substratum. The analyte is subsequently probed with a primary antibody and a series of reagents for signal amplification and detection. Due to the diversity, low concentration, and large dynamic range of protein analytes, RPMAs require stringent signal amplification methods, high quality image acquisition, and software capable of precisely analyzing spot intensities on an array. Microarray detection strategies can be either fluorescent or colorimetric. The choice of a detection system depends on (a) the expected analyte concentration, (b) type of microarray imaging system, and (c) type of sample. The focus of this chapter is to describe RPMA detection and imaging using fluorescent and colorimetric (diaminobenzidine (DAB)) methods.

Analysis of High-Throughput ELISA Microarray Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

White, Amanda M.; Daly, Don S.; Zangar, Richard C.

Our research group develops analytical methods and software for the high-throughput analysis of quantitative enzyme-linked immunosorbent assay (ELISA) microarrays. ELISA microarrays differ from DNA microarrays in several fundamental aspects and most algorithms for analysis of DNA microarray data are not applicable to ELISA microarrays. In this review, we provide an overview of the steps involved in ELISA microarray data analysis and how the statistically sound algorithms we have developed provide an integrated software suite to address the needs of each data-processing step. The algorithms discussed are available in a set of open-source software tools (http://www.pnl.gov/statistics/ProMAT).

EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

PubMed Central

Zhu, Yuerong; Zhu, Yuelin; Xu, Wei

2008-01-01

Background Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand. Results EzArray is a web-based Affymetrix expression array data management and analysis system for researchers who need to organize microarray data efficiently and get data analyzed instantly. EzArray organizes microarray data into projects that can be analyzed online with predefined or custom procedures. EzArray performs data preprocessing and detection of differentially expressed genes with statistical methods. All analysis procedures are optimized and highly automated so that even novice users with limited pre-knowledge of microarray data analysis can complete initial analysis quickly. Since all input files, analysis parameters, and executed scripts can be downloaded, EzArray provides maximum reproducibility for each analysis. In addition, EzArray integrates with Gene Expression Omnibus (GEO) and allows instantaneous re-analysis of published array data. Conclusion EzArray is a novel Affymetrix expression array data analysis and sharing system. EzArray provides easy-to-use tools for re-analyzing published microarray data and will help both novice and experienced users perform initial analysis of their microarray data from the location of data storage. We believe EzArray will be a useful system for facilities with microarray services and laboratories with multiple members involved in microarray data analysis. EzArray is freely available from . PMID:18218103

Fiber-optic microarray for simultaneous detection of multiple harmful algal bloom species.

PubMed

Ahn, Soohyoun; Kulis, David M; Erdner, Deana L; Anderson, Donald M; Walt, David R

2006-09-01

Harmful algal blooms (HABs) are a serious threat to coastal resources, causing a variety of impacts on public health, regional economies, and ecosystems. Plankton analysis is a valuable component of many HAB monitoring and research programs, but the diversity of plankton poses a problem in discriminating toxic from nontoxic species using conventional detection methods. Here we describe a sensitive and specific sandwich hybridization assay that combines fiber-optic microarrays with oligonucleotide probes to detect and enumerate the HAB species Alexandrium fundyense, Alexandrium ostenfeldii, and Pseudo-nitzschia australis. Microarrays were prepared by loading oligonucleotide probe-coupled microspheres (diameter, 3 mum) onto the distal ends of chemically etched imaging fiber bundles. Hybridization of target rRNA from HAB cells to immobilized probes on the microspheres was visualized using Cy3-labeled secondary probes in a sandwich-type assay format. We applied these microarrays to the detection and enumeration of HAB cells in both cultured and field samples. Our study demonstrated a detection limit of approximately 5 cells for all three target organisms within 45 min, without a separate amplification step, in both sample types. We also developed a multiplexed microarray to detect the three HAB species simultaneously, which successfully detected the target organisms, alone and in combination, without cross-reactivity. Our study suggests that fiber-optic microarrays can be used for rapid and sensitive detection and potential enumeration of HAB species in the environment.

Chemiluminescence microarrays in analytical chemistry: a critical review.

PubMed

Seidel, Michael; Niessner, Reinhard

2014-09-01

Multi-analyte immunoassays on microarrays and on multiplex DNA microarrays have been described for quantitative analysis of small organic molecules (e.g., antibiotics, drugs of abuse, small molecule toxins), proteins (e.g., antibodies or protein toxins), and microorganisms, viruses, and eukaryotic cells. In analytical chemistry, multi-analyte detection by use of analytical microarrays has become an innovative research topic because of the possibility of generating several sets of quantitative data for different analyte classes in a short time. Chemiluminescence (CL) microarrays are powerful tools for rapid multiplex analysis of complex matrices. A wide range of applications for CL microarrays is described in the literature dealing with analytical microarrays. The motivation for this review is to summarize the current state of CL-based analytical microarrays. Combining analysis of different compound classes on CL microarrays reduces analysis time, cost of reagents, and use of laboratory space. Applications are discussed, with examples from food safety, water safety, environmental monitoring, diagnostics, forensics, toxicology, and biosecurity. The potential and limitations of research on multiplex analysis by use of CL microarrays are discussed in this review.

Simplified Microarray Technique for Identifying mRNA in Rare Samples

NASA Technical Reports Server (NTRS)

Almeida, Eduardo; Kadambi, Geeta

2007-01-01

Two simplified methods of identifying messenger ribonucleic acid (mRNA), and compact, low-power apparatuses to implement the methods, are at the proof-of-concept stage of development. These methods are related to traditional methods based on hybridization of nucleic acid, but whereas the traditional methods must be practiced in laboratory settings, these methods could be practiced in field settings. Hybridization of nucleic acid is a powerful technique for detection of specific complementary nucleic acid sequences, and is increasingly being used for detection of changes in gene expression in microarrays containing thousands of gene probes. A traditional microarray study entails at least the following six steps: 1. Purification of cellular RNA, 2. Amplification of complementary deoxyribonucleic acid [cDNA] by polymerase chain reaction (PCR), 3. Labeling of cDNA with fluorophores of Cy3 (a green cyanine dye) and Cy5 (a red cyanine dye), 4. Hybridization to a microarray chip, 5. Fluorescence scanning the array(s) with dual excitation wavelengths, and 6. Analysis of the resulting images. This six-step procedure must be performed in a laboratory because it requires bulky equipment.

Characterization and simulation of cDNA microarray spots using a novel mathematical model

PubMed Central

Kim, Hye Young; Lee, Seo Eun; Kim, Min Jung; Han, Jin Il; Kim, Bo Kyung; Lee, Yong Sung; Lee, Young Seek; Kim, Jin Hyuk

2007-01-01

Background The quality of cDNA microarray data is crucial for expanding its application to other research areas, such as the study of gene regulatory networks. Despite the fact that a number of algorithms have been suggested to increase the accuracy of microarray gene expression data, it is necessary to obtain reliable microarray images by improving wet-lab experiments. As the first step of a cDNA microarray experiment, spotting cDNA probes is critical to determining the quality of spot images. Results We developed a governing equation of cDNA deposition during evaporation of a drop in the microarray spotting process. The governing equation included four parameters: the surface site density on the support, the extrapolated equilibrium constant for the binding of cDNA molecules with surface sites on glass slides, the macromolecular interaction factor, and the volume constant of a drop of cDNA solution. We simulated cDNA deposition from the single model equation by varying the value of the parameters. The morphology of the resulting cDNA deposit can be classified into three types: a doughnut shape, a peak shape, and a volcano shape. The spot morphology can be changed into a flat shape by varying the experimental conditions while considering the parameters of the governing equation of cDNA deposition. The four parameters were estimated by fitting the governing equation to the real microarray images. With the results of the simulation and the parameter estimation, the phenomenon of the formation of cDNA deposits in each type was investigated. Conclusion This study explains how various spot shapes can exist and suggests which parameters are to be adjusted for obtaining a good spot. This system is able to explore the cDNA microarray spotting process in a predictable, manageable and descriptive manner. We hope it can provide a way to predict the incidents that can occur during a real cDNA microarray experiment, and produce useful data for several research applications involving cDNA microarrays. PMID:18096047

Minimum Information Specification For In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE)

PubMed Central

Deutsch, Eric W; Ball, Catherine A; Berman, Jules J; Bova, G Steven; Brazma, Alvis; Bumgarner, Roger E; Campbell, David; Causton, Helen C; Christiansen, Jeffrey H; Daian, Fabrice; Dauga, Delphine; Davidson, Duncan R; Gimenez, Gregory; Goo, Young Ah; Grimmond, Sean; Henrich, Thorsten; Herrmann, Bernhard G; Johnson, Michael H; Korb, Martin; Mills, Jason C; Oudes, Asa J; Parkinson, Helen E; Pascal, Laura E; Pollet, Nicolas; Quackenbush, John; Ramialison, Mirana; Ringwald, Martin; Salgado, David; Sansone, Susanna-Assunta; Sherlock, Gavin; Stoeckert, Christian J; Swedlow, Jason; Taylor, Ronald C; Walashek, Laura; Warford, Anthony; Wilkinson, David G; Zhou, Yi; Zon, Leonard I; Liu, Alvin Y; True, Lawrence D

2015-01-01

One purpose of the biomedical literature is to report results in sufficient detail so that the methods of data collection and analysis can be independently replicated and verified. Here we present for consideration a minimum information specification for gene expression localization experiments, called the “Minimum Information Specification For In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE)”. It is modelled after the MIAME (Minimum Information About a Microarray Experiment) specification for microarray experiments. Data specifications like MIAME and MISFISHIE specify the information content without dictating a format for encoding that information. The MISFISHIE specification describes six types of information that should be provided for each experiment: Experimental Design, Biomaterials and Treatments, Reporters, Staining, Imaging Data, and Image Characterizations. This specification has benefited the consortium within which it was initially developed and is expected to benefit the wider research community. We welcome feedback from the scientific community to help improve our proposal. PMID:18327244

ATMAD: robust image analysis for Automatic Tissue MicroArray De-arraying.

PubMed

Nguyen, Hoai Nam; Paveau, Vincent; Cauchois, Cyril; Kervrann, Charles

2018-04-19

Over the last two decades, an innovative technology called Tissue Microarray (TMA), which combines multi-tissue and DNA microarray concepts, has been widely used in the field of histology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembled onto a single support - typically a glass slide - according to a design grid (array) layout, in order to allow multiplex analysis by treating numerous samples under identical and standardized conditions. However, during the TMA manufacturing process, the sample positions can be highly distorted from the design grid due to the imprecision when assembling tissue samples and the deformation of the embedding waxes. Consequently, these distortions may lead to severe errors of (histological) assay results when the sample identities are mismatched between the design and its manufactured output. The development of a robust method for de-arraying TMA, which localizes and matches TMA samples with their design grid, is therefore crucial to overcome the bottleneck of this prominent technology. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD) approach dedicated to images acquired with brightfield and fluorescence microscopes (or scanners). First, tissue samples are localized in the large image by applying a locally adaptive thresholding on the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametric shape model is considered for segmenting ellipse-shaped objects at each detected position. Segmented objects that do not meet the size and the roundness criteria are discarded from the list of tissue samples before being matched with the design grid. Sample matching is performed by estimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimated deformation, the true tissue samples that were preliminary rejected in the early image processing step are recognized by running a second segmentation step. We developed a novel de-arraying approach for TMA analysis. By combining wavelet-based detection, active contour segmentation, and thin-plate spline interpolation, our approach is able to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background and non-linear deformation of TMA grid. In addition, the deformation estimation produces quantitative information to asset the manufacturing quality of TMAs.

cDNA microarray analysis of esophageal cancer: discoveries and prospects.

PubMed

Shimada, Yutaka; Sato, Fumiaki; Shimizu, Kazuharu; Tsujimoto, Gozoh; Tsukada, Kazuhiro

2009-07-01

Recent progress in molecular biology has revealed many genetic and epigenetic alterations that are involved in the development and progression of esophageal cancer. Microarray analysis has also revealed several genetic networks that are involved in esophageal cancer. However, clinical application of microarray techniques and use of microarray data have not yet occurred. In this review, we focus on the recent developments and problems with microarray analysis of esophageal cancer.

Rapid Microarray Detection of DNA and Proteins in Microliter Volumes with SPR Imaging Measurements

PubMed Central

Seefeld, Ting Hu; Zhou, Wen-Juan; Corn, Robert M.

2011-01-01

A four chamber microfluidic biochip is fabricated for the rapid detection of multiple proteins and nucleic acids from microliter volume samples with the technique of surface plasmon resonance imaging (SPRI). The 18 mm × 18 mm biochip consists of four 3 μL microfluidic chambers attached to an SF10 glass substrate, each of which contains three individually addressable SPRI gold thin film microarray elements. The twelve element (4 × 3) SPRI microarray consists of gold thin film spots (1 mm2 area; 45 nm thickness) each in individually addressable 0.5 μL volume microchannels. Microarrays of single-stranded DNA and RNA (ssDNA and ssRNA respectively) are fabricated by either chemical and/or enzymatic attachment reactions in these microchannels; the SPRI microarrays are then used to detect femtomole amounts (nanomolar concentrations) of DNA and proteins (single stranded DNA binding protein and thrombin via aptamer-protein bioaffinity interactions). Microarrays of ssRNA microarray elements were also used for the ultrasensitive detection of zeptomole amounts (femtomolar concentrations) of DNA via the technique of RNase H-amplified SPRI. Enzymatic removal of ssRNA from the surface due to the hybridization adsorption of target ssDNA is detected as a reflectivity decrease in the SPR imaging measurements. The observed reflectivity loss was proportional to the log of the target ssDNA concentration with a detection limit of 10 fM or 30 zeptomoles (18,000 molecules). This enzymatic amplified ssDNA detection method is not limited by diffusion of ssDNA to the interface, and thus is extremely fast, requiring only 200 seconds in the microliter volume format. PMID:21488682

Importing MAGE-ML format microarray data into BioConductor.

PubMed

Durinck, Steffen; Allemeersch, Joke; Carey, Vincent J; Moreau, Yves; De Moor, Bart

2004-12-12

The microarray gene expression markup language (MAGE-ML) is a widely used XML (eXtensible Markup Language) standard for describing and exchanging information about microarray experiments. It can describe microarray designs, microarray experiment designs, gene expression data and data analysis results. We describe RMAGEML, a new Bioconductor package that provides a link between cDNA microarray data stored in MAGE-ML format and the Bioconductor framework for preprocessing, visualization and analysis of microarray experiments. http://www.bioconductor.org. Open Source.

Deletion of the transcriptional coactivator PGC1α in skeletal muscles is associated with reduced expression of genes related to oxidative muscle function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hatazawa, Yukino; Research Fellow of Japan Society for the Promotion of Science, Tokyo; Minami, Kimiko

The expression of the transcriptional coactivator PGC1α is increased in skeletal muscles during exercise. Previously, we showed that increased PGC1α leads to prolonged exercise performance (the duration for which running can be continued) and, at the same time, increases the expression of branched-chain amino acid (BCAA) metabolism-related enzymes and genes that are involved in supplying substrates for the TCA cycle. We recently created mice with PGC1α knockout specifically in the skeletal muscles (PGC1α KO mice), which show decreased mitochondrial content. In this study, global gene expression (microarray) analysis was performed in the skeletal muscles of PGC1α KO mice compared withmore » that of wild-type control mice. As a result, decreased expression of genes involved in the TCA cycle, oxidative phosphorylation, and BCAA metabolism were observed. Compared with previously obtained microarray data on PGC1α-overexpressing transgenic mice, each gene showed the completely opposite direction of expression change. Bioinformatic analysis of the promoter region of genes with decreased expression in PGC1α KO mice predicted the involvement of several transcription factors, including a nuclear receptor, ERR, in their regulation. As PGC1α KO microarray data in this study show opposing findings to the PGC1α transgenic data, a loss-of-function experiment, as well as a gain-of-function experiment, revealed PGC1α’s function in the oxidative energy metabolism of skeletal muscles. - Highlights: • Microarray analysis was performed in the skeletal muscle of PGC1α KO mice. • Expression of genes in the oxidative energy metabolism was decreased. • Bioinformatic analysis of promoter region of the genes predicted involvement of ERR. • PGC1α KO microarray data in this study show the mirror image of transgenic data.« less

The Longhorn Array Database (LAD): An Open-Source, MIAME compliant implementation of the Stanford Microarray Database (SMD)

PubMed Central

Killion, Patrick J; Sherlock, Gavin; Iyer, Vishwanath R

2003-01-01

Background The power of microarray analysis can be realized only if data is systematically archived and linked to biological annotations as well as analysis algorithms. Description The Longhorn Array Database (LAD) is a MIAME compliant microarray database that operates on PostgreSQL and Linux. It is a fully open source version of the Stanford Microarray Database (SMD), one of the largest microarray databases. LAD is available at Conclusions Our development of LAD provides a simple, free, open, reliable and proven solution for storage and analysis of two-color microarray data. PMID:12930545

Using phylogenetic probes for quantification of stable isotope labeling and microbial community analysis

DOEpatents

Brodie, Eoin L; DeSantis, Todd Z; Karaoz, Ulas; Andersen, Gary L

2014-12-09

Herein is described methods for a high-sensitivity means to measure the incorporation of stable isotope labeled substrates into RNA following stable isotope probing experiments (SIP). RNA is hybridized to a set of probes such as phylogenetic microarrays and isotope incorporation is quantified such as by secondary ion mass spectrometer imaging (NanoSIMS).

Cellular neural networks, the Navier-Stokes equation, and microarray image reconstruction.

PubMed

Zineddin, Bachar; Wang, Zidong; Liu, Xiaohui

2011-11-01

Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier-Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time.

Development of a Digital Microarray with Interferometric Reflectance Imaging

NASA Astrophysics Data System (ADS)

Sevenler, Derin

This dissertation describes a new type of molecular assay for nucleic acids and proteins. We call this technique a digital microarray since it is conceptually similar to conventional fluorescence microarrays, yet it performs enumerative ('digital') counting of the number captured molecules. Digital microarrays are approximately 10,000-fold more sensitive than fluorescence microarrays, yet maintain all of the strengths of the platform including low cost and high multiplexing (i.e., many different tests on the same sample simultaneously). Digital microarrays use gold nanorods to label the captured target molecules. Each gold nanorod on the array is individually detected based on its light scattering, with an interferometric microscopy technique called SP-IRIS. Our optimized high-throughput version of SP-IRIS is able to scan a typical array of 500 spots in less than 10 minutes. Digital DNA microarrays may have utility in applications where sequencing is prohibitively expensive or slow. As an example, we describe a digital microarray assay for gene expression markers of bacterial drug resistance.

Spectral Analysis of Breast Cancer on Tissue Microarrays: Seeing Beyond Morphology

DTIC Science & Technology

2005-04-01

Harvey N., Szymanski J.J., Bloch J.J., Mitchell M. investigation of image feature extraction by a genetic algorithm. Proc. SPIE 1999;3812:24-31. 11...automated feature extraction using multiple data sources. Proc. SPIE 2003;5099:190-200. 15 4 Spectral-Spatial Analysis of Urine Cytology Angeletti et al...Appendix Contents: 1. Harvey, N.R., Levenson, R.M., Rimm, D.L. (2003) Investigation of Automated Feature Extraction Techniques for Applications in

Image Processing of Porous Silicon Microarray in Refractive Index Change Detection.

PubMed

Guo, Zhiqing; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola; Li, Chuanxi

2017-06-08

A new method for extracting the dots is proposed by the reflected light image of porous silicon (PSi) microarray utilization in this paper. The method consists of three parts: pretreatment, tilt correction and spot segmentation. First, based on the characteristics of different components in HSV (Hue, Saturation, Value) space, a special pretreatment is proposed for the reflected light image to obtain the contour edges of the array cells in the image. Second, through the geometric relationship of the target object between the initial external rectangle and the minimum bounding rectangle (MBR), a new tilt correction algorithm based on the MBR is proposed to adjust the image. Third, based on the specific requirements of the reflected light image segmentation, the array cells are segmented into dots as large as possible and the distance between the dots is equal in the corrected image. Experimental results show that the pretreatment part of this method can effectively avoid the influence of complex background and complete the binarization processing of the image. The tilt correction algorithm has a shorter computation time, which makes it highly suitable for tilt correction of reflected light images. The segmentation algorithm makes the dots in a regular arrangement, excludes the edges and the bright spots. This method could be utilized in the fast, accurate and automatic dots extraction of the PSi microarray reflected light image.

Image Processing of Porous Silicon Microarray in Refractive Index Change Detection

PubMed Central

Guo, Zhiqing; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola; Li, Chuanxi

2017-01-01

A new method for extracting the dots is proposed by the reflected light image of porous silicon (PSi) microarray utilization in this paper. The method consists of three parts: pretreatment, tilt correction and spot segmentation. First, based on the characteristics of different components in HSV (Hue, Saturation, Value) space, a special pretreatment is proposed for the reflected light image to obtain the contour edges of the array cells in the image. Second, through the geometric relationship of the target object between the initial external rectangle and the minimum bounding rectangle (MBR), a new tilt correction algorithm based on the MBR is proposed to adjust the image. Third, based on the specific requirements of the reflected light image segmentation, the array cells are segmented into dots as large as possible and the distance between the dots is equal in the corrected image. Experimental results show that the pretreatment part of this method can effectively avoid the influence of complex background and complete the binarization processing of the image. The tilt correction algorithm has a shorter computation time, which makes it highly suitable for tilt correction of reflected light images. The segmentation algorithm makes the dots in a regular arrangement, excludes the edges and the bright spots. This method could be utilized in the fast, accurate and automatic dots extraction of the PSi microarray reflected light image. PMID:28594383

Gene expression profile of blood cells for the prediction of delayed cerebral ischemia after intracranial aneurysm rupture: a pilot study in humans.

PubMed

Baumann, Antoine; Devaux, Yvan; Audibert, Gérard; Zhang, Lu; Bracard, Serge; Colnat-Coulbois, Sophie; Klein, Olivier; Zannad, Faiez; Charpentier, Claire; Longrois, Dan; Mertes, Paul-Michel

2013-01-01

Delayed cerebral ischemia (DCI) is a potentially devastating complication after intracranial aneurysm rupture and its mechanisms remain poorly elucidated. Early identification of the patients prone to developing DCI after rupture may represent a major breakthrough in its prevention and treatment. The single gene approach of DCI has demonstrated interest in humans. We hypothesized that whole genome expression profile of blood cells may be useful for better comprehension and prediction of aneurysmal DCI. Over a 35-month period, 218 patients with aneurysm rupture were included in this study. DCI was defined as the occurrence of a new delayed neurological deficit occurring within 2 weeks after aneurysm rupture with evidence of ischemia either on perfusion-diffusion MRI, CT angiography or CT perfusion imaging, or with cerebral angiography. DCI patients were matched against controls based on 4 out of 5 criteria (age, sex, Fisher grade, aneurysm location and smoking status). Genome-wide expression analysis of blood cells obtained at admission was performed by microarrays. Transcriptomic analysis was performed using long oligonucleotide microarrays representing 25,000 genes. Quantitative PCR: 1 µg of total RNA extracted was reverse-transcribed, and the resulting cDNA was diluted 10-fold before performing quantitative PCR. Microarray data were first analyzed by 'Significance Analysis of Microarrays' software which includes the Benjamini correction for multiple testing. In a second step, microarray data fold change was compared using a two-tailed, paired t test. Analysis of receiver-operating characteristic (ROC) curves and the area under the ROC curves were used for prediction analysis. Logistic regression models were used to investigate the additive value of multiple biomarkers. A total of 16 patients demonstrated DCI. Significance Analysis of Microarrays software failed to retrieve significant genes, most probably because of the heterogeneity of the patients included in the microarray experiments and the small size of the DCI population sample. Standard two-tailed paired t test and C-statistic revealed significant associations between gene expression and the occurrence of DCI: in particular, the expression of neuroregulin 1 was 1.6-fold upregulated in patients with DCI (p = 0.01) and predicted DCI with an area under the ROC curve of 0.96. Logistic regression analyses revealed a significant association between neuroregulin 1 and DCI (odds ratio 1.46, 95% confidence interval 1.02-2.09, p = 0.02). This pilot study suggests that blood cells may be a reservoir of prognostic biomarkers of DCI in patients with intracranial aneurysm rupture. Despite an evident lack of power, this study elicited neuroregulin 1, a vasoreactivity-, inflammation- and angiogenesis-related gene, as a possible candidate predictor of DCI. Larger cohort studies are needed but genome-wide microarray-based studies are promising research tools for the understanding of DCI after intracranial aneurysm rupture. © 2013 S. Karger AG, Basel.

The Microarray Revolution: Perspectives from Educators

ERIC Educational Resources Information Center

Brewster, Jay L.; Beason, K. Beth; Eckdahl, Todd T.; Evans, Irene M.

2004-01-01

In recent years, microarray analysis has become a key experimental tool, enabling the analysis of genome-wide patterns of gene expression. This review approaches the microarray revolution with a focus upon four topics: 1) the early development of this technology and its application to cancer diagnostics; 2) a primer of microarray research,…

Quantitative multiplex quantum dot in-situ hybridisation based gene expression profiling in tissue microarrays identifies prognostic genes in acute myeloid leukaemia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tholouli, Eleni; MacDermott, Sarah; Hoyland, Judith

2012-08-24

Highlights: Black-Right-Pointing-Pointer Development of a quantitative high throughput in situ expression profiling method. Black-Right-Pointing-Pointer Application to a tissue microarray of 242 AML bone marrow samples. Black-Right-Pointing-Pointer Identification of HOXA4, HOXA9, Meis1 and DNMT3A as prognostic markers in AML. -- Abstract: Measurement and validation of microarray gene signatures in routine clinical samples is problematic and a rate limiting step in translational research. In order to facilitate measurement of microarray identified gene signatures in routine clinical tissue a novel method combining quantum dot based oligonucleotide in situ hybridisation (QD-ISH) and post-hybridisation spectral image analysis was used for multiplex in-situ transcript detection inmore » archival bone marrow trephine samples from patients with acute myeloid leukaemia (AML). Tissue-microarrays were prepared into which white cell pellets were spiked as a standard. Tissue microarrays were made using routinely processed bone marrow trephines from 242 patients with AML. QD-ISH was performed for six candidate prognostic genes using triplex QD-ISH for DNMT1, DNMT3A, DNMT3B, and for HOXA4, HOXA9, Meis1. Scrambled oligonucleotides were used to correct for background staining followed by normalisation of expression against the expression values for the white cell pellet standard. Survival analysis demonstrated that low expression of HOXA4 was associated with poorer overall survival (p = 0.009), whilst high expression of HOXA9 (p < 0.0001), Meis1 (p = 0.005) and DNMT3A (p = 0.04) were associated with early treatment failure. These results demonstrate application of a standardised, quantitative multiplex QD-ISH method for identification of prognostic markers in formalin-fixed paraffin-embedded clinical samples, facilitating measurement of gene expression signatures in routine clinical samples.« less

Robust Segmentation of Overlapping Cells in Histopathology Specimens Using Parallel Seed Detection and Repulsive Level Set

PubMed Central

Qi, Xin; Xing, Fuyong; Foran, David J.; Yang, Lin

2013-01-01

Automated image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of breast cancer. Automated segmentation of the cells comprising imaged tissue microarrays (TMA) is a prerequisite for any subsequent quantitative analysis. Unfortunately, crowding and overlapping of cells present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel algorithm which can reliably separate touching cells in hematoxylin stained breast TMA specimens which have been acquired using a standard RGB camera. The algorithm is composed of two steps. It begins with a fast, reliable object center localization approach which utilizes single-path voting followed by mean-shift clustering. Next, the contour of each cell is obtained using a level set algorithm based on an interactive model. We compared the experimental results with those reported in the most current literature. Finally, performance was evaluated by comparing the pixel-wise accuracy provided by human experts with that produced by the new automated segmentation algorithm. The method was systematically tested on 234 image patches exhibiting dense overlap and containing more than 2200 cells. It was also tested on whole slide images including blood smears and tissue microarrays containing thousands of cells. Since the voting step of the seed detection algorithm is well suited for parallelization, a parallel version of the algorithm was implemented using graphic processing units (GPU) which resulted in significant speed-up over the C/C++ implementation. PMID:22167559

Oligonucleotide microarray chip for the quantification of MS2, ΦX174, and adenoviruses on the multiplex analysis platform MCR 3.

PubMed

Lengger, Sandra; Otto, Johannes; Elsässer, Dennis; Schneider, Oliver; Tiehm, Andreas; Fleischer, Jens; Niessner, Reinhard; Seidel, Michael

2014-05-01

Pathogenic viruses are emerging contaminants in water which should be analyzed for water safety to preserve public health. A strategy was developed to quantify RNA and DNA viruses in parallel on chemiluminescence flow-through oligonucleotide microarrays. In order to show the proof of principle, bacteriophage MS2, ΦX174, and the human pathogenic adenovirus type 2 (hAdV2) were analyzed in spiked tap water samples on the analysis platform MCR 3. The chemiluminescence microarray imaging unit was equipped with a Peltier heater for a controlled heating of the flow cell. The efficiency and selectivity of DNA hybridization could be increased resulting in higher signal intensities and lower cross-reactivities of polymerase chain reaction (PCR) products from other viruses. The total analysis time for DNA/RNA extraction, cDNA synthesis for RNA viruses, polymerase chain reaction, single-strand separation, and oligonucleotide microarray analysis was performed in 4-4.5 h. The parallel quantification was possible in a concentration range of 9.6 × 10(5)-1.4 × 10(10) genomic units (GU)/mL for bacteriophage MS2, 1.4 × 10(5)-3.7 × 10(8) GU/mL for bacteriophage ΦX174, and 6.5 × 10(3)-1.2 × 10(5) for hAdV2, respectively, by using a measuring temperature of 40 °C. Detection limits could be calculated to 6.6 × 10(5) GU/mL for MS2, 5.3 × 10(3) GU/mL for ΦX174, and 1.5 × 10(2) GU/mL for hAdV2, respectively. Real samples of surface water and treated wastewater were tested. Generally, found concentrations of hAdV2, bacteriophage MS2, and ΦX174 were at the detection limit. Nevertheless, bacteriophages could be identified with similar results by means of quantitative PCR and oligonucleotide microarray analysis on the MCR 3.

EMAAS: An extensible grid-based Rich Internet Application for microarray data analysis and management

PubMed Central

Barton, G; Abbott, J; Chiba, N; Huang, DW; Huang, Y; Krznaric, M; Mack-Smith, J; Saleem, A; Sherman, BT; Tiwari, B; Tomlinson, C; Aitman, T; Darlington, J; Game, L; Sternberg, MJE; Butcher, SA

2008-01-01

Background Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large data sets. This, together with the proliferation of tools and techniques for microarray data analysis, makes it very challenging for a laboratory scientist to keep up-to-date with the latest developments in this field. Our aim was to develop a distributed e-support system for microarray data analysis and management. Results EMAAS (Extensible MicroArray Analysis System) is a multi-user rich internet application (RIA) providing simple, robust access to up-to-date resources for microarray data storage and analysis, combined with integrated tools to optimise real time user support and training. The system leverages the power of distributed computing to perform microarray analyses, and provides seamless access to resources located at various remote facilities. The EMAAS framework allows users to import microarray data from several sources to an underlying database, to pre-process, quality assess and analyse the data, to perform functional analyses, and to track data analysis steps, all through a single easy to use web portal. This interface offers distance support to users both in the form of video tutorials and via live screen feeds using the web conferencing tool EVO. A number of analysis packages, including R-Bioconductor and Affymetrix Power Tools have been integrated on the server side and are available programmatically through the Postgres-PLR library or on grid compute clusters. Integrated distributed resources include the functional annotation tool DAVID, GeneCards and the microarray data repositories GEO, CELSIUS and MiMiR. EMAAS currently supports analysis of Affymetrix 3' and Exon expression arrays, and the system is extensible to cater for other microarray and transcriptomic platforms. Conclusion EMAAS enables users to track and perform microarray data management and analysis tasks through a single easy-to-use web application. The system architecture is flexible and scalable to allow new array types, analysis algorithms and tools to be added with relative ease and to cope with large increases in data volume. PMID:19032776

An Introduction to MAMA (Meta-Analysis of MicroArray data) System.

PubMed

Zhang, Zhe; Fenstermacher, David

2005-01-01

Analyzing microarray data across multiple experiments has been proven advantageous. To support this kind of analysis, we are developing a software system called MAMA (Meta-Analysis of MicroArray data). MAMA utilizes a client-server architecture with a relational database on the server-side for the storage of microarray datasets collected from various resources. The client-side is an application running on the end user's computer that allows the user to manipulate microarray data and analytical results locally. MAMA implementation will integrate several analytical methods, including meta-analysis within an open-source framework offering other developers the flexibility to plug in additional statistical algorithms.

A Microwell-Printing Fabrication Strategy for the On-Chip Templated Biosynthesis of Protein Microarrays for Surface Plasmon Resonance Imaging

PubMed Central

Manuel, Gerald; Lupták, Andrej; Corn, Robert M.

2017-01-01

A two-step templated, ribosomal biosynthesis/printing method for the fabrication of protein microarrays for surface plasmon resonance imaging (SPRI) measurements is demonstrated. In the first step, a sixteen component microarray of proteins is created in microwells by cell free on chip protein synthesis; each microwell contains both an in vitro transcription and translation (IVTT) solution and 350 femtomoles of a specific DNA template sequence that together are used to create approximately 40 picomoles of a specific hexahistidine-tagged protein. In the second step, the protein microwell array is used to contact print one or more protein microarrays onto nitrilotriacetic acid (NTA)-functionalized gold thin film SPRI chips for real-time SPRI surface bioaffinity adsorption measurements. Even though each microwell array element only contains approximately 40 picomoles of protein, the concentration is sufficiently high for the efficient bioaffinity adsorption and capture of the approximately 100 femtomoles of hexahistidine-tagged protein required to create each SPRI microarray element. As a first example, the protein biosynthesis process is verified with fluorescence imaging measurements of a microwell array containing His-tagged green fluorescent protein (GFP), yellow fluorescent protein (YFP) and mCherry (RFP), and then the fidelity of SPRI chips printed from this protein microwell array is ascertained by measuring the real-time adsorption of various antibodies specific to these three structurally related proteins. This greatly simplified two-step synthesis/printing fabrication methodology eliminates most of the handling, purification and processing steps normally required in the synthesis of multiple protein probes, and enables the rapid fabrication of SPRI protein microarrays from DNA templates for the study of protein-protein bioaffinity interactions. PMID:28706572

An improved K-means clustering method for cDNA microarray image segmentation.

PubMed

Wang, T N; Li, T J; Shao, G F; Wu, S X

2015-07-14

Microarray technology is a powerful tool for human genetic research and other biomedical applications. Numerous improvements to the standard K-means algorithm have been carried out to complete the image segmentation step. However, most of the previous studies classify the image into two clusters. In this paper, we propose a novel K-means algorithm, which first classifies the image into three clusters, and then one of the three clusters is divided as the background region and the other two clusters, as the foreground region. The proposed method was evaluated on six different data sets. The analyses of accuracy, efficiency, expression values, special gene spots, and noise images demonstrate the effectiveness of our method in improving the segmentation quality.

TAMEE: data management and analysis for tissue microarrays.

PubMed

Thallinger, Gerhard G; Baumgartner, Kerstin; Pirklbauer, Martin; Uray, Martina; Pauritsch, Elke; Mehes, Gabor; Buck, Charles R; Zatloukal, Kurt; Trajanoski, Zlatko

2007-03-07

With the introduction of tissue microarrays (TMAs) researchers can investigate gene and protein expression in tissues on a high-throughput scale. TMAs generate a wealth of data calling for extended, high level data management. Enhanced data analysis and systematic data management are required for traceability and reproducibility of experiments and provision of results in a timely and reliable fashion. Robust and scalable applications have to be utilized, which allow secure data access, manipulation and evaluation for researchers from different laboratories. TAMEE (Tissue Array Management and Evaluation Environment) is a web-based database application for the management and analysis of data resulting from the production and application of TMAs. It facilitates storage of production and experimental parameters, of images generated throughout the TMA workflow, and of results from core evaluation. Database content consistency is achieved using structured classifications of parameters. This allows the extraction of high quality results for subsequent biologically-relevant data analyses. Tissue cores in the images of stained tissue sections are automatically located and extracted and can be evaluated using a set of predefined analysis algorithms. Additional evaluation algorithms can be easily integrated into the application via a plug-in interface. Downstream analysis of results is facilitated via a flexible query generator. We have developed an integrated system tailored to the specific needs of research projects using high density TMAs. It covers the complete workflow of TMA production, experimental use and subsequent analysis. The system is freely available for academic and non-profit institutions from http://genome.tugraz.at/Software/TAMEE.

Assessing differential expression in two-color microarrays: a resampling-based empirical Bayes approach.

PubMed

Li, Dongmei; Le Pape, Marc A; Parikh, Nisha I; Chen, Will X; Dye, Timothy D

2013-01-01

Microarrays are widely used for examining differential gene expression, identifying single nucleotide polymorphisms, and detecting methylation loci. Multiple testing methods in microarray data analysis aim at controlling both Type I and Type II error rates; however, real microarray data do not always fit their distribution assumptions. Smyth's ubiquitous parametric method, for example, inadequately accommodates violations of normality assumptions, resulting in inflated Type I error rates. The Significance Analysis of Microarrays, another widely used microarray data analysis method, is based on a permutation test and is robust to non-normally distributed data; however, the Significance Analysis of Microarrays method fold change criteria are problematic, and can critically alter the conclusion of a study, as a result of compositional changes of the control data set in the analysis. We propose a novel approach, combining resampling with empirical Bayes methods: the Resampling-based empirical Bayes Methods. This approach not only reduces false discovery rates for non-normally distributed microarray data, but it is also impervious to fold change threshold since no control data set selection is needed. Through simulation studies, sensitivities, specificities, total rejections, and false discovery rates are compared across the Smyth's parametric method, the Significance Analysis of Microarrays, and the Resampling-based empirical Bayes Methods. Differences in false discovery rates controls between each approach are illustrated through a preterm delivery methylation study. The results show that the Resampling-based empirical Bayes Methods offer significantly higher specificity and lower false discovery rates compared to Smyth's parametric method when data are not normally distributed. The Resampling-based empirical Bayes Methods also offers higher statistical power than the Significance Analysis of Microarrays method when the proportion of significantly differentially expressed genes is large for both normally and non-normally distributed data. Finally, the Resampling-based empirical Bayes Methods are generalizable to next generation sequencing RNA-seq data analysis.

Transfection microarray and the applications.

PubMed

Miyake, Masato; Yoshikawa, Tomohiro; Fujita, Satoshi; Miyake, Jun

2009-05-01

Microarray transfection has been extensively studied for high-throughput functional analysis of mammalian cells. However, control of efficiency and reproducibility are the critical issues for practical use. By using solid-phase transfection accelerators and nano-scaffold, we provide a highly efficient and reproducible microarray-transfection device, "transfection microarray". The device would be applied to the limited number of available primary cells and stem cells not only for large-scale functional analysis but also reporter-based time-lapse cellular event analysis.

Microarray technology for major chemical contaminants analysis in food: current status and prospects.

PubMed

Zhang, Zhaowei; Li, Peiwu; Hu, Xiaofeng; Zhang, Qi; Ding, Xiaoxia; Zhang, Wen

2012-01-01

Chemical contaminants in food have caused serious health issues in both humans and animals. Microarray technology is an advanced technique suitable for the analysis of chemical contaminates. In particular, immuno-microarray approach is one of the most promising methods for chemical contaminants analysis. The use of microarrays for the analysis of chemical contaminants is the subject of this review. Fabrication strategies and detection methods for chemical contaminants are discussed in detail. Application to the analysis of mycotoxins, biotoxins, pesticide residues, and pharmaceutical residues is also described. Finally, future challenges and opportunities are discussed.

Contributions to Statistical Problems Related to Microarray Data

ERIC Educational Resources Information Center

Hong, Feng

2009-01-01

Microarray is a high throughput technology to measure the gene expression. Analysis of microarray data brings many interesting and challenging problems. This thesis consists three studies related to microarray data. First, we propose a Bayesian model for microarray data and use Bayes Factors to identify differentially expressed genes. Second, we…

Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data

NASA Astrophysics Data System (ADS)

Palumbo, Francesco; D'Enza, Alfonso Iodice

The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence.

Microarray platform for omics analysis

NASA Astrophysics Data System (ADS)

Mecklenburg, Michael; Xie, Bin

2001-09-01

Microarray technology has revolutionized genetic analysis. However, limitations in genome analysis has lead to renewed interest in establishing 'omic' strategies. As we enter the post-genomic era, new microarray technologies are needed to address these new classes of 'omic' targets, such as proteins, as well as lipids and carbohydrates. We have developed a microarray platform that combines self- assembling monolayers with the biotin-streptavidin system to provide a robust, versatile immobilization scheme. A hydrophobic film is patterned on the surface creating an array of tension wells that eliminates evaporation effects thereby reducing the shear stress to which biomolecules are exposed to during immobilization. The streptavidin linker layer makes it possible to adapt and/or develop microarray based assays using virtually any class of biomolecules including: carbohydrates, peptides, antibodies, receptors, as well as them ore traditional DNA based arrays. Our microarray technology is designed to furnish seamless compatibility across the various 'omic' platforms by providing a common blueprint for fabricating and analyzing arrays. The prototype microarray uses a microscope slide footprint patterned with 2 by 96 flat wells. Data on the microarray platform will be presented.

Microarrays in brain research: the good, the bad and the ugly.

PubMed

Mirnics, K

2001-06-01

Making sense of microarray data is a complex process, in which the interpretation of findings will depend on the overall experimental design and judgement of the investigator performing the analysis. As a result, differences in tissue harvesting, microarray types, sample labelling and data analysis procedures make post hoc sharing of microarray data a great challenge. To ensure rapid and meaningful data exchange, we need to create some order out of the existing chaos. In these ground-breaking microarray standardization and data sharing efforts, NIH agencies should take a leading role

Micro-Analyzer: automatic preprocessing of Affymetrix microarray data.

PubMed

Guzzi, Pietro Hiram; Cannataro, Mario

2013-08-01

A current trend in genomics is the investigation of the cell mechanism using different technologies, in order to explain the relationship among genes, molecular processes and diseases. For instance, the combined use of gene-expression arrays and genomic arrays has been demonstrated as an effective instrument in clinical practice. Consequently, in a single experiment different kind of microarrays may be used, resulting in the production of different types of binary data (images and textual raw data). The analysis of microarray data requires an initial preprocessing phase, that makes raw data suitable for use on existing analysis platforms, such as the TIGR M4 (TM4) Suite. An additional challenge to be faced by emerging data analysis platforms is the ability to treat in a combined way those different microarray formats coupled with clinical data. In fact, resulting integrated data may include both numerical and symbolic data (e.g. gene expression and SNPs regarding molecular data), as well as temporal data (e.g. the response to a drug, time to progression and survival rate), regarding clinical data. Raw data preprocessing is a crucial step in analysis but is often performed in a manual and error prone way using different software tools. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of different microarray data are needed. The paper presents Micro-Analyzer (Microarray Analyzer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix gene expression and SNP binary data. It represents the evolution of the μ-CS tool, extending the preprocessing to SNP arrays that were not allowed in μ-CS. The Micro-Analyzer is provided as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data (gene expression and SNPs) by invoking TM4 platform. It avoids: (i) the manual invocation of external tools (e.g. the Affymetrix Power Tools), (ii) the manual loading of preprocessing libraries, and (iii) the management of intermediate files, such as results and metadata. Micro-Analyzer users can directly manage Affymetrix binary data without worrying about locating and invoking the proper preprocessing tools and chip-specific libraries. Moreover, users of the Micro-Analyzer tool can load the preprocessed data directly into the well-known TM4 platform, extending in such a way also the TM4 capabilities. Consequently, Micro Analyzer offers the following advantages: (i) it reduces possible errors in the preprocessing and further analysis phases, e.g. due to the incorrect choice of parameters or due to the use of old libraries, (ii) it enables the combined and centralized pre-processing of different arrays, (iii) it may enhance the quality of further analysis by storing the workflow, i.e. information about the preprocessing steps, and (iv) finally Micro-Analzyer is freely available as a standalone application at the project web site http://sourceforge.net/projects/microanalyzer/. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

HDBStat!: a platform-independent software suite for statistical analysis of high dimensional biology data.

PubMed

Trivedi, Prinal; Edwards, Jode W; Wang, Jelai; Gadbury, Gary L; Srinivasasainagendra, Vinodh; Zakharkin, Stanislav O; Kim, Kyoungmi; Mehta, Tapan; Brand, Jacob P L; Patki, Amit; Page, Grier P; Allison, David B

2005-04-06

Many efforts in microarray data analysis are focused on providing tools and methods for the qualitative analysis of microarray data. HDBStat! (High-Dimensional Biology-Statistics) is a software package designed for analysis of high dimensional biology data such as microarray data. It was initially developed for the analysis of microarray gene expression data, but it can also be used for some applications in proteomics and other aspects of genomics. HDBStat! provides statisticians and biologists a flexible and easy-to-use interface to analyze complex microarray data using a variety of methods for data preprocessing, quality control analysis and hypothesis testing. Results generated from data preprocessing methods, quality control analysis and hypothesis testing methods are output in the form of Excel CSV tables, graphs and an Html report summarizing data analysis. HDBStat! is a platform-independent software that is freely available to academic institutions and non-profit organizations. It can be downloaded from our website http://www.soph.uab.edu/ssg_content.asp?id=1164.

Quality control of inkjet technology for DNA microarray fabrication.

PubMed

Pierik, Anke; Dijksman, Frits; Raaijmakers, Adrie; Wismans, Ton; Stapert, Henk

2008-12-01

A robust manufacturing process is essential to make high-quality DNA microarrays, especially for use in diagnostic tests. We investigated different failure modes of the inkjet printing process used to manufacture low-density microarrays. A single nozzle inkjet spotter was provided with two optical imaging systems, monitoring in real time the flight path of every droplet. If a droplet emission failure is detected, the printing process is automatically stopped. We analyzed over 1.3 million droplets. This information was used to investigate the performance of the inkjet system and to obtain detailed insight into the frequency and causes of jetting failures. Of all the substrates investigated, 96.2% were produced without any system or jetting failures. In 1.6% of the substrates, droplet emission failed and was correctly identified. Appropriate measures could then be taken to get the process back on track. In 2.2%, the imaging systems failed while droplet emission occurred correctly. In 0.1% of the substrates, droplet emission failure that was not timely detected occurred. Thus, the overall yield of the microarray manufacturing process was 99.9%, which is highly acceptable for prototyping.

WebArray: an online platform for microarray data analysis

PubMed Central

Xia, Xiaoqin; McClelland, Michael; Wang, Yipeng

2005-01-01

Background Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematics, statistics and computer skills for implementation. Commercially available software can provide a user-friendly interface at considerable cost. To facilitate the use of these tools for microarray data analysis on an open platform we developed an online microarray data analysis platform, WebArray, for bench biologists to utilize these tools to explore data from single/dual color microarray experiments. Results The currently implemented functions were based on limma and affy package from Bioconductor, the spacings LOESS histogram (SPLOSH) method, PCA-assisted normalization method and genome mapping method. WebArray incorporates these packages and provides a user-friendly interface for accessing a wide range of key functions of limma and others, such as spot quality weight, background correction, graphical plotting, normalization, linear modeling, empirical bayes statistical analysis, false discovery rate (FDR) estimation, chromosomal mapping for genome comparison. Conclusion WebArray offers a convenient platform for bench biologists to access several cutting-edge microarray data analysis tools. The website is freely available at . It runs on a Linux server with Apache and MySQL. PMID:16371165

Design and evaluation of Actichip, a thematic microarray for the study of the actin cytoskeleton

PubMed Central

Muller, Jean; Mehlen, André; Vetter, Guillaume; Yatskou, Mikalai; Muller, Arnaud; Chalmel, Frédéric; Poch, Olivier; Friederich, Evelyne; Vallar, Laurent

2007-01-01

Background The actin cytoskeleton plays a crucial role in supporting and regulating numerous cellular processes. Mutations or alterations in the expression levels affecting the actin cytoskeleton system or related regulatory mechanisms are often associated with complex diseases such as cancer. Understanding how qualitative or quantitative changes in expression of the set of actin cytoskeleton genes are integrated to control actin dynamics and organisation is currently a challenge and should provide insights in identifying potential targets for drug discovery. Here we report the development of a dedicated microarray, the Actichip, containing 60-mer oligonucleotide probes for 327 genes selected for transcriptome analysis of the human actin cytoskeleton. Results Genomic data and sequence analysis features were retrieved from GenBank and stored in an integrative database called Actinome. From these data, probes were designed using a home-made program (CADO4MI) allowing sequence refinement and improved probe specificity by combining the complementary information recovered from the UniGene and RefSeq databases. Actichip performance was analysed by hybridisation with RNAs extracted from epithelial MCF-7 cells and human skeletal muscle. Using thoroughly standardised procedures, we obtained microarray images with excellent quality resulting in high data reproducibility. Actichip displayed a large dynamic range extending over three logs with a limit of sensitivity between one and ten copies of transcript per cell. The array allowed accurate detection of small changes in gene expression and reliable classification of samples based on the expression profiles of tissue-specific genes. When compared to two other oligonucleotide microarray platforms, Actichip showed similar sensitivity and concordant expression ratios. Moreover, Actichip was able to discriminate the highly similar actin isoforms whereas the two other platforms did not. Conclusion Our data demonstrate that Actichip is a powerful alternative to commercial high density microarrays for cytoskeleton gene profiling in normal or pathological samples. Actichip is available upon request. PMID:17727702

Issues in the analysis of oligonucleotide tiling microarrays for transcript mapping

NASA Technical Reports Server (NTRS)

Royce, Thomas E.; Rozowsky, Joel S.; Bertone, Paul; Samanta, Manoj; Stolc, Viktor; Weissman, Sherman; Snyder, Michael; Gerstein, Mark

2005-01-01

Traditional microarrays use probes complementary to known genes to quantitate the differential gene expression between two or more conditions. Genomic tiling microarray experiments differ in that probes that span a genomic region at regular intervals are used to detect the presence or absence of transcription. This difference means the same sets of biases and the methods for addressing them are unlikely to be relevant to both types of experiment. We introduce the informatics challenges arising in the analysis of tiling microarray experiments as open problems to the scientific community and present initial approaches for the analysis of this nascent technology.

The Development of Protein Microarrays and Their Applications in DNA-Protein and Protein-Protein Interaction Analyses of Arabidopsis Transcription Factors

PubMed Central

Gong, Wei; He, Kun; Covington, Mike; Dinesh-Kumar, S. P.; Snyder, Michael; Harmer, Stacey L.; Zhu, Yu-Xian; Deng, Xing Wang

2009-01-01

We used our collection of Arabidopsis transcription factor (TF) ORFeome clones to construct protein microarrays containing as many as 802 TF proteins. These protein microarrays were used for both protein-DNA and protein-protein interaction analyses. For protein-DNA interaction studies, we examined AP2/ERF family TFs and their cognate cis-elements. By careful comparison of the DNA-binding specificity of 13 TFs on the protein microarray with previous non-microarray data, we showed that protein microarrays provide an efficient and high throughput tool for genome-wide analysis of TF-DNA interactions. This microarray protein-DNA interaction analysis allowed us to derive a comprehensive view of DNA-binding profiles of AP2/ERF family proteins in Arabidopsis. It also revealed four TFs that bound the EE (evening element) and had the expected phased gene expression under clock-regulation, thus providing a basis for further functional analysis of their roles in clock regulation of gene expression. We also developed procedures for detecting protein interactions using this TF protein microarray and discovered four novel partners that interact with HY5, which can be validated by yeast two-hybrid assays. Thus, plant TF protein microarrays offer an attractive high-throughput alternative to traditional techniques for TF functional characterization on a global scale. PMID:19802365

Karyotype versus microarray testing for genetic abnormalities after stillbirth.

PubMed

Reddy, Uma M; Page, Grier P; Saade, George R; Silver, Robert M; Thorsten, Vanessa R; Parker, Corette B; Pinar, Halit; Willinger, Marian; Stoll, Barbara J; Heim-Hall, Josefine; Varner, Michael W; Goldenberg, Robert L; Bukowski, Radek; Wapner, Ronald J; Drews-Botsch, Carolyn D; O'Brien, Barbara M; Dudley, Donald J; Levy, Brynn

2012-12-06

Genetic abnormalities have been associated with 6 to 13% of stillbirths, but the true prevalence may be higher. Unlike karyotype analysis, microarray analysis does not require live cells, and it detects small deletions and duplications called copy-number variants. The Stillbirth Collaborative Research Network conducted a population-based study of stillbirth in five geographic catchment areas. Standardized postmortem examinations and karyotype analyses were performed. A single-nucleotide polymorphism array was used to detect copy-number variants of at least 500 kb in placental or fetal tissue. Variants that were not identified in any of three databases of apparently unaffected persons were then classified into three groups: probably benign, clinical significance unknown, or pathogenic. We compared the results of karyotype and microarray analyses of samples obtained after delivery. In our analysis of samples from 532 stillbirths, microarray analysis yielded results more often than did karyotype analysis (87.4% vs. 70.5%, P<0.001) and provided better detection of genetic abnormalities (aneuploidy or pathogenic copy-number variants, 8.3% vs. 5.8%; P=0.007). Microarray analysis also identified more genetic abnormalities among 443 antepartum stillbirths (8.8% vs. 6.5%, P=0.02) and 67 stillbirths with congenital anomalies (29.9% vs. 19.4%, P=0.008). As compared with karyotype analysis, microarray analysis provided a relative increase in the diagnosis of genetic abnormalities of 41.9% in all stillbirths, 34.5% in antepartum stillbirths, and 53.8% in stillbirths with anomalies. Microarray analysis is more likely than karyotype analysis to provide a genetic diagnosis, primarily because of its success with nonviable tissue, and is especially valuable in analyses of stillbirths with congenital anomalies or in cases in which karyotype results cannot be obtained. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development.).

Emerging Use of Gene Expression Microarrays in Plant Physiology

DOE PAGES

Wullschleger, Stan D.; Difazio, Stephen P.

2003-01-01

Microarrays have become an important technology for the global analysis of gene expression in humans, animals, plants, and microbes. Implemented in the context of a well-designed experiment, cDNA and oligonucleotide arrays can provide highthroughput, simultaneous analysis of transcript abundance for hundreds, if not thousands, of genes. However, despite widespread acceptance, the use of microarrays as a tool to better understand processes of interest to the plant physiologist is still being explored. To help illustrate current uses of microarrays in the plant sciences, several case studies that we believe demonstrate the emerging application of gene expression arrays in plant physiology weremore » selected from among the many posters and presentations at the 2003 Plant and Animal Genome XI Conference. Based on this survey, microarrays are being used to assess gene expression in plants exposed to the experimental manipulation of air temperature, soil water content and aluminium concentration in the root zone. Analysis often includes characterizing transcript profiles for multiple post-treatment sampling periods and categorizing genes with common patterns of response using hierarchical clustering techniques. In addition, microarrays are also providing insights into developmental changes in gene expression associated with fibre and root elongation in cotton and maize, respectively. Technical and analytical limitations of microarrays are discussed and projects attempting to advance areas of microarray design and data analysis are highlighted. Finally, although much work remains, we conclude that microarrays are a valuable tool for the plant physiologist interested in the characterization and identification of individual genes and gene families with potential application in the fields of agriculture, horticulture and forestry.« less

A genome-wide 20 K citrus microarray for gene expression analysis

PubMed Central

Martinez-Godoy, M Angeles; Mauri, Nuria; Juarez, Jose; Marques, M Carmen; Santiago, Julia; Forment, Javier; Gadea, Jose

2008-01-01

Background Understanding of genetic elements that contribute to key aspects of citrus biology will impact future improvements in this economically important crop. Global gene expression analysis demands microarray platforms with a high genome coverage. In the last years, genome-wide EST collections have been generated in citrus, opening the possibility to create new tools for functional genomics in this crop plant. Results We have designed and constructed a publicly available genome-wide cDNA microarray that include 21,081 putative unigenes of citrus. As a functional companion to the microarray, a web-browsable database [1] was created and populated with information about the unigenes represented in the microarray, including cDNA libraries, isolated clones, raw and processed nucleotide and protein sequences, and results of all the structural and functional annotation of the unigenes, like general description, BLAST hits, putative Arabidopsis orthologs, microsatellites, putative SNPs, GO classification and PFAM domains. We have performed a Gene Ontology comparison with the full set of Arabidopsis proteins to estimate the genome coverage of the microarray. We have also performed microarray hybridizations to check its usability. Conclusion This new cDNA microarray replaces the first 7K microarray generated two years ago and allows gene expression analysis at a more global scale. We have followed a rational design to minimize cross-hybridization while maintaining its utility for different citrus species. Furthermore, we also provide access to a website with full structural and functional annotation of the unigenes represented in the microarray, along with the ability to use this site to directly perform gene expression analysis using standard tools at different publicly available servers. Furthermore, we show how this microarray offers a good representation of the citrus genome and present the usefulness of this genomic tool for global studies in citrus by using it to catalogue genes expressed in citrus globular embryos. PMID:18598343

Enhancing interdisciplinary mathematics and biology education: a microarray data analysis course bridging these disciplines.

PubMed

Tra, Yolande V; Evans, Irene M

2010-01-01

BIO2010 put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and Microarray Genome and Clustering Tool software and added R statistical software along with Bioconductor packages. In response to student feedback, one microarray data set was fully analyzed in class, starting from preprocessing to gene discovery to pathway analysis using the latter software. A class project was to conduct a similar analysis where students analyzed their own data or data from a published journal paper. This exercise showed the impact that filtering, preprocessing, and different normalization methods had on gene inclusion in the final data set. We conclude that this course achieved its goals to equip students with skills to analyze data from a microarray experiment. We offer our insight about collaborative teaching as well as how other faculty might design and implement a similar interdisciplinary course.

Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

PubMed Central

Evans, Irene M.

2010-01-01

BIO2010 put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and Microarray Genome and Clustering Tool software and added R statistical software along with Bioconductor packages. In response to student feedback, one microarray data set was fully analyzed in class, starting from preprocessing to gene discovery to pathway analysis using the latter software. A class project was to conduct a similar analysis where students analyzed their own data or data from a published journal paper. This exercise showed the impact that filtering, preprocessing, and different normalization methods had on gene inclusion in the final data set. We conclude that this course achieved its goals to equip students with skills to analyze data from a microarray experiment. We offer our insight about collaborative teaching as well as how other faculty might design and implement a similar interdisciplinary course. PMID:20810954

AGScan: a pluggable microarray image quantification software based on the ImageJ library.

PubMed

Cathelin, R; Lopez, F; Klopp, Ch

2007-01-15

Many different programs are available to analyze microarray images. Most programs are commercial packages, some are free. In the latter group only few propose automatic grid alignment and batch mode. More often than not a program implements only one quantification algorithm. AGScan is an open source program that works on all major platforms. It is based on the ImageJ library [Rasband (1997-2006)] and offers a plug-in extension system to add new functions to manipulate images, align grid and quantify spots. It is appropriate for daily laboratory use and also as a framework for new algorithms. The program is freely distributed under X11 Licence. The install instructions can be found in the user manual. The software can be downloaded from http://mulcyber.toulouse.inra.fr/projects/agscan/. The questions and plug-ins can be sent to the contact listed below.

Chromosomal Microarray versus Karyotyping for Prenatal Diagnosis

PubMed Central

Wapner, Ronald J.; Martin, Christa Lese; Levy, Brynn; Ballif, Blake C.; Eng, Christine M.; Zachary, Julia M.; Savage, Melissa; Platt, Lawrence D.; Saltzman, Daniel; Grobman, William A.; Klugman, Susan; Scholl, Thomas; Simpson, Joe Leigh; McCall, Kimberly; Aggarwal, Vimla S.; Bunke, Brian; Nahum, Odelia; Patel, Ankita; Lamb, Allen N.; Thom, Elizabeth A.; Beaudet, Arthur L.; Ledbetter, David H.; Shaffer, Lisa G.; Jackson, Laird

2013-01-01

Background Chromosomal microarray analysis has emerged as a primary diagnostic tool for the evaluation of developmental delay and structural malformations in children. We aimed to evaluate the accuracy, efficacy, and incremental yield of chromosomal microarray analysis as compared with karyotyping for routine prenatal diagnosis. Methods Samples from women undergoing prenatal diagnosis at 29 centers were sent to a central karyotyping laboratory. Each sample was split in two; standard karyotyping was performed on one portion and the other was sent to one of four laboratories for chromosomal microarray. Results We enrolled a total of 4406 women. Indications for prenatal diagnosis were advanced maternal age (46.6%), abnormal result on Down’s syndrome screening (18.8%), structural anomalies on ultrasonography (25.2%), and other indications (9.4%). In 4340 (98.8%) of the fetal samples, microarray analysis was successful; 87.9% of samples could be used without tissue culture. Microarray analysis of the 4282 nonmosaic samples identified all the aneuploidies and unbalanced rearrangements identified on karyotyping but did not identify balanced translocations and fetal triploidy. In samples with a normal karyotype, microarray analysis revealed clinically relevant deletions or duplications in 6.0% with a structural anomaly and in 1.7% of those whose indications were advanced maternal age or positive screening results. Conclusions In the context of prenatal diagnostic testing, chromosomal microarray analysis identified additional, clinically significant cytogenetic information as compared with karyotyping and was equally efficacious in identifying aneuploidies and unbalanced rearrangements but did not identify balanced translocations and triploidies. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and others; ClinicalTrials.gov number, NCT01279733.) PMID:23215555

Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier.

PubMed

Kumar, Mukesh; Rath, Nitish Kumar; Rath, Santanu Kumar

2016-04-01

Microarray-based gene expression profiling has emerged as an efficient technique for classification, prognosis, diagnosis, and treatment of cancer. Frequent changes in the behavior of this disease generates an enormous volume of data. Microarray data satisfies both the veracity and velocity properties of big data, as it keeps changing with time. Therefore, the analysis of microarray datasets in a small amount of time is essential. They often contain a large amount of expression, but only a fraction of it comprises genes that are significantly expressed. The precise identification of genes of interest that are responsible for causing cancer are imperative in microarray data analysis. Most existing schemes employ a two-phase process such as feature selection/extraction followed by classification. In this paper, various statistical methods (tests) based on MapReduce are proposed for selecting relevant features. After feature selection, a MapReduce-based K-nearest neighbor (mrKNN) classifier is also employed to classify microarray data. These algorithms are successfully implemented in a Hadoop framework. A comparative analysis is done on these MapReduce-based models using microarray datasets of various dimensions. From the obtained results, it is observed that these models consume much less execution time than conventional models in processing big data. Copyright © 2016 Elsevier Inc. All rights reserved.

A Universal Protocol for Photochemical Covalent Immobilization of Intact Carbohydrates for the Preparation of Carbohydrate Microarrays

PubMed Central

Wang, Huibin; Zhang, Yiming; Yuan, Xun; Chen, Yi; Yan, Mingdi

2010-01-01

A universal photochemical method has been established for the immobilization of intact carbohydrates and their analogues, and for the fabrication of carbohydrate microarrays. The method features the use of perfluorophenyl azide (PFPA)-modified substrates and the photochemical reaction of surface azido groups with printed carbohydrates. Various aldoses, ketoses, non-reducing sugars such as alditols and their derivatives can be directly arrayed on the PFPA-modified chips. The lectin-recognition ability of arrayed mannose, glucose and their oligo- and polysaccharides were confirmed using surface plasmon resonance imaging and laser-induced fluorescence imaging. PMID:21138274

A universal protocol for photochemical covalent immobilization of intact carbohydrates for the preparation of carbohydrate microarrays.

PubMed

Wang, Huibin; Zhang, Yiming; Yuan, Xun; Chen, Yi; Yan, Mingdi

2011-01-19

A universal photochemical method has been established for the immobilization of intact carbohydrates and their analogues, and for the fabrication of carbohydrate microarrays. The method features the use of perfluorophenyl azide (PFPA)-modified substrates and the photochemical reaction of surface azido groups with printed carbohydrates. Various aldoses, ketoses, nonreducing sugars such as alditols, and their derivatives can be directly arrayed on the PFPA-modified chips. The lectin-recognition ability of arrayed mannose, glucose, and their oligo- and polysaccharides were confirmed using surface plasmon resonance imaging and laser-induced fluorescence imaging.

CMOS Imaging of Temperature Effects on Pin-Printed Xerogel Sensor Microarrays.

PubMed

Lei Yao; Ka Yi Yung; Chodavarapu, Vamsy P; Bright, Frank V

2011-04-01

In this paper, we study the effect of temperature on the operation and performance of a xerogel-based sensor microarrays coupled to a complementary metal-oxide semiconductor (CMOS) imager integrated circuit (IC) that images the photoluminescence response from the sensor microarray. The CMOS imager uses a 32 × 32 (1024 elements) array of active pixel sensors and each pixel includes a high-gain phototransistor to convert the detected optical signals into electrical currents. A correlated double sampling circuit and pixel address/digital control/signal integration circuit are also implemented on-chip. The CMOS imager data are read out as a serial coded signal. The sensor system uses a light-emitting diode to excite target analyte responsive organometallic luminophores doped within discrete xerogel-based sensor elements. As a proto type, we developed a 3 × 3 (9 elements) array of oxygen (O2) sensors. Each group of three sensor elements in the array (arranged in a column) is designed to provide a different and specific sensitivity to the target gaseous O2 concentration. This property of multiple sensitivities is achieved by using a mix of two O2 sensitive luminophores in each pin-printed xerogel sensor element. The CMOS imager is designed to be low noise and consumes a static power of 320.4 μW and an average dynamic power of 624.6 μW when operating at 100-Hz sampling frequency and 1.8-V dc power supply.

Image microarrays (IMA): Digital pathology's missing tool

PubMed Central

Hipp, Jason; Cheng, Jerome; Pantanowitz, Liron; Hewitt, Stephen; Yagi, Yukako; Monaco, James; Madabhushi, Anant; Rodriguez-canales, Jaime; Hanson, Jeffrey; Roy-Chowdhuri, Sinchita; Filie, Armando C.; Feldman, Michael D.; Tomaszewski, John E.; Shih, Natalie NC.; Brodsky, Victor; Giaccone, Giuseppe; Emmert-Buck, Michael R.; Balis, Ulysses J.

2011-01-01

Introduction: The increasing availability of whole slide imaging (WSI) data sets (digital slides) from glass slides offers new opportunities for the development of computer-aided diagnostic (CAD) algorithms. With the all-digital pathology workflow that these data sets will enable in the near future, literally millions of digital slides will be generated and stored. Consequently, the field in general and pathologists, specifically, will need tools to help extract actionable information from this new and vast collective repository. Methods: To address this limitation, we designed and implemented a tool (dCORE) to enable the systematic capture of image tiles with constrained size and resolution that contain desired histopathologic features. Results: In this communication, we describe a user-friendly tool that will enable pathologists to mine digital slides archives to create image microarrays (IMAs). IMAs are to digital slides as tissue microarrays (TMAs) are to cell blocks. Thus, a single digital slide could be transformed into an array of hundreds to thousands of high quality digital images, with each containing key diagnostic morphologies and appropriate controls. Current manual digital image cut-and-paste methods that allow for the creation of a grid of images (such as an IMA) of matching resolutions are tedious. Conclusion: The ability to create IMAs representing hundreds to thousands of vetted morphologic features has numerous applications in education, proficiency testing, consensus case review, and research. Lastly, in a manner analogous to the way conventional TMA technology has significantly accelerated in situ studies of tissue specimens use of IMAs has similar potential to significantly accelerate CAD algorithm development. PMID:22200030

ELISA-BASE: An Integrated Bioinformatics Tool for Analyzing and Tracking ELISA Microarray Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

White, Amanda M.; Collett, James L.; Seurynck-Servoss, Shannon L.

ELISA-BASE is an open-source database for capturing, organizing and analyzing protein enzyme-linked immunosorbent assay (ELISA) microarray data. ELISA-BASE is an extension of the BioArray Soft-ware Environment (BASE) database system, which was developed for DNA microarrays. In order to make BASE suitable for protein microarray experiments, we developed several plugins for importing and analyzing quantitative ELISA microarray data. Most notably, our Protein Microarray Analysis Tool (ProMAT) for processing quantita-tive ELISA data is now available as a plugin to the database.

Revisiting the immune microenvironment of diffuse large B-cell lymphoma using a tissue microarray and immunohistochemistry: robust semi-automated analysis reveals CD3 and FoxP3 as potential predictors of response to R-CHOP

PubMed Central

Coutinho, Rita; Clear, Andrew J.; Mazzola, Emanuele; Owen, Andrew; Greaves, Paul; Wilson, Andrew; Matthews, Janet; Lee, Abigail; Alvarez, Rute; da Silva, Maria Gomes; Cabeçadas, José; Neuberg, Donna; Calaminici, Maria; Gribben, John G.

2015-01-01

Gene expression studies have identified the microenvironment as a prognostic player in diffuse large B-cell lymphoma. However, there is a lack of simple immune biomarkers that can be applied in the clinical setting and could be helpful in stratifying patients. Immunohistochemistry has been used for this purpose but the results are inconsistent. We decided to reinvestigate the immune microenvironment and its impact using immunohistochemistry, with two systems of image analysis, in a large set of patients with diffuse large B-cell lymphoma. Diagnostic tissue from 309 patients was arrayed onto tissue microarrays. Results from 161 chemoimmunotherapy-treated patients were used for outcome prediction. Positive cells, percentage stained area and numbers of pixels/area were quantified and results were compared with the purpose of inferring consistency between the two semi-automated systems. Measurement cutpoints were assessed using a recursive partitioning algorithm classifying results according to survival. Kaplan-Meier estimators and Fisher exact tests were evaluated to check for significant differences between measurement classes, and for dependence between pairs of measurements, respectively. Results were validated by multivariate analysis incorporating the International Prognostic Index. The concordance between the two systems of image analysis was surprisingly high, supporting their applicability for immunohistochemistry studies. Patients with a high density of CD3 and FoxP3 by both methods had a better outcome. Automated analysis should be the preferred method for immunohistochemistry studies. Following the use of two methods of semi-automated analysis we suggest that CD3 and FoxP3 play a role in predicting response to chemoimmunotherapy in diffuse large B-cell lymphoma. PMID:25425693

Revisiting the immune microenvironment of diffuse large B-cell lymphoma using a tissue microarray and immunohistochemistry: robust semi-automated analysis reveals CD3 and FoxP3 as potential predictors of response to R-CHOP.

PubMed

Coutinho, Rita; Clear, Andrew J; Mazzola, Emanuele; Owen, Andrew; Greaves, Paul; Wilson, Andrew; Matthews, Janet; Lee, Abigail; Alvarez, Rute; da Silva, Maria Gomes; Cabeçadas, José; Neuberg, Donna; Calaminici, Maria; Gribben, John G

2015-03-01

Gene expression studies have identified the microenvironment as a prognostic player in diffuse large B-cell lymphoma. However, there is a lack of simple immune biomarkers that can be applied in the clinical setting and could be helpful in stratifying patients. Immunohistochemistry has been used for this purpose but the results are inconsistent. We decided to reinvestigate the immune microenvironment and its impact using immunohistochemistry, with two systems of image analysis, in a large set of patients with diffuse large B-cell lymphoma. Diagnostic tissue from 309 patients was arrayed onto tissue microarrays. Results from 161 chemoimmunotherapy-treated patients were used for outcome prediction. Positive cells, percentage stained area and numbers of pixels/area were quantified and results were compared with the purpose of inferring consistency between the two semi-automated systems. Measurement cutpoints were assessed using a recursive partitioning algorithm classifying results according to survival. Kaplan-Meier estimators and Fisher exact tests were evaluated to check for significant differences between measurement classes, and for dependence between pairs of measurements, respectively. Results were validated by multivariate analysis incorporating the International Prognostic Index. The concordance between the two systems of image analysis was surprisingly high, supporting their applicability for immunohistochemistry studies. Patients with a high density of CD3 and FoxP3 by both methods had a better outcome. Automated analysis should be the preferred method for immunohistochemistry studies. Following the use of two methods of semi-automated analysis we suggest that CD3 and FoxP3 play a role in predicting response to chemoimmunotherapy in diffuse large B-cell lymphoma. Copyright© Ferrata Storti Foundation.

MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format.

PubMed

Ahmed, Zeeshan; Dandekar, Thomas

2015-01-01

Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography  (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providing major original data, experimental and computational results in concise form. One major challenge for implementing a system for scientific literature analysis is extracting and analyzing text and figures from published PDF files by physical and logical document analysis. Here we present a product line architecture based bioinformatics tool 'Mining Scientific Literature (MSL)', which supports the extraction of text and images by interpreting all kinds of published PDF files using advanced data mining and image processing techniques. It provides modules for the marginalization of extracted text based on different coordinates and keywords, visualization of extracted figures and extraction of embedded text from all kinds of biological and biomedical figures using applied Optimal Character Recognition (OCR). Moreover, for further analysis and usage, it generates the system's output in different formats including text, PDF, XML and images files. Hence, MSL is an easy to install and use analysis tool to interpret published scientific literature in PDF format.

Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays

PubMed Central

2010-01-01

Background Recent developments in high-throughput methods of analyzing transcriptomic profiles are promising for many areas of biology, including ecophysiology. However, although commercial microarrays are available for most common laboratory models, transcriptome analysis in non-traditional model species still remains a challenge. Indeed, the signal resulting from heterologous hybridization is low and difficult to interpret because of the weak complementarity between probe and target sequences, especially when no microarray dedicated to a genetically close species is available. Results We show here that transcriptome analysis in a species genetically distant from laboratory models is made possible by using MAXRS, a new method of analyzing heterologous hybridization on microarrays. This method takes advantage of the design of several commercial microarrays, with different probes targeting the same transcript. To illustrate and test this method, we analyzed the transcriptome of king penguin pectoralis muscle hybridized to Affymetrix chicken microarrays, two organisms separated by an evolutionary distance of approximately 100 million years. The differential gene expression observed between different physiological situations computed by MAXRS was confirmed by real-time PCR on 10 genes out of 11 tested. Conclusions MAXRS appears to be an appropriate method for gene expression analysis under heterologous hybridization conditions. PMID:20509979

A Human Lectin Microarray for Sperm Surface Glycosylation Analysis *

PubMed Central

Sun, Yangyang; Cheng, Li; Gu, Yihua; Xin, Aijie; Wu, Bin; Zhou, Shumin; Guo, Shujuan; Liu, Yin; Diao, Hua; Shi, Huijuan; Wang, Guangyu; Tao, Sheng-ce

2016-01-01

Glycosylation is one of the most abundant and functionally important protein post-translational modifications. As such, technology for efficient glycosylation analysis is in high demand. Lectin microarrays are a powerful tool for such investigations and have been successfully applied for a variety of glycobiological studies. However, most of the current lectin microarrays are primarily constructed from plant lectins, which are not well suited for studies of human glycosylation because of the extreme complexity of human glycans. Herein, we constructed a human lectin microarray with 60 human lectin and lectin-like proteins. All of the lectins and lectin-like proteins were purified from yeast, and most showed binding to human glycans. To demonstrate the applicability of the human lectin microarray, human sperm were probed on the microarray and strong bindings were observed for several lectins, including galectin-1, 7, 8, GalNAc-T6, and ERGIC-53 (LMAN1). These bindings were validated by flow cytometry and fluorescence immunostaining. Further, mass spectrometry analysis showed that galectin-1 binds several membrane-associated proteins including heat shock protein 90. Finally, functional assays showed that binding of galectin-8 could significantly enhance the acrosome reaction within human sperms. To our knowledge, this is the first construction of a human lectin microarray, and we anticipate it will find wide use for a range of human or mammalian studies, alone or in combination with plant lectin microarrays. PMID:27364157

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gentry, T.; Schadt, C.; Zhou, J.

Microarray technology has the unparalleled potential tosimultaneously determine the dynamics and/or activities of most, if notall, of the microbial populations in complex environments such as soilsand sediments. Researchers have developed several types of arrays thatcharacterize the microbial populations in these samples based on theirphylogenetic relatedness or functional genomic content. Several recentstudies have used these microarrays to investigate ecological issues;however, most have only analyzed a limited number of samples withrelatively few experiments utilizing the full high-throughput potentialof microarray analysis. This is due in part to the unique analyticalchallenges that these samples present with regard to sensitivity,specificity, quantitation, and data analysis. Thismore » review discussesspecific applications of microarrays to microbial ecology research alongwith some of the latest studies addressing the difficulties encounteredduring analysis of complex microbial communities within environmentalsamples. With continued development, microarray technology may ultimatelyachieve its potential for comprehensive, high-throughput characterizationof microbial populations in near real-time.« less

A genome-scale map of expression for a mouse brain section obtained using voxelation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chin, Mark H.; Geng, Alex B.; Khan, Arshad H.

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed 2- dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative RT-PCR voxelation, mass spectrometry voxelation and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genesmore » with unexpected patterns were identified and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.« less

Tissue microarrays and digital image analysis.

PubMed

Ryan, Denise; Mulrane, Laoighse; Rexhepaj, Elton; Gallagher, William M

2011-01-01

Tissue microarrays (TMAs) have recently emerged as very valuable tools for high-throughput pathological assessment, especially in the cancer research arena. This important technology, however, has yet to fully penetrate into the area of toxicology. Here, we describe the creation of TMAs representative of samples produced from conventional toxicology studies within a large-scale, multi-institutional pan-European project, PredTox. PredTox, short for Predictive Toxicology, formed part of an EU FP6 Integrated Project, Innovative Medicines for Europe (InnoMed), and aimed to study pre-clinically 16 compounds of known liver and/or kidney toxicity. In more detail, TMAs were constructed from materials corresponding to the full face sections of liver and kidney from rats treated with different drug candidates by members of the consortium. We also describe the process of digital slide scanning of kidney and liver sections, in the context of creating an online resource of histopathological data.

PRACTICAL STRATEGIES FOR PROCESSING AND ANALYZING SPOTTED OLIGONUCLEOTIDE MICROARRAY DATA

EPA Science Inventory

Thoughtful data analysis is as important as experimental design, biological sample quality, and appropriate experimental procedures for making microarrays a useful supplement to traditional toxicology. In the present study, spotted oligonucleotide microarrays were used to profile...

A biomimetic algorithm for the improved detection of microarray features

NASA Astrophysics Data System (ADS)

Nicolau, Dan V., Jr.; Nicolau, Dan V.; Maini, Philip K.

2007-02-01

One the major difficulties of microarray technology relate to the processing of large and - importantly - error-loaded images of the dots on the chip surface. Whatever the source of these errors, those obtained in the first stage of data acquisition - segmentation - are passed down to the subsequent processes, with deleterious results. As it has been demonstrated recently that biological systems have evolved algorithms that are mathematically efficient, this contribution attempts to test an algorithm that mimics a bacterial-"patented" algorithm for the search of available space and nutrients to find, "zero-in" and eventually delimitate the features existent on the microarray surface.

Proteomic analysis of formalin-fixed paraffin embedded tissue by MALDI imaging mass spectrometry

PubMed Central

Casadonte, Rita; Caprioli, Richard M

2012-01-01

Archived formalin-fixed paraffin-embedded (FFPE) tissue collections represent a valuable informational resource for proteomic studies. Multiple FFPE core biopsies can be assembled in a single block to form tissue microarrays (TMAs). We describe a protocol for analyzing protein in FFPE -TMAs using matrix-assisted laser desorption/ionization (MAL DI) imaging mass spectrometry (IMS). The workflow incorporates an antigen retrieval step following deparaffinization, in situ trypsin digestion, matrix application and then mass spectrometry signal acquisition. The direct analysis of FFPE -TMA tissue using IMS allows direct analysis of multiple tissue samples in a single experiment without extraction and purification of proteins. The advantages of high speed and throughput, easy sample handling and excellent reproducibility make this technology a favorable approach for the proteomic analysis of clinical research cohorts with large sample numbers. For example, TMA analysis of 300 FFPE cores would typically require 6 h of total time through data acquisition, not including data analysis. PMID:22011652

GeneXplorer: an interactive web application for microarray data visualization and analysis.

PubMed

Rees, Christian A; Demeter, Janos; Matese, John C; Botstein, David; Sherlock, Gavin

2004-10-01

When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to figures within the paper, can be browsed. We set out to create a CGI application containing many of the features of some of the existing standalone software for the visualization of clustered microarray data. We present GeneXplorer, a web application for interactive microarray data visualization and analysis in a web environment. GeneXplorer allows users to browse a microarray dataset in an intuitive fashion. It provides simple access to microarray data over the Internet and uses only HTML and JavaScript to display graphic and annotation information. It provides radar and zoom views of the data, allows display of the nearest neighbors to a gene expression vector based on their Pearson correlations and provides the ability to search gene annotation fields. The software is released under the permissive MIT Open Source license, and the complete documentation and the entire source code are freely available for download from CPAN http://search.cpan.org/dist/Microarray-GeneXplorer/.

Nanotechnology: moving from microarrays toward nanoarrays.

PubMed

Chen, Hua; Li, Jun

2007-01-01

Microarrays are important tools for high-throughput analysis of biomolecules. The use of microarrays for parallel screening of nucleic acid and protein profiles has become an industry standard. A few limitations of microarrays are the requirement for relatively large sample volumes and elongated incubation time, as well as the limit of detection. In addition, traditional microarrays make use of bulky instrumentation for the detection, and sample amplification and labeling are quite laborious, which increase analysis cost and delays the time for obtaining results. These problems limit microarray techniques from point-of-care and field applications. One strategy for overcoming these problems is to develop nanoarrays, particularly electronics-based nanoarrays. With further miniaturization, higher sensitivity, and simplified sample preparation, nanoarrays could potentially be employed for biomolecular analysis in personal healthcare and monitoring of trace pathogens. In this chapter, it is intended to introduce the concept and advantage of nanotechnology and then describe current methods and protocols for novel nanoarrays in three aspects: (1) label-free nucleic acids analysis using nanoarrays, (2) nanoarrays for protein detection by conventional optical fluorescence microscopy as well as by novel label-free methods such as atomic force microscopy, and (3) nanoarray for enzymatic-based assay. These nanoarrays will have significant applications in drug discovery, medical diagnosis, genetic testing, environmental monitoring, and food safety inspection.

A meta-data based method for DNA microarray imputation.

PubMed

Jörnsten, Rebecka; Ouyang, Ming; Wang, Hui-Yu

2007-03-29

DNA microarray experiments are conducted in logical sets, such as time course profiling after a treatment is applied to the samples, or comparisons of the samples under two or more conditions. Due to cost and design constraints of spotted cDNA microarray experiments, each logical set commonly includes only a small number of replicates per condition. Despite the vast improvement of the microarray technology in recent years, missing values are prevalent. Intuitively, imputation of missing values is best done using many replicates within the same logical set. In practice, there are few replicates and thus reliable imputation within logical sets is difficult. However, it is in the case of few replicates that the presence of missing values, and how they are imputed, can have the most profound impact on the outcome of downstream analyses (e.g. significance analysis and clustering). This study explores the feasibility of imputation across logical sets, using the vast amount of publicly available microarray data to improve imputation reliability in the small sample size setting. We download all cDNA microarray data of Saccharomyces cerevisiae, Arabidopsis thaliana, and Caenorhabditis elegans from the Stanford Microarray Database. Through cross-validation and simulation, we find that, for all three species, our proposed imputation using data from public databases is far superior to imputation within a logical set, sometimes to an astonishing degree. Furthermore, the imputation root mean square error for significant genes is generally a lot less than that of non-significant ones. Since downstream analysis of significant genes, such as clustering and network analysis, can be very sensitive to small perturbations of estimated gene effects, it is highly recommended that researchers apply reliable data imputation prior to further analysis. Our method can also be applied to cDNA microarray experiments from other species, provided good reference data are available.

BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets

PubMed Central

2010-01-01

Background The development of DNA microarrays has facilitated the generation of hundreds of thousands of transcriptomic datasets. The use of a common reference microarray design allows existing transcriptomic data to be readily compared and re-analysed in the light of new data, and the combination of this design with large datasets is ideal for 'systems'-level analyses. One issue is that these datasets are typically collected over many years and may be heterogeneous in nature, containing different microarray file formats and gene array layouts, dye-swaps, and showing varying scales of log2- ratios of expression between microarrays. Excellent software exists for the normalisation and analysis of microarray data but many data have yet to be analysed as existing methods struggle with heterogeneous datasets; options include normalising microarrays on an individual or experimental group basis. Our solution was to develop the Batch Anti-Banana Algorithm in R (BABAR) algorithm and software package which uses cyclic loess to normalise across the complete dataset. We have already used BABAR to analyse the function of Salmonella genes involved in the process of infection of mammalian cells. Results The only input required by BABAR is unprocessed GenePix or BlueFuse microarray data files. BABAR provides a combination of 'within' and 'between' microarray normalisation steps and diagnostic boxplots. When applied to a real heterogeneous dataset, BABAR normalised the dataset to produce a comparable scaling between the microarrays, with the microarray data in excellent agreement with RT-PCR analysis. When applied to a real non-heterogeneous dataset and a simulated dataset, BABAR's performance in identifying differentially expressed genes showed some benefits over standard techniques. Conclusions BABAR is an easy-to-use software tool, simplifying the simultaneous normalisation of heterogeneous two-colour common reference design cDNA microarray-based transcriptomic datasets. We show BABAR transforms real and simulated datasets to allow for the correct interpretation of these data, and is the ideal tool to facilitate the identification of differentially expressed genes or network inference analysis from transcriptomic datasets. PMID:20128918

Isolation and measurement of the features of arrays of cell aggregates formed by dielectrophoresis using the user-specified Multi Regions Masking (MRM) technique

NASA Astrophysics Data System (ADS)

Yusvana, Rama; Headon, Denis; Markx, Gerard H.

2009-08-01

The use of dielectrophoresis for the construction of artificial skin tissue with skin cells in follicle-like 3D cell aggregates in well-defined patterns is demonstrated. To analyse the patterns produced and to study their development after their formation a Virtual Instrument (VI) system was developed using the LabVIEW IMAQ Vision Development Module. A series of programming functions (algorithms) was used to isolate the features on the image (in our case; the patterned aggregates) and separate them from all other unwanted regions on the image. The image was subsequently converted into a binary version, covering only the desired microarray regions which could then be analysed by computer for automatic object measurements. The analysis utilized the simple and easy-to-use User-Specified Multi-Regions Masking (MRM) technique, which allows one to concentrate the analysis on the desired regions specified in the mask. This simplified the algorithms for the analysis of images of cell arrays having similar geometrical properties. By having a collection of scripts containing masks of different patterns, it was possible to quickly and efficiently develop sets of custom virtual instruments for the offline or online analysis of images of cell arrays in the database.

The efficacy of microarray screening for autosomal recessive retinitis pigmentosa in routine clinical practice

PubMed Central

van Huet, Ramon A. C.; Pierrache, Laurence H.M.; Meester-Smoor, Magda A.; Klaver, Caroline C.W.; van den Born, L. Ingeborgh; Hoyng, Carel B.; de Wijs, Ilse J.; Collin, Rob W. J.; Hoefsloot, Lies H.

2015-01-01

Purpose To determine the efficacy of multiple versions of a commercially available arrayed primer extension (APEX) microarray chip for autosomal recessive retinitis pigmentosa (arRP). Methods We included 250 probands suspected of arRP who were genetically analyzed with the APEX microarray between January 2008 and November 2013. The mode of inheritance had to be autosomal recessive according to the pedigree (including isolated cases). If the microarray identified a heterozygous mutation, we performed Sanger sequencing of exons and exon–intron boundaries of that specific gene. The efficacy of this microarray chip with the additional Sanger sequencing approach was determined by the percentage of patients that received a molecular diagnosis. We also collected data from genetic tests other than the APEX analysis for arRP to provide a detailed description of the molecular diagnoses in our study cohort. Results The APEX microarray chip for arRP identified the molecular diagnosis in 21 (8.5%) of the patients in our cohort. Additional Sanger sequencing yielded a second mutation in 17 patients (6.8%), thereby establishing the molecular diagnosis. In total, 38 patients (15.2%) received a molecular diagnosis after analysis using the microarray and additional Sanger sequencing approach. Further genetic analyses after a negative result of the arRP microarray (n = 107) resulted in a molecular diagnosis of arRP (n = 23), autosomal dominant RP (n = 5), X-linked RP (n = 2), and choroideremia (n = 1). Conclusions The efficacy of the commercially available APEX microarray chips for arRP appears to be low, most likely caused by the limitations of this technique and the genetic and allelic heterogeneity of RP. Diagnostic yields up to 40% have been reported for next-generation sequencing (NGS) techniques that, as expected, thereby outperform targeted APEX analysis. PMID:25999674

Principles of gene microarray data analysis.

PubMed

Mocellin, Simone; Rossi, Carlo Riccardo

2007-01-01

The development of several gene expression profiling methods, such as comparative genomic hybridization (CGH), differential display, serial analysis of gene expression (SAGE), and gene microarray, together with the sequencing of the human genome, has provided an opportunity to monitor and investigate the complex cascade of molecular events leading to tumor development and progression. The availability of such large amounts of information has shifted the attention of scientists towards a nonreductionist approach to biological phenomena. High throughput technologies can be used to follow changing patterns of gene expression over time. Among them, gene microarray has become prominent because it is easier to use, does not require large-scale DNA sequencing, and allows for the parallel quantification of thousands of genes from multiple samples. Gene microarray technology is rapidly spreading worldwide and has the potential to drastically change the therapeutic approach to patients affected with tumor. Therefore, it is of paramount importance for both researchers and clinicians to know the principles underlying the analysis of the huge amount of data generated with microarray technology.

16S rRNA gene-based phylogenetic microarray for simultaneous identification of members of the genus Burkholderia.

PubMed

Schönmann, Susan; Loy, Alexander; Wimmersberger, Céline; Sobek, Jens; Aquino, Catharine; Vandamme, Peter; Frey, Beat; Rehrauer, Hubert; Eberl, Leo

2009-04-01

For cultivation-independent and highly parallel analysis of members of the genus Burkholderia, an oligonucleotide microarray (phylochip) consisting of 131 hierarchically nested 16S rRNA gene-targeted oligonucleotide probes was developed. A novel primer pair was designed for selective amplification of a 1.3 kb 16S rRNA gene fragment of Burkholderia species prior to microarray analysis. The diagnostic performance of the microarray for identification and differentiation of Burkholderia species was tested with 44 reference strains of the genera Burkholderia, Pandoraea, Ralstonia and Limnobacter. Hybridization patterns based on presence/absence of probe signals were interpreted semi-automatically using the novel likelihood-based strategy of the web-tool Phylo- Detect. Eighty-eight per cent of the reference strains were correctly identified at the species level. The evaluated microarray was applied to investigate shifts in the Burkholderia community structure in acidic forest soil upon addition of cadmium, a condition that selected for Burkholderia species. The microarray results were in agreement with those obtained from phylogenetic analysis of Burkholderia 16S rRNA gene sequences recovered from the same cadmiumcontaminated soil, demonstrating the value of the Burkholderia phylochip for determinative and environmental studies.

Support vector machine and principal component analysis for microarray data classification

NASA Astrophysics Data System (ADS)

Astuti, Widi; Adiwijaya

2018-03-01

Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

Experimental Approaches to Microarray Analysis of Tumor Samples

ERIC Educational Resources Information Center

Furge, Laura Lowe; Winter, Michael B.; Meyers, Jacob I.; Furge, Kyle A.

2008-01-01

Comprehensive measurement of gene expression using high-density nucleic acid arrays (i.e. microarrays) has become an important tool for investigating the molecular differences in clinical and research samples. Consequently, inclusion of discussion in biochemistry, molecular biology, or other appropriate courses of microarray technologies has…

Multiplex cDNA quantification method that facilitates the standardization of gene expression data

PubMed Central

Gotoh, Osamu; Murakami, Yasufumi; Suyama, Akira

2011-01-01

Microarray-based gene expression measurement is one of the major methods for transcriptome analysis. However, current microarray data are substantially affected by microarray platforms and RNA references because of the microarray method can provide merely the relative amounts of gene expression levels. Therefore, valid comparisons of the microarray data require standardized platforms, internal and/or external controls and complicated normalizations. These requirements impose limitations on the extensive comparison of gene expression data. Here, we report an effective approach to removing the unfavorable limitations by measuring the absolute amounts of gene expression levels on common DNA microarrays. We have developed a multiplex cDNA quantification method called GEP-DEAN (Gene expression profiling by DCN-encoding-based analysis). The method was validated by using chemically synthesized DNA strands of known quantities and cDNA samples prepared from mouse liver, demonstrating that the absolute amounts of cDNA strands were successfully measured with a sensitivity of 18 zmol in a highly multiplexed manner in 7 h. PMID:21415008

Split-plot microarray experiments: issues of design, power and sample size.

PubMed

Tsai, Pi-Wen; Lee, Mei-Ling Ting

2005-01-01

This article focuses on microarray experiments with two or more factors in which treatment combinations of the factors corresponding to the samples paired together onto arrays are not completely random. A main effect of one (or more) factor(s) is confounded with arrays (the experimental blocks). This is called a split-plot microarray experiment. We utilise an analysis of variance (ANOVA) model to assess differentially expressed genes for between-array and within-array comparisons that are generic under a split-plot microarray experiment. Instead of standard t- or F-test statistics that rely on mean square errors of the ANOVA model, we use a robust method, referred to as 'a pooled percentile estimator', to identify genes that are differentially expressed across different treatment conditions. We illustrate the design and analysis of split-plot microarray experiments based on a case application described by Jin et al. A brief discussion of power and sample size for split-plot microarray experiments is also presented.

MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format

PubMed Central

Ahmed, Zeeshan; Dandekar, Thomas

2018-01-01

Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography  (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providing major original data, experimental and computational results in concise form. One major challenge for implementing a system for scientific literature analysis is extracting and analyzing text and figures from published PDF files by physical and logical document analysis. Here we present a product line architecture based bioinformatics tool ‘Mining Scientific Literature (MSL)’, which supports the extraction of text and images by interpreting all kinds of published PDF files using advanced data mining and image processing techniques. It provides modules for the marginalization of extracted text based on different coordinates and keywords, visualization of extracted figures and extraction of embedded text from all kinds of biological and biomedical figures using applied Optimal Character Recognition (OCR). Moreover, for further analysis and usage, it generates the system’s output in different formats including text, PDF, XML and images files. Hence, MSL is an easy to install and use analysis tool to interpret published scientific literature in PDF format. PMID:29721305

Detection of cryptic pathogenic copy number variations and constitutional loss of heterozygosity using high resolution SNP microarray analysis in 117 patients referred for cytogenetic analysis and impact on clinical practice.

PubMed

Bruno, D L; Ganesamoorthy, D; Schoumans, J; Bankier, A; Coman, D; Delatycki, M; Gardner, R J M; Hunter, M; James, P A; Kannu, P; McGillivray, G; Pachter, N; Peters, H; Rieubland, C; Savarirayan, R; Scheffer, I E; Sheffield, L; Tan, T; White, S M; Yeung, A; Bowman, Z; Ngo, C; Choy, K W; Cacheux, V; Wong, L; Amor, D J; Slater, H R

2009-02-01

Microarray genome analysis is realising its promise for improving detection of genetic abnormalities in individuals with mental retardation and congenital abnormality. Copy number variations (CNVs) are now readily detectable using a variety of platforms and a major challenge is the distinction of pathogenic from ubiquitous, benign polymorphic CNVs. The aim of this study was to investigate replacement of time consuming, locus specific testing for specific microdeletion and microduplication syndromes with microarray analysis, which theoretically should detect all known syndromes with CNV aetiologies as well as new ones. Genome wide copy number analysis was performed on 117 patients using Affymetrix 250K microarrays. 434 CNVs (195 losses and 239 gains) were found, including 18 pathogenic CNVs and 9 identified as "potentially pathogenic". Almost all pathogenic CNVs were larger than 500 kb, significantly larger than the median size of all CNVs detected. Segmental regions of loss of heterozygosity larger than 5 Mb were found in 5 patients. Genome microarray analysis has improved diagnostic success in this group of patients. Several examples of recently discovered "new syndromes" were found suggesting they are more common than previously suspected and collectively are likely to be a major cause of mental retardation. The findings have several implications for clinical practice. The study revealed the potential to make genetic diagnoses that were not evident in the clinical presentation, with implications for pretest counselling and the consent process. The importance of contributing novel CNVs to high quality databases for genotype-phenotype analysis and review of guidelines for selection of individuals for microarray analysis is emphasised.

Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays

PubMed Central

Lawson, Jonathan; Robinson-Vyas, Rupesh J; McQuillan, Janette P; Paterson, Andy; Christie, Sarah; Kidza-Griffiths, Matthew; McDuffus, Leigh-Anne; Moutasim, Karwan A; Shaw, Emily C; Kiltie, Anne E; Howat, William J; Hanby, Andrew M; Thomas, Gareth J; Smittenaar, Peter

2017-01-01

Background: Academic pathology suffers from an acute and growing lack of workforce resource. This especially impacts on translational elements of clinical trials, which can require detailed analysis of thousands of tissue samples. We tested whether crowdsourcing – enlisting help from the public – is a sufficiently accurate method to score such samples. Methods: We developed a novel online interface to train and test lay participants on cancer detection and immunohistochemistry scoring in tissue microarrays. Lay participants initially performed cancer detection on lung cancer images stained for CD8, and we measured how extending a basic tutorial by annotated example images and feedback-based training affected cancer detection accuracy. We then applied this tutorial to additional cancer types and immunohistochemistry markers – bladder/ki67, lung/EGFR, and oesophageal/CD8 – to establish accuracy compared with experts. Using this optimised tutorial, we then tested lay participants' accuracy on immunohistochemistry scoring of lung/EGFR and bladder/p53 samples. Results: We observed that for cancer detection, annotated example images and feedback-based training both improved accuracy compared with a basic tutorial only. Using this optimised tutorial, we demonstrate highly accurate (>0.90 area under curve) detection of cancer in samples stained with nuclear, cytoplasmic and membrane cell markers. We also observed high Spearman correlations between lay participants and experts for immunohistochemistry scoring (0.91 (0.78, 0.96) and 0.97 (0.91, 0.99) for lung/EGFR and bladder/p53 samples, respectively). Conclusions: These results establish crowdsourcing as a promising method to screen large data sets for biomarkers in cancer pathology research across a range of cancers and immunohistochemical stains. PMID:27959886

MALDI-TOF mass spectrometry for quantitative gene expression analysis of acid responses in Staphylococcus aureus.

PubMed

Rode, Tone Mari; Berget, Ingunn; Langsrud, Solveig; Møretrø, Trond; Holck, Askild

2009-07-01

Microorganisms are constantly exposed to new and altered growth conditions, and respond by changing gene expression patterns. Several methods for studying gene expression exist. During the last decade, the analysis of microarrays has been one of the most common approaches applied for large scale gene expression studies. A relatively new method for gene expression analysis is MassARRAY, which combines real competitive-PCR and MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry. In contrast to microarray methods, MassARRAY technology is suitable for analysing a larger number of samples, though for a smaller set of genes. In this study we compare the results from MassARRAY with microarrays on gene expression responses of Staphylococcus aureus exposed to acid stress at pH 4.5. RNA isolated from the same stress experiments was analysed using both the MassARRAY and the microarray methods. The MassARRAY and microarray methods showed good correlation. Both MassARRAY and microarray estimated somewhat lower fold changes compared with quantitative real-time PCR (qRT-PCR). The results confirmed the up-regulation of the urease genes in acidic environments, and also indicated the importance of metal ion regulation. This study shows that the MassARRAY technology is suitable for gene expression analysis in prokaryotes, and has advantages when a set of genes is being analysed for an organism exposed to many different environmental conditions.

Microarray analysis of potential genes in the pathogenesis of recurrent oral ulcer.

PubMed

Han, Jingying; He, Zhiwei; Li, Kun; Hou, Lu

2015-01-01

Recurrent oral ulcer seriously threatens patients' daily life and health. This study investigated potential genes and pathways that participate in the pathogenesis of recurrent oral ulcer by high throughput bioinformatic analysis. RT-PCR and Western blot were applied to further verify screened interleukins effect. Recurrent oral ulcer related genes were collected from websites and papers, and further found out from Human Genome 280 6.0 microarray data. Each pathway of recurrent oral ulcer related genes were got through chip hybridization. RT-PCR was applied to test four recurrent oral ulcer related genes to verify the microarray data. Data transformation, scatter plot, clustering analysis, and expression pattern analysis were used to analyze recurrent oral ulcer related gene expression changes. Recurrent oral ulcer gene microarray was successfully established. Microarray showed that 551 genes involved in recurrent oral ulcer activity and 196 genes were recurrent oral ulcer related genes. Of them, 76 genes up-regulated, 62 genes down-regulated, and 58 genes up-/down-regulated. Total expression level up-regulated 752 times (60%) and down-regulated 485 times (40%). IL-2 plays an important role in the occurrence, development and recurrence of recurrent oral ulcer on the mRNA and protein levels. Gene microarray can be used to analyze potential genes and pathways in recurrent oral ulcer. IL-2 may be involved in the pathogenesis of recurrent oral ulcer.

A preliminary result of three-dimensional microarray technology to gene analysis with endoscopic ultrasound-guided fine-needle aspiration specimens and pancreatic juices

PubMed Central

2010-01-01

Background Analysis of gene expression and gene mutation may add information to be different from ordinary pathological tissue diagnosis. Since samples obtained endoscopically are very small, it is desired that more sensitive technology is developed for gene analysis. We investigated whether gene expression and gene mutation analysis by newly developed ultra-sensitive three-dimensional (3D) microarray is possible using small amount samples from endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) specimens and pancreatic juices. Methods Small amount samples from 17 EUS-FNA specimens and 16 pancreatic juices were obtained. After nucleic acid extraction, the samples were amplified with labeling and analyzed by the 3D microarray. Results The analyzable rate with the microarray was 46% (6/13) in EUS-FNA specimens of RNAlater® storage, and RNA degradations were observed in all the samples of frozen storage. In pancreatic juices, the analyzable rate was 67% (4/6) in frozen storage samples and 20% (2/10) in RNAlater® storage. EUS-FNA specimens were classified into cancer and non-cancer by gene expression analysis and K-ras codon 12 mutations were also detected using the 3D microarray. Conclusions Gene analysis from small amount samples obtained endoscopically was possible by newly developed 3D microarray technology. High quality RNA from EUS-FNA samples were obtained and remained in good condition only using RNA stabilizer. In contrast, high quality RNA from pancreatic juice samples were obtained only in frozen storage without RNA stabilizer. PMID:20416107

MAAMD: a workflow to standardize meta-analyses and comparison of affymetrix microarray data

PubMed Central

2014-01-01

Background Mandatory deposit of raw microarray data files for public access, prior to study publication, provides significant opportunities to conduct new bioinformatics analyses within and across multiple datasets. Analysis of raw microarray data files (e.g. Affymetrix CEL files) can be time consuming, complex, and requires fundamental computational and bioinformatics skills. The development of analytical workflows to automate these tasks simplifies the processing of, improves the efficiency of, and serves to standardize multiple and sequential analyses. Once installed, workflows facilitate the tedious steps required to run rapid intra- and inter-dataset comparisons. Results We developed a workflow to facilitate and standardize Meta-Analysis of Affymetrix Microarray Data analysis (MAAMD) in Kepler. Two freely available stand-alone software tools, R and AltAnalyze were embedded in MAAMD. The inputs of MAAMD are user-editable csv files, which contain sample information and parameters describing the locations of input files and required tools. MAAMD was tested by analyzing 4 different GEO datasets from mice and drosophila. MAAMD automates data downloading, data organization, data quality control assesment, differential gene expression analysis, clustering analysis, pathway visualization, gene-set enrichment analysis, and cross-species orthologous-gene comparisons. MAAMD was utilized to identify gene orthologues responding to hypoxia or hyperoxia in both mice and drosophila. The entire set of analyses for 4 datasets (34 total microarrays) finished in ~ one hour. Conclusions MAAMD saves time, minimizes the required computer skills, and offers a standardized procedure for users to analyze microarray datasets and make new intra- and inter-dataset comparisons. PMID:24621103

Gene Expression Browser: Large-Scale and Cross-Experiment Microarray Data Management, Search & Visualization

USDA-ARS?s Scientific Manuscript database

The amount of microarray gene expression data in public repositories has been increasing exponentially for the last couple of decades. High-throughput microarray data integration and analysis has become a critical step in exploring the large amount of expression data for biological discovery. Howeve...

Multiplexed Analysis of Serum Breast and Ovarian Cancer Markers by Means of Suspension Bead-quantum Dot Microarrays

NASA Astrophysics Data System (ADS)

Brazhnik, Kristina; Sokolova, Zinaida; Baryshnikova, Maria; Bilan, Regina; Nabiev, Igor; Sukhanova, Alyona

Multiplexed analysis of cancer markers is crucial for early tumor diagnosis and screening. We have designed lab-on-a-bead microarray for quantitative detection of three breast cancer markers in human serum. Quantum dots were used as bead-bound fluorescent tags for identifying each marker by means of flow cytometry. Antigen-specific beads reliably detected CA 15-3, CEA, and CA 125 in serum samples, providing clear discrimination between the samples with respect to the antigen levels. The novel microarray is advantageous over the routine single-analyte ones due to the simultaneous detection of various markers. Therefore the developed microarray is a promising tool for serum tumor marker profiling.

Construction of a cDNA microarray derived from the ascidian Ciona intestinalis.

PubMed

Azumi, Kaoru; Takahashi, Hiroki; Miki, Yasufumi; Fujie, Manabu; Usami, Takeshi; Ishikawa, Hisayoshi; Kitayama, Atsusi; Satou, Yutaka; Ueno, Naoto; Satoh, Nori

2003-10-01

A cDNA microarray was constructed from a basal chordate, the ascidian Ciona intestinalis. The draft genome of Ciona has been read and inferred to contain approximately 16,000 protein-coding genes, and cDNAs for transcripts of 13,464 genes have been characterized and compiled as the "Ciona intestinalis Gene Collection Release I". In the present study, we constructed a cDNA microarray of these 13,464 Ciona genes. A preliminary experiment with Cy3- and Cy5-labeled probes showed extensive differential gene expression between fertilized eggs and larvae. In addition, there was a good correlation between results obtained by the present microarray analysis and those from previous EST analyses. This first microarray of a large collection of Ciona intestinalis cDNA clones should facilitate the analysis of global gene expression and gene networks during the embryogenesis of basal chordates.

Digital Microarrays: Single-Molecule Readout with Interferometric Detection of Plasmonic Nanorod Labels.

PubMed

Sevenler, Derin; Daaboul, George G; Ekiz Kanik, Fulya; Ünlü, Neşe Lortlar; Ünlü, M Selim

2018-05-21

DNA and protein microarrays are a high-throughput technology that allow the simultaneous quantification of tens of thousands of different biomolecular species. The mediocre sensitivity and limited dynamic range of traditional fluorescence microarrays compared to other detection techniques have been the technology's Achilles' heel and prevented their adoption for many biomedical and clinical diagnostic applications. Previous work to enhance the sensitivity of microarray readout to the single-molecule ("digital") regime have either required signal amplifying chemistry or sacrificed throughput, nixing the platform's primary advantages. Here, we report the development of a digital microarray which extends both the sensitivity and dynamic range of microarrays by about 3 orders of magnitude. This technique uses functionalized gold nanorods as single-molecule labels and an interferometric scanner which can rapidly enumerate individual nanorods by imaging them with a 10× objective lens. This approach does not require any chemical signal enhancement such as silver deposition and scans arrays with a throughput similar to commercial fluorescence scanners. By combining single-nanoparticle enumeration and ensemble measurements of spots when the particles are very dense, this system achieves a dynamic range of about 6 orders of magnitude directly from a single scan. As a proof-of-concept digital protein microarray assay, we demonstrated detection of hepatitis B virus surface antigen in buffer with a limit of detection of 3.2 pg/mL. More broadly, the technique's simplicity and high-throughput nature make digital microarrays a flexible platform technology with a wide range of potential applications in biomedical research and clinical diagnostics.

MicroGen: a MIAME compliant web system for microarray experiment information and workflow management.

PubMed

Burgarella, Sarah; Cattaneo, Dario; Pinciroli, Francesco; Masseroli, Marco

2005-12-01

Improvements of bio-nano-technologies and biomolecular techniques have led to increasing production of high-throughput experimental data. Spotted cDNA microarray is one of the most diffuse technologies, used in single research laboratories and in biotechnology service facilities. Although they are routinely performed, spotted microarray experiments are complex procedures entailing several experimental steps and actors with different technical skills and roles. During an experiment, involved actors, who can also be located in a distance, need to access and share specific experiment information according to their roles. Furthermore, complete information describing all experimental steps must be orderly collected to allow subsequent correct interpretation of experimental results. We developed MicroGen, a web system for managing information and workflow in the production pipeline of spotted microarray experiments. It is constituted of a core multi-database system able to store all data completely characterizing different spotted microarray experiments according to the Minimum Information About Microarray Experiments (MIAME) standard, and of an intuitive and user-friendly web interface able to support the collaborative work required among multidisciplinary actors and roles involved in spotted microarray experiment production. MicroGen supports six types of user roles: the researcher who designs and requests the experiment, the spotting operator, the hybridisation operator, the image processing operator, the system administrator, and the generic public user who can access the unrestricted part of the system to get information about MicroGen services. MicroGen represents a MIAME compliant information system that enables managing workflow and supporting collaborative work in spotted microarray experiment production.

Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.

PubMed

Yuan, Yinyin; Failmezger, Henrik; Rueda, Oscar M; Ali, H Raza; Gräf, Stefan; Chin, Suet-Feung; Schwarz, Roland F; Curtis, Christina; Dunning, Mark J; Bardwell, Helen; Johnson, Nicola; Doyle, Sarah; Turashvili, Gulisa; Provenzano, Elena; Aparicio, Sam; Caldas, Carlos; Markowetz, Florian

2012-10-24

Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.

Implementation of GenePattern within the Stanford Microarray Database.

PubMed

Hubble, Jeremy; Demeter, Janos; Jin, Heng; Mao, Maria; Nitzberg, Michael; Reddy, T B K; Wymore, Farrell; Zachariah, Zachariah K; Sherlock, Gavin; Ball, Catherine A

2009-01-01

Hundreds of researchers across the world use the Stanford Microarray Database (SMD; http://smd.stanford.edu/) to store, annotate, view, analyze and share microarray data. In addition to providing registered users at Stanford access to their own data, SMD also provides access to public data, and tools with which to analyze those data, to any public user anywhere in the world. Previously, the addition of new microarray data analysis tools to SMD has been limited by available engineering resources, and in addition, the existing suite of tools did not provide a simple way to design, execute and share analysis pipelines, or to document such pipelines for the purposes of publication. To address this, we have incorporated the GenePattern software package directly into SMD, providing access to many new analysis tools, as well as a plug-in architecture that allows users to directly integrate and share additional tools through SMD. In this article, we describe our implementation of the GenePattern microarray analysis software package into the SMD code base. This extension is available with the SMD source code that is fully and freely available to others under an Open Source license, enabling other groups to create a local installation of SMD with an enriched data analysis capability.

Call for a Computer-Aided Cancer Detection and Classification Research Initiative in Oman.

PubMed

Mirzal, Andri; Chaudhry, Shafique Ahmad

2016-01-01

Cancer is a major health problem in Oman. It is reported that cancer incidence in Oman is the second highest after Saudi Arabia among Gulf Cooperation Council countries. Based on GLOBOCAN estimates, Oman is predicted to face an almost two-fold increase in cancer incidence in the period 2008-2020. However, cancer research in Oman is still in its infancy. This is due to the fact that medical institutions and infrastructure that play central roles in data collection and analysis are relatively new developments in Oman. We believe the country requires an organized plan and efforts to promote local cancer research. In this paper, we discuss current research progress in cancer diagnosis using machine learning techniques to optimize computer aided cancer detection and classification (CAD). We specifically discuss CAD using two major medical data, i.e., medical imaging and microarray gene expression profiling, because medical imaging like mammography, MRI, and PET have been widely used in Oman for assisting radiologists in early cancer diagnosis and microarray data have been proven to be a reliable source for differential diagnosis. We also discuss future cancer research directions and benefits to Oman economy for entering the cancer research and treatment business as it is a multi-billion dollar industry worldwide.

Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient.

PubMed

Yao, Jianchao; Chang, Chunqi; Salmi, Mari L; Hung, Yeung Sam; Loraine, Ann; Roux, Stanley J

2008-06-18

Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.

Fabrication of Carbohydrate Microarrays by Boronate Formation.

PubMed

Adak, Avijit K; Lin, Ting-Wei; Li, Ben-Yuan; Lin, Chun-Cheng

2017-01-01

The interactions between soluble carbohydrates and/or surface displayed glycans and protein receptors are essential to many biological processes and cellular recognition events. Carbohydrate microarrays provide opportunities for high-throughput quantitative analysis of carbohydrate-protein interactions. Over the past decade, various techniques have been implemented for immobilizing glycans on solid surfaces in a microarray format. Herein, we describe a detailed protocol for fabricating carbohydrate microarrays that capitalizes on the intrinsic reactivity of boronic acid toward carbohydrates to form stable boronate diesters. A large variety of unprotected carbohydrates ranging in structure from simple disaccharides and trisaccharides to considerably more complex human milk and blood group (oligo)saccharides have been covalently immobilized in a single step on glass slides, which were derivatized with high-affinity boronic acid ligands. The immobilized ligands in these microarrays maintain the receptor-binding activities including those of lectins and antibodies according to the structures of their pendant carbohydrates for rapid analysis of a number of carbohydrate-recognition events within 30 h. This method facilitates the direct construction of otherwise difficult to obtain carbohydrate microarrays from underivatized glycans.

The Glycan Microarray Story from Construction to Applications.

PubMed

Hyun, Ji Young; Pai, Jaeyoung; Shin, Injae

2017-04-18

Not only are glycan-mediated binding processes in cells and organisms essential for a wide range of physiological processes, but they are also implicated in various pathological processes. As a result, elucidation of glycan-associated biomolecular interactions and their consequences is of great importance in basic biological research and biomedical applications. In 2002, we and others were the first to utilize glycan microarrays in efforts aimed at the rapid analysis of glycan-associated recognition events. Because they contain a number of glycans immobilized in a dense and orderly manner on a solid surface, glycan microarrays enable multiple parallel analyses of glycan-protein binding events while utilizing only small amounts of glycan samples. Therefore, this microarray technology has become a leading edge tool in studies aimed at elucidating roles played by glycans and glycan binding proteins in biological systems. In this Account, we summarize our efforts on the construction of glycan microarrays and their applications in studies of glycan-associated interactions. Immobilization strategies of functionalized and unmodified glycans on derivatized glass surfaces are described. Although others have developed immobilization techniques, our efforts have focused on improving the efficiencies and operational simplicity of microarray construction. The microarray-based technology has been most extensively used for rapid analysis of the glycan binding properties of proteins. In addition, glycan microarrays have been employed to determine glycan-protein interactions quantitatively, detect pathogens, and rapidly assess substrate specificities of carbohydrate-processing enzymes. More recently, the microarrays have been employed to identify functional glycans that elicit cell surface lectin-mediated cellular responses. Owing to these efforts, it is now possible to use glycan microarrays to expand the understanding of roles played by glycans and glycan binding proteins in biological systems.

Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays

PubMed Central

2011-01-01

Introduction The human epidermal growth factor receptor 2 (HER2) is an established biomarker for management of patients with breast cancer. While conventional testing of HER2 protein expression is based on semi-quantitative visual scoring of the immunohistochemistry (IHC) result, efforts to reduce inter-observer variation and to produce continuous estimates of the IHC data are potentiated by digital image analysis technologies. Methods HER2 IHC was performed on the tissue microarrays (TMAs) of 195 patients with an early ductal carcinoma of the breast. Digital images of the IHC slides were obtained by Aperio ScanScope GL Slide Scanner. Membrane connectivity algorithm (HER2-CONNECT™, Visiopharm) was used for digital image analysis (DA). A pathologist evaluated the images on the screen twice (visual evaluations: VE1 and VE2). HER2 fluorescence in situ hybridization (FISH) was performed on the corresponding sections of the TMAs. The agreement between the IHC HER2 scores, obtained by VE1, VE2, and DA was tested for individual TMA spots and patient's maximum TMA spot values (VE1max, VE2max, DAmax). The latter were compared with the FISH data. Correlation of the continuous variable of the membrane connectivity estimate with the FISH data was tested. Results The pathologist intra-observer agreement (VE1 and VE2) on HER2 IHC score was almost perfect: kappa 0.91 (by spot) and 0.88 (by patient). The agreement between visual evaluation and digital image analysis was almost perfect at the spot level (kappa 0.86 and 0.87, with VE1 and VE2 respectively) and at the patient level (kappa 0.80 and 0.86, with VE1max and VE2max, respectively). The DA was more accurate than VE in detection of FISH-positive patients by recruiting 3 or 2 additional FISH-positive patients to the IHC score 2+ category from the IHC 0/1+ category by VE1max or VE2max, respectively. The DA continuous variable of the membrane connectivity correlated with the FISH data (HER2 and CEP17 copy numbers, and HER2/CEP17 ratio). Conclusion HER2 IHC digital image analysis based on membrane connectivity estimate was in almost perfect agreement with the visual evaluation of the pathologist and more accurate in detection of HER2 FISH-positive patients. Most immediate benefit of integrating the DA algorithm into the routine pathology HER2 testing may be obtained by alerting/reassuring pathologists of potentially misinterpreted IHC 0/1+ versus 2+ cases. PMID:21943197

Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays.

PubMed

Laurinaviciene, Aida; Dasevicius, Darius; Ostapenko, Valerijus; Jarmalaite, Sonata; Lazutka, Juozas; Laurinavicius, Arvydas

2011-09-23

The human epidermal growth factor receptor 2 (HER2) is an established biomarker for management of patients with breast cancer. While conventional testing of HER2 protein expression is based on semi-quantitative visual scoring of the immunohistochemistry (IHC) result, efforts to reduce inter-observer variation and to produce continuous estimates of the IHC data are potentiated by digital image analysis technologies. HER2 IHC was performed on the tissue microarrays (TMAs) of 195 patients with an early ductal carcinoma of the breast. Digital images of the IHC slides were obtained by Aperio ScanScope GL Slide Scanner. Membrane connectivity algorithm (HER2-CONNECT, Visiopharm) was used for digital image analysis (DA). A pathologist evaluated the images on the screen twice (visual evaluations: VE1 and VE2). HER2 fluorescence in situ hybridization (FISH) was performed on the corresponding sections of the TMAs. The agreement between the IHC HER2 scores, obtained by VE1, VE2, and DA was tested for individual TMA spots and patient's maximum TMA spot values (VE1max, VE2max, DAmax). The latter were compared with the FISH data. Correlation of the continuous variable of the membrane connectivity estimate with the FISH data was tested. The pathologist intra-observer agreement (VE1 and VE2) on HER2 IHC score was almost perfect: kappa 0.91 (by spot) and 0.88 (by patient). The agreement between visual evaluation and digital image analysis was almost perfect at the spot level (kappa 0.86 and 0.87, with VE1 and VE2 respectively) and at the patient level (kappa 0.80 and 0.86, with VE1max and VE2max, respectively). The DA was more accurate than VE in detection of FISH-positive patients by recruiting 3 or 2 additional FISH-positive patients to the IHC score 2+ category from the IHC 0/1+ category by VE1max or VE2max, respectively. The DA continuous variable of the membrane connectivity correlated with the FISH data (HER2 and CEP17 copy numbers, and HER2/CEP17 ratio). HER2 IHC digital image analysis based on membrane connectivity estimate was in almost perfect agreement with the visual evaluation of the pathologist and more accurate in detection of HER2 FISH-positive patients. Most immediate benefit of integrating the DA algorithm into the routine pathology HER2 testing may be obtained by alerting/reassuring pathologists of potentially misinterpreted IHC 0/1+ versus 2+ cases.

MAPPI-DAT: data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform.

PubMed

Gupta, Surya; De Puysseleyr, Veronic; Van der Heyden, José; Maddelein, Davy; Lemmens, Irma; Lievens, Sam; Degroeve, Sven; Tavernier, Jan; Martens, Lennart

2017-05-01

Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments. MAPPI-DAT is developed in Python, using R for data analysis and MySQL as data management system. MAPPI-DAT is cross-platform and can be ran on Microsoft Windows, Linux and OS X/macOS. The source code and a Microsoft Windows executable are freely available under the permissive Apache2 open source license at https://github.com/compomics/MAPPI-DAT. jan.tavernier@vib-ugent.be or lennart.martens@vib-ugent.be. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

TOXICOGENOMIC ANALYSIS INCORPORATING OPERON-TRANSCRIPTIONAL COUPLING AND TOXICANT CONCENTRATRION-EXPRESSION RESPONSE: Analysis of MX-Treated Salmonella

EPA Science Inventory

What is the study? This study is the first to use microarray analysis in the Ames strains of Salmonella. The microarray chips were custom-designed for this study and are not commercially available, and we evaluated the well-studied drinking water mutagen, MX. Because much inform...

Standardization efforts of digital pathology in Europe.

PubMed

Rojo, Marcial García; Daniel, Christel; Schrader, Thomas

2012-01-01

EURO-TELEPATH is a European COST Action IC0604. It started in 2007 and will end in November 2011. Its main objectives are evaluating and validating the common technological framework and communication standards required to access, transmit, and manage digital medical records by pathologists and other medical specialties in a networked environment. Working Group 1, "Business Modelling in Pathology," has designed main pathology processes - Frozen Study, Formalin Fixed Specimen Study, Telepathology, Cytology, and Autopsy - using Business Process Modelling Notation (BPMN). Working Group 2 has been dedicated to promoting the application of informatics standards in pathology, collaborating with Integrating Healthcare Enterprise (IHE), Digital Imaging and Communications in Medicine (DICOM), Health Level Seven (HL7), and other standardization bodies. Health terminology standardization research has become a topic of great interest. Future research work should focus on standardizing automatic image analysis and tissue microarrays imaging.

MICROARRAY ANALYSIS OF DICHLOROACETIC ACID-INDUCED CHANGES IN GENE EXPRESSION

EPA Science Inventory

MICROARRAY ANALYSIS OF DICHLOROACETIC ACID-INDUCED CHANGES IN GENE EXPRESSION

Dichloroacetic acid (DCA) is a major by-product of water disinfection by chlorination. Several studies have demonstrated the hepatocarcinogenicity of DCA in rodents when administered in dri...

Robust gene selection methods using weighting schemes for microarray data analysis.

PubMed

Kang, Suyeon; Song, Jongwoo

2017-09-02

A common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates. We have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays. The results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

A highly oriented hybrid microarray modified electrode fabricated by a template-free method for ultrasensitive electrochemical DNA recognition

NASA Astrophysics Data System (ADS)

Shi, Lei; Chu, Zhenyu; Dong, Xueliang; Jin, Wanqin; Dempsey, Eithne

2013-10-01

Highly oriented growth of a hybrid microarray was realized by a facile template-free method on gold substrates for the first time. The proposed formation mechanism involves an interfacial structure-directing force arising from self-assembled monolayers (SAMs) between gold substrates and hybrid crystals. Different SAMs and variable surface coverage of the assembled molecules play a critical role in the interfacial directing forces and influence the morphologies of hybrid films. A highly oriented hybrid microarray was formed on the highly aligned and vertical SAMs of 1,4-benzenedithiol molecules with rigid backbones, which afforded an intense structure-directing power for the oriented growth of hybrid crystals. Additionally, the density of the microarray could be adjusted by controlling the surface coverage of assembled molecules. Based on the hybrid microarray modified electrode with a large specific area (ca. 10 times its geometrical area), a label-free electrochemical DNA biosensor was constructed for the detection of an oligonucleotide fragment of the avian flu virus H5N1. The DNA biosensor displayed a significantly low detection limit of 5 pM (S/N = 3), a wide linear response from 10 pM to 10 nM, as well as excellent selectivity, good regeneration and high stability. We expect that the proposed template-free method can provide a new reference for the fabrication of a highly oriented hybrid array and the as-prepared microarray modified electrode will be a promising paradigm in constructing highly sensitive and selective biosensors.Highly oriented growth of a hybrid microarray was realized by a facile template-free method on gold substrates for the first time. The proposed formation mechanism involves an interfacial structure-directing force arising from self-assembled monolayers (SAMs) between gold substrates and hybrid crystals. Different SAMs and variable surface coverage of the assembled molecules play a critical role in the interfacial directing forces and influence the morphologies of hybrid films. A highly oriented hybrid microarray was formed on the highly aligned and vertical SAMs of 1,4-benzenedithiol molecules with rigid backbones, which afforded an intense structure-directing power for the oriented growth of hybrid crystals. Additionally, the density of the microarray could be adjusted by controlling the surface coverage of assembled molecules. Based on the hybrid microarray modified electrode with a large specific area (ca. 10 times its geometrical area), a label-free electrochemical DNA biosensor was constructed for the detection of an oligonucleotide fragment of the avian flu virus H5N1. The DNA biosensor displayed a significantly low detection limit of 5 pM (S/N = 3), a wide linear response from 10 pM to 10 nM, as well as excellent selectivity, good regeneration and high stability. We expect that the proposed template-free method can provide a new reference for the fabrication of a highly oriented hybrid array and the as-prepared microarray modified electrode will be a promising paradigm in constructing highly sensitive and selective biosensors. Electronic supplementary information (ESI) available: Four-probe method for determining the conductivity of the hybrid crystal (Fig. S1); stability comparisons of the hybrid films (Fig. S2); FESEM images of the hybrid microarray (Fig. S3); electrochemical characterizations of the hybrid films (Fig. S4); DFT simulations (Fig. S5); cross-sectional FESEM image of the hybrid microarray (Fig. S6); regeneration and stability tests of the DNA biosensor (Fig. S7). See DOI: 10.1039/c3nr03097k

The application of DNA microarrays in gene expression analysis.

PubMed

van Hal, N L; Vorst, O; van Houwelingen, A M; Kok, E J; Peijnenburg, A; Aharoni, A; van Tunen, A J; Keijer, J

2000-03-31

DNA microarray technology is a new and powerful technology that will substantially increase the speed of molecular biological research. This paper gives a survey of DNA microarray technology and its use in gene expression studies. The technical aspects and their potential improvements are discussed. These comprise array manufacturing and design, array hybridisation, scanning, and data handling. Furthermore, it is discussed how DNA microarrays can be applied in the working fields of: safety, functionality and health of food and gene discovery and pathway engineering in plants.

Implementation of mutual information and bayes theorem for classification microarray data

NASA Astrophysics Data System (ADS)

Dwifebri Purbolaksono, Mahendra; Widiastuti, Kurnia C.; Syahrul Mubarok, Mohamad; Adiwijaya; Aminy Ma’ruf, Firda

2018-03-01

Microarray Technology is one of technology which able to read the structure of gen. The analysis is important for this technology. It is for deciding which attribute is more important than the others. Microarray technology is able to get cancer information to diagnose a person’s gen. Preparation of microarray data is a huge problem and takes a long time. That is because microarray data contains high number of insignificant and irrelevant attributes. So, it needs a method to reduce the dimension of microarray data without eliminating important information in every attribute. This research uses Mutual Information to reduce dimension. System is built with Machine Learning approach specifically Bayes Theorem. This theorem uses a statistical and probability approach. By combining both methods, it will be powerful for Microarray Data Classification. The experiment results show that system is good to classify Microarray data with highest F1-score using Bayesian Network by 91.06%, and Naïve Bayes by 88.85%.

Microarray data mining using Bioconductor packages.

PubMed

Nie, Haisheng; Neerincx, Pieter B T; van der Poel, Jan; Ferrari, Francesco; Bicciato, Silvio; Leunissen, Jack A M; Groenen, Martien A M

2009-07-16

This paper describes the results of a Gene Ontology (GO) term enrichment analysis of chicken microarray data using the Bioconductor packages. By checking the enriched GO terms in three contrasts, MM8-PM8, MM8-MA8, and MM8-MM24, of the provided microarray data during this workshop, this analysis aimed to investigate the host reactions in chickens occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. The results of GO enrichment analysis using GO terms annotated to chicken genes and GO terms annotated to chicken-human orthologous genes were also compared. Furthermore, a locally adaptive statistical procedure (LAP) was performed to test differentially expressed chromosomal regions, rather than individual genes, in the chicken genome after Eimeria challenge. GO enrichment analysis identified significant (raw p-value < 0.05) GO terms for all three contrasts included in the analysis. Some of the GO terms linked to, generally, primary immune responses or secondary immune responses indicating the GO enrichment analysis is a useful approach to analyze microarray data. The comparisons of GO enrichment results using chicken gene information and chicken-human orthologous gene information showed more refined GO terms related to immune responses when using chicken-human orthologous gene information, this suggests that using chicken-human orthologous gene information has higher power to detect significant GO terms with more refined functionality. Furthermore, three chromosome regions were identified to be significantly up-regulated in contrast MM8-PM8 (q-value < 0.01). Overall, this paper describes a practical approach to analyze microarray data in farm animals where the genome information is still incomplete. For farm animals, such as chicken, with currently limited gene annotation, borrowing gene annotation information from orthologous genes in well-annotated species, such as human, will help improve the pathway analysis results substantially. Furthermore, LAP analysis approach is a relatively new and very useful way to be applied in microarray analysis.

Polyadenylation state microarray (PASTA) analysis.

PubMed

Beilharz, Traude H; Preiss, Thomas

2011-01-01

Nearly all eukaryotic mRNAs terminate in a poly(A) tail that serves important roles in mRNA utilization. In the cytoplasm, the poly(A) tail promotes both mRNA stability and translation, and these functions are frequently regulated through changes in tail length. To identify the scope of poly(A) tail length control in a transcriptome, we developed the polyadenylation state microarray (PASTA) method. It involves the purification of mRNA based on poly(A) tail length using thermal elution from poly(U) sepharose, followed by microarray analysis of the resulting fractions. In this chapter we detail our PASTA approach and describe some methods for bulk and mRNA-specific poly(A) tail length measurements of use to monitor the procedure and independently verify the microarray data.

The Use of Atomic Force Microscopy for 3D Analysis of Nucleic Acid Hybridization on Microarrays.

PubMed

Dubrovin, E V; Presnova, G V; Rubtsova, M Yu; Egorov, A M; Grigorenko, V G; Yaminsky, I V

2015-01-01

Oligonucleotide microarrays are considered today to be one of the most efficient methods of gene diagnostics. The capability of atomic force microscopy (AFM) to characterize the three-dimensional morphology of single molecules on a surface allows one to use it as an effective tool for the 3D analysis of a microarray for the detection of nucleic acids. The high resolution of AFM offers ways to decrease the detection threshold of target DNA and increase the signal-to-noise ratio. In this work, we suggest an approach to the evaluation of the results of hybridization of gold nanoparticle-labeled nucleic acids on silicon microarrays based on an AFM analysis of the surface both in air and in liquid which takes into account of their three-dimensional structure. We suggest a quantitative measure of the hybridization results which is based on the fraction of the surface area occupied by the nanoparticles.

The Utility of Chromosomal Microarray Analysis in Developmental and Behavioral Pediatrics

ERIC Educational Resources Information Center

Beaudet, Arthur L.

2013-01-01

Chromosomal microarray analysis (CMA) has emerged as a powerful new tool to identify genomic abnormalities associated with a wide range of developmental disabilities including congenital malformations, cognitive impairment, and behavioral abnormalities. CMA includes array comparative genomic hybridization (CGH) and single nucleotide polymorphism…

Evaluation of chronic lymphocytic leukemia by oligonucleotide-based microarray analysis uncovers novel aberrations not detected by FISH or cytogenetic analysis

PubMed Central

2011-01-01

Background Cytogenetic evaluation is a key component of the diagnosis and prognosis of chronic lymphocytic leukemia (CLL). We performed oligonucleotide-based comparative genomic hybridization microarray analysis on 34 samples with CLL and known abnormal karyotypes previously determined by cytogenetics and/or fluorescence in situ hybridization (FISH). Results Using a custom designed microarray that targets >1800 genes involved in hematologic disease and other malignancies, we identified additional cryptic aberrations and novel findings in 59% of cases. These included gains and losses of genes associated with cell cycle regulation, apoptosis and susceptibility loci on 3p21.31, 5q35.2q35.3, 10q23.31q23.33, 11q22.3, and 22q11.23. Conclusions Our results show that microarray analysis will detect known aberrations, including microscopic and cryptic alterations. In addition, novel genomic changes will be uncovered that may become important prognostic predictors or treatment targets for CLL in the future. PMID:22087757

New diagnostics for melanoma detection: from artificial intelligence to RNA microarrays.

PubMed

Ahlgrimm-Siess, Verena; Laimer, Martin; Arzberger, Edith; Hofmann-Wellenhof, Rainer

2012-07-01

Early detection of melanoma remains crucial to ensuring a favorable prognosis. Dermoscopy and total body photography are well-established noninvasive aids that increase the diagnostic accuracy of dermatologists in their daily routine, beyond that of a naked-eye examination. New noninvasive diagnostic techniques, such as reflectance confocal microscopy, multispectral digital imaging and RNA microarrays, are currently being investigated to determine their utility for melanoma detection. This review presents emerging technologies for noninvasive melanoma diagnosis, and discusses their advantages and limitations.

Interim report on updated microarray probes for the LLNL Burkholderia pseudomallei SNP array

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gardner, S; Jaing, C

2012-03-27

The overall goal of this project is to forensically characterize 100 unknown Burkholderia isolates in the US-Australia collaboration. We will identify genome-wide single nucleotide polymorphisms (SNPs) from B. pseudomallei and near neighbor species including B. mallei, B. thailandensis and B. oklahomensis. We will design microarray probes to detect these SNP markers and analyze 100 Burkholderia genomic DNAs extracted from environmental, clinical and near neighbor isolates from Australian collaborators on the Burkholderia SNP microarray. We will analyze the microarray genotyping results to characterize the genetic diversity of these new isolates and triage the samples for whole genome sequencing. In this interimmore » report, we described the SNP analysis and the microarray probe design for the Burkholderia SNP microarray.« less

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

PubMed

Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

2017-06-06

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

MGDB: crossing the marker genes of a user microarray with a database of public-microarrays marker genes.

PubMed

Huerta, Mario; Munyi, Marc; Expósito, David; Querol, Enric; Cedano, Juan

2014-06-15

The microarrays performed by scientific teams grow exponentially. These microarray data could be useful for researchers around the world, but unfortunately they are underused. To fully exploit these data, it is necessary (i) to extract these data from a repository of the high-throughput gene expression data like Gene Expression Omnibus (GEO) and (ii) to make the data from different microarrays comparable with tools easy to use for scientists. We have developed these two solutions in our server, implementing a database of microarray marker genes (Marker Genes Data Base). This database contains the marker genes of all GEO microarray datasets and it is updated monthly with the new microarrays from GEO. Thus, researchers can see whether the marker genes of their microarray are marker genes in other microarrays in the database, expanding the analysis of their microarray to the rest of the public microarrays. This solution helps not only to corroborate the conclusions regarding a researcher's microarray but also to identify the phenotype of different subsets of individuals under investigation, to frame the results with microarray experiments from other species, pathologies or tissues, to search for drugs that promote the transition between the studied phenotypes, to detect undesirable side effects of the treatment applied, etc. Thus, the researcher can quickly add relevant information to his/her studies from all of the previous analyses performed in other studies as long as they have been deposited in public repositories. Marker-gene database tool: http://ibb.uab.es/mgdb © The Author 2014. Published by Oxford University Press.

Serum biomarkers of Burkholderia mallei infection elucidated by proteomic imaging of skin and lung abscesses.

PubMed

Glaros, Trevor G; Blancett, Candace D; Bell, Todd M; Natesan, Mohan; Ulrich, Robert G

2015-01-01

The bacterium Burkholderia mallei is the etiological agent of glanders, a highly contagious, often fatal zoonotic infectious disease that is also a biodefense concern. Clinical laboratory assays that analyze blood or other biological fluids are the highest priority because these specimens can be collected with minimal risk to the patient. However, progress in developing sensitive assays for monitoring B. mallei infection is hampered by a shortage of useful biomarkers. Reasoning that there should be a strong correlation between the proteomes of infected tissues and circulating serum, we employed imaging mass spectrometry (IMS) of thin-sectioned tissues from Chlorocebus aethiops (African green) monkeys infected with B. mallei to localize host and pathogen proteins that were associated with abscesses. Using laser-capture microdissection of specific regions identified by IMS and histology within the tissue sections, a more extensive proteomic analysis was performed by a technique that combined the physical separation capabilities of liquid chromatography (LC) with the sensitive mass analysis capabilities of mass spectrometry (LC-MS/MS). By examining standard formalin-fixed, paraffin-embedded tissue sections, this strategy resulted in the identification of several proteins that were associated with lung and skin abscesses, including the host protein calprotectin and the pathogen protein GroEL. Elevated levels of calprotectin detected by ELISA and antibody responses to GroEL, measured by a microarray of the bacterial proteome, were subsequently detected in the sera of C. aethiops, Macaca mulatta, and Macaca fascicularis primates infected with B. mallei. Our results demonstrate that a combination of multidimensional MS analysis of traditional histology specimens with high-content protein microarrays can be used to discover lead pairs of host-pathogen biomarkers of infection that are identifiable in biological fluids.

Transcriptome analysis of salinity stress responses in common wheat using a 22k oligo-DNA microarray.

PubMed

Kawaura, Kanako; Mochida, Keiichi; Yamazaki, Yukiko; Ogihara, Yasunari

2006-04-01

In this study, we constructed a 22k wheat oligo-DNA microarray. A total of 148,676 expressed sequence tags of common wheat were collected from the database of the Wheat Genomics Consortium of Japan. These were grouped into 34,064 contigs, which were then used to design an oligonucleotide DNA microarray. Following a multistep selection of the sense strand, 21,939 60-mer oligo-DNA probes were selected for attachment on the microarray slide. This 22k oligo-DNA microarray was used to examine the transcriptional response of wheat to salt stress. More than 95% of the probes gave reproducible hybridization signals when targeted with RNAs extracted from salt-treated wheat shoots and roots. With the microarray, we identified 1,811 genes whose expressions changed more than 2-fold in response to salt. These included genes known to mediate response to salt, as well as unknown genes, and they were classified into 12 major groups by hierarchical clustering. These gene expression patterns were also confirmed by real-time reverse transcription-PCR. Many of the genes with unknown function were clustered together with genes known to be involved in response to salt stress. Thus, analysis of gene expression patterns combined with gene ontology should help identify the function of the unknown genes. Also, functional analysis of these wheat genes should provide new insight into the response to salt stress. Finally, these results indicate that the 22k oligo-DNA microarray is a reliable method for monitoring global gene expression patterns in wheat.

Identifying molecular features for prostate cancer with Gleason 7 based on microarray gene expression profiles.

PubMed

Bălăcescu, Loredana; Bălăcescu, O; Crişan, N; Fetica, B; Petruţ, B; Bungărdean, Cătălina; Rus, Meda; Tudoran, Oana; Meurice, G; Irimie, Al; Dragoş, N; Berindan-Neagoe, Ioana

2011-01-01

Prostate cancer represents the first leading cause of cancer among western male population, with different clinical behavior ranging from indolent to metastatic disease. Although many molecules and deregulated pathways are known, the molecular mechanisms involved in the development of prostate cancer are not fully understood. The aim of this study was to explore the molecular variation underlying the prostate cancer, based on microarray analysis and bioinformatics approaches. Normal and prostate cancer tissues were collected by macrodissection from prostatectomy pieces. All prostate cancer specimens used in our study were Gleason score 7. Gene expression microarray (Agilent Technologies) was used for Whole Human Genome evaluation. The bioinformatics and functional analysis were based on Limma and Ingenuity software. The microarray analysis identified 1119 differentially expressed genes between prostate cancer and normal prostate, which were up- or down-regulated at least 2-fold. P-values were adjusted for multiple testing using Benjamini-Hochberg method with a false discovery rate of 0.01. These genes were analyzed with Ingenuity Pathway Analysis software and were established 23 genetic networks. Our microarray results provide new information regarding the molecular networks in prostate cancer stratified as Gleason 7. These data highlighted gene expression profiles for better understanding of prostate cancer progression.

Porous Silicon Antibody Microarrays for Quantitative Analysis: Measurement of Free and Total PSA in Clinical Plasma Samples

PubMed Central

Tojo, Axel; Malm, Johan; Marko-Varga, György; Lilja, Hans; Laurell, Thomas

2014-01-01

The antibody microarrays have become widespread, but their use for quantitative analyses in clinical samples has not yet been established. We investigated an immunoassay based on nanoporous silicon antibody microarrays for quantification of total prostate-specific-antigen (PSA) in 80 clinical plasma samples, and provide quantitative data from a duplex microarray assay that simultaneously quantifies free and total PSA in plasma. To further develop the assay the porous silicon chips was placed into a standard 96-well microtiter plate for higher throughput analysis. The samples analyzed by this quantitative microarray were 80 plasma samples obtained from men undergoing clinical PSA testing (dynamic range: 0.14-44ng/ml, LOD: 0.14ng/ml). The second dataset, measuring free PSA (dynamic range: 0.40-74.9ng/ml, LOD: 0.47ng/ml) and total PSA (dynamic range: 0.87-295ng/ml, LOD: 0.76ng/ml), was also obtained from the clinical routine. The reference for the quantification was a commercially available assay, the ProStatus PSA Free/Total DELFIA. In an analysis of 80 plasma samples the microarray platform performs well across the range of total PSA levels. This assay might have the potential to substitute for the large-scale microtiter plate format in diagnostic applications. The duplex assay paves the way for a future quantitative multiplex assay, which analyses several prostate cancer biomarkers simultaneously. PMID:22921878

Systematic evaluation of RNA quality, microarray data reliability and pathway analysis in fresh, fresh frozen and formalin-fixed paraffin-embedded tissue samples.

PubMed

Wimmer, Isabella; Tröscher, Anna R; Brunner, Florian; Rubino, Stephen J; Bien, Christian G; Weiner, Howard L; Lassmann, Hans; Bauer, Jan

2018-04-20

Formalin-fixed paraffin-embedded (FFPE) tissues are valuable resources commonly used in pathology. However, formalin fixation modifies nucleic acids challenging the isolation of high-quality RNA for genetic profiling. Here, we assessed feasibility and reliability of microarray studies analysing transcriptome data from fresh, fresh-frozen (FF) and FFPE tissues. We show that reproducible microarray data can be generated from only 2 ng FFPE-derived RNA. For RNA quality assessment, fragment size distribution (DV200) and qPCR proved most suitable. During RNA isolation, extending tissue lysis time to 10 hours reduced high-molecular-weight species, while additional incubation at 70 °C markedly increased RNA yields. Since FF- and FFPE-derived microarrays constitute different data entities, we used indirect measures to investigate gene signal variation and relative gene expression. Whole-genome analyses revealed high concordance rates, while reviewing on single-genes basis showed higher data variation in FFPE than FF arrays. Using an experimental model, gene set enrichment analysis (GSEA) of FFPE-derived microarrays and fresh tissue-derived RNA-Seq datasets yielded similarly affected pathways confirming the applicability of FFPE tissue in global gene expression analysis. Our study provides a workflow comprising RNA isolation, quality assessment and microarray profiling using minimal RNA input, thus enabling hypothesis-generating pathway analyses from limited amounts of precious, pathologically significant FFPE tissues.

Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients

PubMed Central

2016-01-01

Abstract Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z -score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z -score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z -score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications. PMID:26504096

Derivation of Tissue-specific Functional Gene Sets to Aid Transcriptomic Analysis of Chemical Impacts on the Teleost Reproductive Axis.

EPA Science Inventory

Oligonucleotide microarrays are a powerful tool for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the poor power of microarray-based analyses to detect diffe...

A probabilistic framework for microarray data analysis: fundamental probability models and statistical inference.

PubMed

Ogunnaike, Babatunde A; Gelmi, Claudio A; Edwards, Jeremy S

2010-05-21

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Microarray-based screening of heat shock protein inhibitors.

PubMed

Schax, Emilia; Walter, Johanna-Gabriela; Märzhäuser, Helene; Stahl, Frank; Scheper, Thomas; Agard, David A; Eichner, Simone; Kirschning, Andreas; Zeilinger, Carsten

2014-06-20

Based on the importance of heat shock proteins (HSPs) in diseases such as cancer, Alzheimer's disease or malaria, inhibitors of these chaperons are needed. Today's state-of-the-art techniques to identify HSP inhibitors are performed in microplate format, requiring large amounts of proteins and potential inhibitors. In contrast, we have developed a miniaturized protein microarray-based assay to identify novel inhibitors, allowing analysis with 300 pmol of protein. The assay is based on competitive binding of fluorescence-labeled ATP and potential inhibitors to the ATP-binding site of HSP. Therefore, the developed microarray enables the parallel analysis of different ATP-binding proteins on a single microarray. We have demonstrated the possibility of multiplexing by immobilizing full-length human HSP90α and HtpG of Helicobacter pylori on microarrays. Fluorescence-labeled ATP was competed by novel geldanamycin/reblastatin derivatives with IC50 values in the range of 0.5 nM to 4 μM and Z(*)-factors between 0.60 and 0.96. Our results demonstrate the potential of a target-oriented multiplexed protein microarray to identify novel inhibitors for different members of the HSP90 family. Copyright © 2014 Elsevier B.V. All rights reserved.

Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data

PubMed Central

Watson, Christopher M.; Crinnion, Laura A.; Gurgel‐Gianetti, Juliana; Harrison, Sally M.; Daly, Catherine; Antanavicuite, Agne; Lascelles, Carolina; Markham, Alexander F.; Pena, Sergio D. J.; Bonthron, David T.

2015-01-01

ABSTRACT Autozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of exome sequencing. Although these methods have simplified the generation of experimental data, its analysis, particularly when disparate data types must be integrated, remains time consuming. Moreover, the huge volume of sequence variant data generated from next generation sequencing experiments opens up the possibility of using these data instead of microarray genotype data to identify disease loci. To allow these two types of data to be used in an integrated fashion, we have developed AgileVCFMapper, a program that performs both the mapping of disease loci by SNP genotyping and the analysis of potentially deleterious variants using exome sequence variant data, in a single step. This method does not require microarray SNP genotype data, although analysis with a combination of microarray and exome genotype data enables more precise delineation of disease loci, due to superior marker density and distribution. PMID:26037133

Quantitative characterization of conformational-specific protein-DNA binding using a dual-spectral interferometric imaging biosensor.

PubMed

Zhang, Xirui; Daaboul, George G; Spuhler, Philipp S; Dröge, Peter; Ünlü, M Selim

2016-03-14

DNA-binding proteins play crucial roles in the maintenance and functions of the genome and yet, their specific binding mechanisms are not fully understood. Recently, it was discovered that DNA-binding proteins recognize specific binding sites to carry out their functions through an indirect readout mechanism by recognizing and capturing DNA conformational flexibility and deformation. High-throughput DNA microarray-based methods that provide large-scale protein-DNA binding information have shown effective and comprehensive analysis of protein-DNA binding affinities, but do not provide information of DNA conformational changes in specific protein-DNA complexes. Building on the high-throughput capability of DNA microarrays, we demonstrate a quantitative approach that simultaneously measures the amount of protein binding to DNA and nanometer-scale DNA conformational change induced by protein binding in a microarray format. Both measurements rely on spectral interferometry on a layered substrate using a single optical instrument in two distinct modalities. In the first modality, we quantitate the amount of binding of protein to surface-immobilized DNA in each DNA spot using a label-free spectral reflectivity technique that accurately measures the surface densities of protein and DNA accumulated on the substrate. In the second modality, for each DNA spot, we simultaneously measure DNA conformational change using a fluorescence vertical sectioning technique that determines average axial height of fluorophores tagged to specific nucleotides of the surface-immobilized DNA. The approach presented in this paper, when combined with current high-throughput DNA microarray-based technologies, has the potential to serve as a rapid and simple method for quantitative and large-scale characterization of conformational specific protein-DNA interactions.

Thermodynamically optimal whole-genome tiling microarray design and validation.

PubMed

Cho, Hyejin; Chou, Hui-Hsien

2016-06-13

Microarray is an efficient apparatus to interrogate the whole transcriptome of species. Microarray can be designed according to annotated gene sets, but the resulted microarrays cannot be used to identify novel transcripts and this design method is not applicable to unannotated species. Alternatively, a whole-genome tiling microarray can be designed using only genomic sequences without gene annotations, and it can be used to detect novel RNA transcripts as well as known genes. The difficulty with tiling microarray design lies in the tradeoff between probe-specificity and coverage of the genome. Sequence comparison methods based on BLAST or similar software are commonly employed in microarray design, but they cannot precisely determine the subtle thermodynamic competition between probe targets and partially matched probe nontargets during hybridizations. Using the whole-genome thermodynamic analysis software PICKY to design tiling microarrays, we can achieve maximum whole-genome coverage allowable under the thermodynamic constraints of each target genome. The resulted tiling microarrays are thermodynamically optimal in the sense that all selected probes share the same melting temperature separation range between their targets and closest nontargets, and no additional probes can be added without violating the specificity of the microarray to the target genome. This new design method was used to create two whole-genome tiling microarrays for Escherichia coli MG1655 and Agrobacterium tumefaciens C58 and the experiment results validated the design.

PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments.

PubMed

Usadel, Björn; Nagel, Axel; Steinhauser, Dirk; Gibon, Yves; Bläsing, Oliver E; Redestig, Henning; Sreenivasulu, Nese; Krall, Leonard; Hannah, Matthew A; Poree, Fabien; Fernie, Alisdair R; Stitt, Mark

2006-12-18

Microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis. Here we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs.PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis.PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is freely available at http://mapman.mpimp-golm.mpg.de/pageman/. PageMan allows multiple microarray experiments to be efficiently condensed into a single page graphical display. The flexible interface allows data to be quickly and easily visualized, facilitating comparisons within experiments and to published experiments, thus enabling researchers to gain a rapid overview of the biological responses in the experiments.

Identification of candidate genes in osteoporosis by integrated microarray analysis.

PubMed

Li, J J; Wang, B Q; Fei, Q; Yang, Y; Li, D

2016-12-01

In order to screen the altered gene expression profile in peripheral blood mononuclear cells of patients with osteoporosis, we performed an integrated analysis of the online microarray studies of osteoporosis. We searched the Gene Expression Omnibus (GEO) database for microarray studies of peripheral blood mononuclear cells in patients with osteoporosis. Subsequently, we integrated gene expression data sets from multiple microarray studies to obtain differentially expressed genes (DEGs) between patients with osteoporosis and normal controls. Gene function analysis was performed to uncover the functions of identified DEGs. A total of three microarray studies were selected for integrated analysis. In all, 1125 genes were found to be significantly differentially expressed between osteoporosis patients and normal controls, with 373 upregulated and 752 downregulated genes. Positive regulation of the cellular amino metabolic process (gene ontology (GO): 0033240, false discovery rate (FDR) = 1.00E + 00) was significantly enriched under the GO category for biological processes, while for molecular functions, flavin adenine dinucleotide binding (GO: 0050660, FDR = 3.66E-01) and androgen receptor binding (GO: 0050681, FDR = 6.35E-01) were significantly enriched. DEGs were enriched in many osteoporosis-related signalling pathways, including those of mitogen-activated protein kinase (MAPK) and calcium. Protein-protein interaction (PPI) network analysis showed that the significant hub proteins contained ubiquitin specific peptidase 9, X-linked (Degree = 99), ubiquitin specific peptidase 19 (Degree = 57) and ubiquitin conjugating enzyme E2 B (Degree = 57). Analysis of gene function of identified differentially expressed genes may expand our understanding of fundamental mechanisms leading to osteoporosis. Moreover, significantly enriched pathways, such as MAPK and calcium, may involve in osteoporosis through osteoblastic differentiation and bone formation.Cite this article: J. J. Li, B. Q. Wang, Q. Fei, Y. Yang, D. Li. Identification of candidate genes in osteoporosis by integrated microarray analysis. Bone Joint Res 2016;5:594-601. DOI: 10.1302/2046-3758.512.BJR-2016-0073.R1. © 2016 Fei et al.

A metadata-aware application for remote scoring and exchange of tissue microarray images

PubMed Central

2013-01-01

Background The use of tissue microarrays (TMA) and advances in digital scanning microscopy has enabled the collection of thousands of tissue images. There is a need for software tools to annotate, query and share this data amongst researchers in different physical locations. Results We have developed an open source web-based application for remote scoring of TMA images, which exploits the value of Microsoft Silverlight Deep Zoom to provide a intuitive interface for zooming and panning around digital images. We use and extend existing XML-based standards to ensure that the data collected can be archived and that our system is interoperable with other standards-compliant systems. Conclusion The application has been used for multi-centre scoring of TMA slides composed of tissues from several Phase III breast cancer trials and ten different studies participating in the International Breast Cancer Association Consortium (BCAC). The system has enabled researchers to simultaneously score large collections of TMA and export the standardised data to integrate with pathological and clinical outcome data, thereby facilitating biomarker discovery. PMID:23635078

Workflows for microarray data processing in the Kepler environment.

PubMed

Stropp, Thomas; McPhillips, Timothy; Ludäscher, Bertram; Bieda, Mark

2012-05-17

Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis demands, including integration with new data sources. Bioinformatics pipelines are usually custom built for different applications, making them typically difficult to modify, extend and repurpose. Scientific workflow systems are intended to address these issues by providing general-purpose frameworks in which to develop and execute such pipelines. The Kepler workflow environment is a well-established system under continual development that is employed in several areas of scientific research. Kepler provides a flexible graphical interface, featuring clear display of parameter values, for design and modification of workflows. It has capabilities for developing novel computational components in the R, Python, and Java programming languages, all of which are widely used for bioinformatics algorithm development, along with capabilities for invoking external applications and using web services. We developed a series of fully functional bioinformatics pipelines addressing common tasks in microarray processing in the Kepler workflow environment. These pipelines consist of a set of tools for GFF file processing of NimbleGen chromatin immunoprecipitation on microarray (ChIP-chip) datasets and more comprehensive workflows for Affymetrix gene expression microarray bioinformatics and basic primer design for PCR experiments, which are often used to validate microarray results. Although functional in themselves, these workflows can be easily customized, extended, or repurposed to match the needs of specific projects and are designed to be a toolkit and starting point for specific applications. These workflows illustrate a workflow programming paradigm focusing on local resources (programs and data) and therefore are close to traditional shell scripting or R/BioConductor scripting approaches to pipeline design. Finally, we suggest that microarray data processing task workflows may provide a basis for future example-based comparison of different workflow systems. We provide a set of tools and complete workflows for microarray data analysis in the Kepler environment, which has the advantages of offering graphical, clear display of conceptual steps and parameters and the ability to easily integrate other resources such as remote data and web services.

Quantitative comparison and reproducibility of pathologist scoring and digital image analysis of estrogen receptor β2 immunohistochemistry in prostate cancer.

PubMed

Rizzardi, Anthony E; Zhang, Xiaotun; Vogel, Rachel Isaksson; Kolb, Suzanne; Geybels, Milan S; Leung, Yuet-Kin; Henriksen, Jonathan C; Ho, Shuk-Mei; Kwak, Julianna; Stanford, Janet L; Schmechel, Stephen C

2016-07-11

Digital image analysis offers advantages over traditional pathologist visual scoring of immunohistochemistry, although few studies examining the correlation and reproducibility of these methods have been performed in prostate cancer. We evaluated the correlation between digital image analysis (continuous variable data) and pathologist visual scoring (quasi-continuous variable data), reproducibility of each method, and association of digital image analysis methods with outcomes using prostate cancer tissue microarrays (TMAs) stained for estrogen receptor-β2 (ERβ2). Prostate cancer TMAs were digitized and evaluated by pathologist visual scoring versus digital image analysis for ERβ2 staining within tumor epithelium. Two independent analysis runs were performed to evaluate reproducibility. Image analysis data were evaluated for associations with recurrence-free survival and disease specific survival following radical prostatectomy. We observed weak/moderate Spearman correlation between digital image analysis and pathologist visual scores of tumor nuclei (Analysis Run A: 0.42, Analysis Run B: 0.41), and moderate/strong correlation between digital image analysis and pathologist visual scores of tumor cytoplasm (Analysis Run A: 0.70, Analysis Run B: 0.69). For the reproducibility analysis, there was high Spearman correlation between pathologist visual scores generated for individual TMA spots across Analysis Runs A and B (Nuclei: 0.84, Cytoplasm: 0.83), and very high correlation between digital image analysis for individual TMA spots across Analysis Runs A and B (Nuclei: 0.99, Cytoplasm: 0.99). Further, ERβ2 staining was significantly associated with increased risk of prostate cancer-specific mortality (PCSM) when quantified by cytoplasmic digital image analysis (HR 2.16, 95 % CI 1.02-4.57, p = 0.045), nuclear image analysis (HR 2.67, 95 % CI 1.20-5.96, p = 0.016), and total malignant epithelial area analysis (HR 5.10, 95 % CI 1.70-15.34, p = 0.004). After adjusting for clinicopathologic factors, only total malignant epithelial area ERβ2 staining was significantly associated with PCSM (HR 4.08, 95 % CI 1.37-12.15, p = 0.012). Digital methods of immunohistochemical quantification are more reproducible than pathologist visual scoring in prostate cancer, suggesting that digital methods are preferable and especially warranted for studies involving large sample sizes.

A new functional membrane protein microarray based on tethered phospholipid bilayers.

PubMed

Chadli, Meriem; Maniti, Ofelia; Marquette, Christophe; Tillier, Bruno; Cortès, Sandra; Girard-Egrot, Agnès

2018-04-30

A new prototype of a membrane protein biochip is presented in this article. This biochip was created by the combination of novel technologies of peptide-tethered bilayer lipid membrane (pep-tBLM) formation and solid support micropatterning. Pep-tBLMs integrating a membrane protein were obtained in the form of microarrays on a gold chip. The formation of the microspots was visualized in real-time by surface plasmon resonance imaging (SPRi) and the functionality of a GPCR (CXCR4), reinserted locally into microwells, was assessed by ligand binding studies. In brief, to achieve micropatterning, P19-4H, a 4 histidine-possessing peptide spacer, was spotted inside microwells obtained on polystyrene-coated gold, and Ni-chelating proteoliposomes were injected into the reaction chamber. Proteoliposome binding to the peptide was based on metal-chelate interaction. The peptide-tethered lipid bilayer was finally obtained by addition of a fusogenic peptide (AH peptide) to promote proteoliposome fusion. The CXCR4 pep-tBLM microarray was characterized by surface plasmon resonance imaging (SPRi) throughout the building-up process. This new generation of membrane protein biochip represents a promising method of developing a screening tool for drug discovery.

CNV-ROC: A cost effective, computer-aided analytical performance evaluator of chromosomal microarrays

PubMed Central

Goodman, Corey W.; Major, Heather J.; Walls, William D.; Sheffield, Val C.; Casavant, Thomas L.; Darbro, Benjamin W.

2016-01-01

Chromosomal microarrays (CMAs) are routinely used in both research and clinical laboratories; yet, little attention has been given to the estimation of genome-wide true and false negatives during the assessment of these assays and how such information could be used to calibrate various algorithmic metrics to improve performance. Low-throughput, locus-specific methods such as fluorescence in situ hybridization (FISH), quantitative PCR (qPCR), or multiplex ligation-dependent probe amplification (MLPA) preclude rigorous calibration of various metrics used by copy number variant (CNV) detection algorithms. To aid this task, we have established a comparative methodology, CNV-ROC, which is capable of performing a high throughput, low cost, analysis of CMAs that takes into consideration genome-wide true and false negatives. CNV-ROC uses a higher resolution microarray to confirm calls from a lower resolution microarray and provides for a true measure of genome-wide performance metrics at the resolution offered by microarray testing. CNV-ROC also provides for a very precise comparison of CNV calls between two microarray platforms without the need to establish an arbitrary degree of overlap. Comparison of CNVs across microarrays is done on a per-probe basis and receiver operator characteristic (ROC) analysis is used to calibrate algorithmic metrics, such as log2 ratio threshold, to enhance CNV calling performance. CNV-ROC addresses a critical and consistently overlooked aspect of analytical assessments of genome-wide techniques like CMAs which is the measurement and use of genome-wide true and false negative data for the calculation of performance metrics and comparison of CNV profiles between different microarray experiments. PMID:25595567

MICROARRAY DATA ANALYSIS USING MULTIPLE STATISTICAL MODELS

EPA Science Inventory

Microarray Data Analysis Using Multiple Statistical Models

Wenjun Bao1, Judith E. Schmid1, Amber K. Goetz1, Ming Ouyang2, William J. Welsh2,Andrew I. Brooks3,4, ChiYi Chu3,Mitsunori Ogihara3,4, Yinhe Cheng5, David J. Dix1. 1National Health and Environmental Effects Researc...

Parents' Perceptions of the Usefulness of Chromosomal Microarray Analysis for Children with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Reiff, Marian; Giarelli, Ellen; Bernhardt, Barbara A.; Easley, Ebony; Spinner, Nancy B.; Sankar, Pamela L.; Mulchandani, Surabhi

2015-01-01

Clinical guidelines recommend chromosomal microarray analysis (CMA) for all children with autism spectrum disorders (ASDs). We explored the test's perceived usefulness among parents of children with ASD who had undergone CMA, and received a result categorized as pathogenic, variant of uncertain significance, or negative. Fifty-seven parents…

A Graphical Systems Model and Tissue-specific Functional Gene Sets to Aid Transcriptomic Analysis of Chemical Impacts on the Female Teleost Reproductive Axis

EPA Science Inventory

Oligonucleotide microarrays and other ‘omics’ approaches are powerful tools for unsupervised analysis of chemical impacts on biological systems. However, the lack of well annotated biological pathways for many aquatic organisms, including fish, and the poor power of microarray-b...

CGO: utilizing and integrating gene expression microarray data in clinical research and data management.

PubMed

Bumm, Klaus; Zheng, Mingzhong; Bailey, Clyde; Zhan, Fenghuang; Chiriva-Internati, M; Eddlemon, Paul; Terry, Julian; Barlogie, Bart; Shaughnessy, John D

2002-02-01

Clinical GeneOrganizer (CGO) is a novel windows-based archiving, organization and data mining software for the integration of gene expression profiling in clinical medicine. The program implements various user-friendly tools and extracts data for further statistical analysis. This software was written for Affymetrix GeneChip *.txt files, but can also be used for any other microarray-derived data. The MS-SQL server version acts as a data mart and links microarray data with clinical parameters of any other existing database and therefore represents a valuable tool for combining gene expression analysis and clinical disease characteristics.

cluML: A markup language for clustering and cluster validity assessment of microarray data.

PubMed

Bolshakova, Nadia; Cunningham, Pádraig

2005-01-01

cluML is a new markup language for microarray data clustering and cluster validity assessment. The XML-based format has been designed to address some of the limitations observed in traditional formats, such as inability to store multiple clustering (including biclustering) and validation results within a dataset. cluML is an effective tool to support biomedical knowledge representation in gene expression data analysis. Although cluML was developed for DNA microarray analysis applications, it can be effectively used for the representation of clustering and for the validation of other biomedical and physical data that has no limitations.

Towards MRI microarrays.

PubMed

Hall, Andrew; Mundell, Victoria J; Blanco-Andujar, Cristina; Bencsik, Martin; McHale, Glen; Newton, Michael I; Cave, Gareth W V

2010-04-14

Superparamagnetic iron oxide nanometre scale particles have been utilised as contrast agents to image staked target binding oligonucleotide arrays using MRI to correlate the signal intensity and T(2)* relaxation times in different NMR fluids.

Analysis of developmental gene conservation in the Actinomycetales using DNA/DNA microarray comparisons.

PubMed

Kirby, Ralph; Herron, Paul; Hoskisson, Paul

2011-02-01

Based on available genome sequences, Actinomycetales show significant gene synteny across a wide range of species and genera. In addition, many genera show varying degrees of complex morphological development. Using the presence of gene synteny as a basis, it is clear that an analysis of gene conservation across the Streptomyces and various other Actinomycetales will provide information on both the importance of genes and gene clusters and the evolution of morphogenesis in these bacteria. Genome sequencing, although becoming cheaper, is still relatively expensive for comparing large numbers of strains. Thus, a heterologous DNA/DNA microarray hybridization dataset based on a Streptomyces coelicolor microarray allows a cheaper and greater depth of analysis of gene conservation. This study, using both bioinformatical and microarray approaches, was able to classify genes previously identified as involved in morphogenesis in Streptomyces into various subgroups in terms of conservation across species and genera. This will allow the targeting of genes for further study based on their importance at the species level and at higher evolutionary levels.

Cross species analysis of microarray expression data

PubMed Central

Lu, Yong; Huggins, Peter; Bar-Joseph, Ziv

2009-01-01

Motivation: Many biological systems operate in a similar manner across a large number of species or conditions. Cross-species analysis of sequence and interaction data is often applied to determine the function of new genes. In contrast to these static measurements, microarrays measure the dynamic, condition-specific response of complex biological systems. The recent exponential growth in microarray expression datasets allows researchers to combine expression experiments from multiple species to identify genes that are not only conserved in sequence but also operated in a similar way in the different species studied. Results: In this review we discuss the computational and technical challenges associated with these studies, the approaches that have been developed to address these challenges and the advantages of cross-species analysis of microarray data. We show how successful application of these methods lead to insights that cannot be obtained when analyzing data from a single species. We also highlight current open problems and discuss possible ways to address them. Contact: zivbj@cs.cmu.edu PMID:19357096

Separation and Analysis of Adherent and Non-Adherent Cancer Cells Using a Single-Cell Microarray Chip.

PubMed

Yamamura, Shohei; Yamada, Eriko; Kimura, Fukiko; Miyajima, Kumiko; Shigeto, Hajime

2017-10-21

A new single-cell microarray chip was designed and developed to separate and analyze single adherent and non-adherent cancer cells. The single-cell microarray chip is made of polystyrene with over 60,000 microchambers of 10 different size patterns (31-40 µm upper diameter, 11-20 µm lower diameter). A drop of suspension of adherent carcinoma (NCI-H1650) and non-adherent leukocyte (CCRF-CEM) cells was placed onto the chip, and single-cell occupancy of NCI-H1650 and CCRF-CEM was determined to be 79% and 84%, respectively. This was achieved by controlling the chip design and surface treatment. Analysis of protein expression in single NCI-H1650 and CCRF-CEM cells was performed on the single-cell microarray chip by multi-antibody staining. Additionally, with this system, we retrieved positive single cells from the microchambers by a micromanipulator. Thus, this system demonstrates the potential for easy and accurate separation and analysis of various types of single cells.

A common-path phase-shift interferometry surface plasmon imaging system

NASA Astrophysics Data System (ADS)

Su, Y.-T.; Chen, Shean-Jen; Yeh, T.-L.

2005-03-01

A biosensing imaging system is proposed based on the integration of surface plasmon resonance (SPR) and common-path phase-shift interferometry (PSI) techniques to measure the two-dimensional spatial phase variation caused by biomolecular interactions upon a sensing chip. The SPR phase imaging system can offer high resolution and high-throughout screening capabilities to analyze microarray biomolecular interaction without the need for additional labeling. With the long-term stability advantage of the common-path PSI technique even with external disturbances such as mechanical vibration, buffer flow noise, and laser unstable issue, the system can match the demand of real-time kinetic study for biomolecular interaction analysis (BIA). The SPR-PSI imaging system has achieved a detection limit of 2×10-7 refraction index change, a long-term phase stability of 2.5x10-4π rms over four hours, and a spatial phase resolution of 10-3 π with a lateral resolution of 100μm.

Cambridge Healthtech Institute's Third Annual Conference on Lab-on-a-Chip and Microarrays. 22-24 January 2001, Zurich, Switzerland.

PubMed

Jain, K K

2001-02-01

Cambridge Healthtech Institute's Third Annual Conference on Lab-on-a-Chip and Microarray technology covered the latest advances in this technology and applications in life sciences. Highlights of the meetings are reported briefly with emphasis on applications in genomics, drug discovery and molecular diagnostics. There was an emphasis on microfluidics because of the wide applications in laboratory and drug discovery. The lab-on-a-chip provides the facilities of a complete laboratory in a hand-held miniature device. Several microarray systems have been used for hybridisation and detection techniques. Oligonucleotide scanning arrays provide a versatile tool for the analysis of nucleic acid interactions and provide a platform for improving the array-based methods for investigation of antisense therapeutics. A method for analysing combinatorial DNA arrays using oligonucleotide-modified gold nanoparticle probes and a conventional scanner has considerable potential in molecular diagnostics. Various applications of microarray technology for high-throughput screening in drug discovery and single nucleotide polymorphisms (SNP) analysis were discussed. Protein chips have important applications in proteomics. With the considerable amount of data generated by the different technologies using microarrays, it is obvious that the reading of the information and its interpretation and management through the use of bioinformatics is essential. Various techniques for data analysis were presented. Biochip and microarray technology has an essential role to play in the evolving trends in healthcare, which integrate diagnosis with prevention/treatment and emphasise personalised medicines.

Differential gene expression detection and sample classification using penalized linear regression models.

PubMed

Wu, Baolin

2006-02-15

Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p > n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the 'large p small n' is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the 'nearest shrunken centroid' proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies. Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the (1) penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the (1) penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.

Probabilistic segmentation and intensity estimation for microarray images.

PubMed

Gottardo, Raphael; Besag, Julian; Stephens, Matthew; Murua, Alejandro

2006-01-01

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods, including relatively common 'doughnut-shaped' spots; (b) models the image directly as background plus hybridization intensity, and estimates the two quantities simultaneously, avoiding the common logical error that estimates of foreground may be less than those of the corresponding background if the two are estimated separately; and (c) uses a probabilistic modeling approach to simultaneously perform segmentation and intensity estimation, and to compute spot quality measures. We describe two approaches to parameter estimation: a fast algorithm, based on the expectation-maximization and the iterated conditional modes algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to offer some advantages over other methods. We use an HIV experiment to compare our approach to two commercial software products: Spot and Arrayvision.

Best practices for hybridization design in two-colour microarray analysis.

PubMed

Knapen, Dries; Vergauwen, Lucia; Laukens, Kris; Blust, Ronny

2009-07-01

Two-colour microarrays are a popular platform of choice in gene expression studies. Because two different samples are hybridized on a single microarray, and several microarrays are usually needed in a given experiment, there are many possible ways to combine samples on different microarrays. The actual combination employed is commonly referred to as the 'hybridization design'. Different types of hybridization designs have been developed, all aimed at optimizing the experimental setup for the detection of differentially expressed genes while coping with technical noise. Here, we first provide an overview of the different classes of hybridization designs, discussing their advantages and limitations, and then we illustrate the current trends in the use of different hybridization design types in contemporary research.

[Diagnosis of a case with Williams-Beuren syndrome with nephrocalcinosis using chromosome microarray analysis].

PubMed

Jin, S J; Liu, M; Long, W J; Luo, X P

2016-12-02

Objective: To explore the clinical phenotypes and the genetic cause for a boy with unexplained growth retardation, nephrocalcinosis, auditory anomalies and multi-organ/system developmental disorders. Method: Routine G-banding and chromosome microarray analysis were applied to a child with unexplained growth retardation, nephrocalcinosis, auditory anomalies and multi-organ/system developmental disorders treated in the Department of Pediatrics of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology in September 2015 and his parents to conduct the chromosomal karyotype analysis and the whole genome scanning. Deleted genes were searched in the Decipher and NCBI databases, and their relationships with the clinical phenotypes were analyzed. Result: A six-month-old boy was refered to us because of unexplained growth retardation and feeding intolerance.The affected child presented with abnormal manifestation such as special face, umbilical hernia, growth retardation, hypothyroidism, congenital heart disease, right ear sensorineural deafness, hypercalcemia and nephrocalcinosis. The child's karyotype was 46, XY, 16qh + , and his parents' karyotypes were normal. Chromosome microarray analysis revealed a 1 436 kb deletion on the 7q11.23(72701098_74136633) region of the child. This region included 23 protein-coding genes, which were reported to be corresponding to Williams-Beuren syndrome and its certain clinical phenotypes. His parents' results of chromosome microarray analysis were normal. Conclusion: A boy with characteristic manifestation of Williams-Beuren syndrome and rare nephrocalcinosis was diagnosed using chromosome microarray analysis. The deletion on the 7q11.23 might be related to the clinical phenotypes of Williams-Beuren syndrome, yet further studies are needed.

AFM 4.0: a toolbox for DNA microarray analysis

PubMed Central

Breitkreutz, Bobby-Joe; Jorgensen, Paul; Breitkreutz, Ashton; Tyers, Mike

2001-01-01

We have developed a series of programs, collectively packaged as Array File Maker 4.0 (AFM), that manipulate and manage DNA microarray data. AFM 4.0 is simple to use, applicable to any organism or microarray, and operates within the familiar confines of Microsoft Excel. Given a database of expression ratios, AFM 4.0 generates input files for clustering, helps prepare colored figures and Venn diagrams, and can uncover aneuploidy in yeast microarray data. AFM 4.0 should be especially useful to laboratories that do not have access to specialized commercial or in-house software. PMID:11532221

APPLICATION OF CDNA MICROARRAY TECHNOLOGY TO IN VITRO TOXICOLOGY AND THE SELECTION OF GENES FOR A REAL TIME RT-PCR-BASED SCREEN FOR OXIDATIVE STRESS IN HEP-G2 CELLS

EPA Science Inventory

Large-scale analysis of gene expression using cDNA microarrays promises the
rapid detection of the mode of toxicity for drugs and other chemicals. cDNA
microarrays were used to examine chemically-induced alterations of gene
expression in HepG2 cells exposed to oxidative ...

Where statistics and molecular microarray experiments biology meet.

PubMed

Kelmansky, Diana M

2013-01-01

This review chapter presents a statistical point of view to microarray experiments with the purpose of understanding the apparent contradictions that often appear in relation to their results. We give a brief introduction of molecular biology for nonspecialists. We describe microarray experiments from their construction and the biological principles the experiments rely on, to data acquisition and analysis. The role of epidemiological approaches and sample size considerations are also discussed.

Self-directed student research through analysis of microarray datasets: a computer-based functional genomics practical class for masters-level students.

PubMed

Grenville-Briggs, Laura J; Stansfield, Ian

2011-01-01

This report describes a linked series of Masters-level computer practical workshops. They comprise an advanced functional genomics investigation, based upon analysis of a microarray dataset probing yeast DNA damage responses. The workshops require the students to analyse highly complex transcriptomics datasets, and were designed to stimulate active learning through experience of current research methods in bioinformatics and functional genomics. They seek to closely mimic a realistic research environment, and require the students first to propose research hypotheses, then test those hypotheses using specific sections of the microarray dataset. The complexity of the microarray data provides students with the freedom to propose their own unique hypotheses, tested using appropriate sections of the microarray data. This research latitude was highly regarded by students and is a strength of this practical. In addition, the focus on DNA damage by radiation and mutagenic chemicals allows them to place their results in a human medical context, and successfully sparks broad interest in the subject material. In evaluation, 79% of students scored the practical workshops on a five-point scale as 4 or 5 (totally effective) for student learning. More broadly, the general use of microarray data as a "student research playground" is also discussed. Copyright © 2011 Wiley Periodicals, Inc.

Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments

PubMed Central

Brodsky, Leonid; Leontovich, Andrei; Shtutman, Michael; Feinstein, Elena

2004-01-01

Mathematical methods of analysis of microarray hybridizations deal with gene expression profiles as elementary units. However, some of these profiles do not reflect a biologically relevant transcriptional response, but rather stem from technical artifacts. Here, we describe two technically independent but rationally interconnected methods for identification of such artifactual profiles. Our diagnostics are based on detection of deviations from uniformity, which is assumed as the main underlying principle of microarray design. Method 1 is based on detection of non-uniformity of microarray distribution of printed genes that are clustered based on the similarity of their expression profiles. Method 2 is based on evaluation of the presence of gene-specific microarray spots within the slides’ areas characterized by an abnormal concentration of low/high differential expression values, which we define as ‘patterns of differentials’. Applying two novel algorithms, for nested clustering (method 1) and for pattern detection (method 2), we can make a dual estimation of the profile’s quality for almost every printed gene. Genes with artifactual profiles detected by method 1 may then be removed from further analysis. Suspicious differential expression values detected by method 2 may be either removed or weighted according to the probabilities of patterns that cover them, thus diminishing their input in any further data analysis. PMID:14999086

Novel calibration tools and validation concepts for microarray-based platforms used in molecular diagnostics and food safety control.

PubMed

Brunner, C; Hoffmann, K; Thiele, T; Schedler, U; Jehle, H; Resch-Genger, U

2015-04-01

Commercial platforms consisting of ready-to-use microarrays printed with target-specific DNA probes, a microarray scanner, and software for data analysis are available for different applications in medical diagnostics and food analysis, detecting, e.g., viral and bacteriological DNA sequences. The transfer of these tools from basic research to routine analysis, their broad acceptance in regulated areas, and their use in medical practice requires suitable calibration tools for regular control of instrument performance in addition to internal assay controls. Here, we present the development of a novel assay-adapted calibration slide for a commercialized DNA-based assay platform, consisting of precisely arranged fluorescent areas of various intensities obtained by incorporating different concentrations of a "green" dye and a "red" dye in a polymer matrix. These dyes present "Cy3" and "Cy5" analogues with improved photostability, chosen based upon their spectroscopic properties closely matching those of common labels for the green and red channel of microarray scanners. This simple tool allows to efficiently and regularly assess and control the performance of the microarray scanner provided with the biochip platform and to compare different scanners. It will be eventually used as fluorescence intensity scale for referencing of assays results and to enhance the overall comparability of diagnostic tests.

Analysis of host response to bacterial infection using error model based gene expression microarray experiments

PubMed Central

Stekel, Dov J.; Sarti, Donatella; Trevino, Victor; Zhang, Lihong; Salmon, Mike; Buckley, Chris D.; Stevens, Mark; Pallen, Mark J.; Penn, Charles; Falciani, Francesco

2005-01-01

A key step in the analysis of microarray data is the selection of genes that are differentially expressed. Ideally, such experiments should be properly replicated in order to infer both technical and biological variability, and the data should be subjected to rigorous hypothesis tests to identify the differentially expressed genes. However, in microarray experiments involving the analysis of very large numbers of biological samples, replication is not always practical. Therefore, there is a need for a method to select differentially expressed genes in a rational way from insufficiently replicated data. In this paper, we describe a simple method that uses bootstrapping to generate an error model from a replicated pilot study that can be used to identify differentially expressed genes in subsequent large-scale studies on the same platform, but in which there may be no replicated arrays. The method builds a stratified error model that includes array-to-array variability, feature-to-feature variability and the dependence of error on signal intensity. We apply this model to the characterization of the host response in a model of bacterial infection of human intestinal epithelial cells. We demonstrate the effectiveness of error model based microarray experiments and propose this as a general strategy for a microarray-based screening of large collections of biological samples. PMID:15800204

AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays

PubMed Central

Hu, Guohong; Wang, Hui-Yun; Greenawalt, Danielle M.; Azaro, Marco A.; Luo, Minjie; Tereshchenko, Irina V.; Cui, Xiangfeng; Yang, Qifeng; Gao, Richeng; Shen, Li; Li, Honghua

2006-01-01

Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of ∼160 000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300 000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from . PMID:16982644

The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

PubMed Central

Sioson, Allan A; Mane, Shrinivasrao P; Li, Pinghua; Sha, Wei; Heath, Lenwood S; Bohnert, Hans J; Grene, Ruth

2006-01-01

Background Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data. Results The Expresso analysis using MIV data consistently identifies more genes as differentially expressed, when compared to Expresso analysis with IIV data. The typical TM4 normalization and filtering pipeline corrects systematic intensity-specific bias on a per microarray basis. Subsequent statistical analysis with Expresso or a TM4 t-test can effectively identify differentially expressed genes. The best agreement with qRT-PCR data is obtained through the use of Expresso analysis and MIV data. Conclusion The results of this research are of practical value to biologists who analyze microarray data sets. The TM4 normalization and filtering pipeline corrects microarray-specific systematic bias and complements the normalization stage in Expresso analysis. The results of Expresso using MIV data have the best agreement with qRT-PCR results. In one experiment, MIV is a better choice than IIV as input to data normalization and statistical analysis methods, as it yields as greater number of statistically significant differentially expressed genes; TM4 does not support the choice of MIV input data. Overall, the more flexible and extensive statistical models of Expresso achieve more accurate analytical results, when judged by the yardstick of qRT-PCR data, in the context of an experimental design of modest complexity. PMID:16626497

Diagnostic Yield of Chromosomal Microarray Analysis in an Autism Primary Care Practice: Which Guidelines to Implement?

ERIC Educational Resources Information Center

McGrew, Susan G.; Peters, Brittany R.; Crittendon, Julie A.; Veenstra-VanderWeele, Jeremy

2012-01-01

Genetic testing is recommended for patients with ASD; however specific recommendations vary by specialty. American Academy of Pediatrics and American Academy of Neurology guidelines recommend G-banded karyotype and Fragile X DNA. The American College of Medical Genetics recommends Chromosomal Microarray Analysis (CMA). We determined the yield of…

Self-Directed Student Research through Analysis of Microarray Datasets: A Computer-Based Functional Genomics Practical Class for Masters-Level Students

ERIC Educational Resources Information Center

Grenville-Briggs, Laura J.; Stansfield, Ian

2011-01-01

This report describes a linked series of Masters-level computer practical workshops. They comprise an advanced functional genomics investigation, based upon analysis of a microarray dataset probing yeast DNA damage responses. The workshops require the students to analyse highly complex transcriptomics datasets, and were designed to stimulate…

A MICROARRAY ANALYSIS OF GENE EXPRESSION IN THE EMBRYONIC FORELIMB OF THE C57BL/6J MOUSE REVEALS SIGNIFICANT ALTERATIONS METABOLIC AND DEVELOPMENTAL REGULATION FOLLOWING ETHANOL EXPOSURE.

EPA Science Inventory

The observation of transcriptional changes following embryonic ethanol exposure may provide significant insights into the biological response to ethanol exposure. In this study, we used microarray analysis to examine the transcriptional response of the developing limb to a dose ...

Pathway modeling of microarray data: A case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ovacik, Meric A.; Sen, Banalata; Euling, Susan Y.

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significancemore » analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.« less

Quantitative Time-Resolved Fluorescence Imaging of Androgen Receptor and Prostate-Specific Antigen in Prostate Tissue Sections.

PubMed

Krzyzanowska, Agnieszka; Lippolis, Giuseppe; Helczynski, Leszek; Anand, Aseem; Peltola, Mari; Pettersson, Kim; Lilja, Hans; Bjartell, Anders

2016-05-01

Androgen receptor (AR) and prostate-specific antigen (PSA) are expressed in the prostate and are involved in prostate cancer (PCa). The aim of this study was to develop reliable protocols for reproducible quantification of AR and PSA in benign and malignant prostate tissue using time-resolved fluorescence (TRF) imaging techniques. AR and PSA were detected with TRF in tissue microarrays from 91 PCa patients. p63/ alpha-methylacyl-CoA racemase (AMACR) staining on consecutive sections was used to categorize tissue areas as benign or cancerous. Automated image analysis was used to quantify staining intensity. AR intensity was significantly higher in AMACR+ and lower in AMACR- cancer areas as compared with benign epithelium. The PSA intensity was significantly lower in cancer areas, particularly in AMACR- glands. The AR/PSA ratio varied significantly in the AMACR+ tumor cells as compared with benign glands. There was a trend of more rapid disease progression in patients with higher AR/PSA ratios in the AMACR- areas. This study demonstrates the feasibility of developing reproducible protocols for TRF imaging and automated image analysis to study the expression of AR and PSA in benign and malignant prostate. It also highlighted the differences in AR and PSA protein expression within AMACR- and AMACR+ cancer regions. © 2016 The Histochemical Society.

CNV-ROC: A cost effective, computer-aided analytical performance evaluator of chromosomal microarrays.

PubMed

Goodman, Corey W; Major, Heather J; Walls, William D; Sheffield, Val C; Casavant, Thomas L; Darbro, Benjamin W

2015-04-01

Chromosomal microarrays (CMAs) are routinely used in both research and clinical laboratories; yet, little attention has been given to the estimation of genome-wide true and false negatives during the assessment of these assays and how such information could be used to calibrate various algorithmic metrics to improve performance. Low-throughput, locus-specific methods such as fluorescence in situ hybridization (FISH), quantitative PCR (qPCR), or multiplex ligation-dependent probe amplification (MLPA) preclude rigorous calibration of various metrics used by copy number variant (CNV) detection algorithms. To aid this task, we have established a comparative methodology, CNV-ROC, which is capable of performing a high throughput, low cost, analysis of CMAs that takes into consideration genome-wide true and false negatives. CNV-ROC uses a higher resolution microarray to confirm calls from a lower resolution microarray and provides for a true measure of genome-wide performance metrics at the resolution offered by microarray testing. CNV-ROC also provides for a very precise comparison of CNV calls between two microarray platforms without the need to establish an arbitrary degree of overlap. Comparison of CNVs across microarrays is done on a per-probe basis and receiver operator characteristic (ROC) analysis is used to calibrate algorithmic metrics, such as log2 ratio threshold, to enhance CNV calling performance. CNV-ROC addresses a critical and consistently overlooked aspect of analytical assessments of genome-wide techniques like CMAs which is the measurement and use of genome-wide true and false negative data for the calculation of performance metrics and comparison of CNV profiles between different microarray experiments. Copyright © 2015 Elsevier Inc. All rights reserved.

MRI phenotypes with high neurodegeneration are associated with peripheral blood B-cell changes.

PubMed

Comabella, Manuel; Cantó, Ester; Nurtdinov, Ramil; Río, Jordi; Villar, Luisa M; Picón, Carmen; Castilló, Joaquín; Fissolo, Nicolás; Aymerich, Xavier; Auger, Cristina; Rovira, Alex; Montalban, Xavier

2016-01-15

Little is known about the mechanisms leading to neurodegeneration in multiple sclerosis (MS) and the role of peripheral blood cells in this neurodegenerative component. We aimed to correlate brain radiological phenotypes defined by high and low neurodegeneration with gene expression profiling of peripheral blood mononuclear cells (PBMC) from MS patients. Magnetic resonance imaging (MRI) scans from 64 patients with relapsing-remitting MS (RRMS) were classified into radiological phenotypes characterized by low (N = 27) and high (N = 37) neurodegeneration according to the number of contrast-enhancing lesions, the relative volume of non-enhancing black holes on T1-weighted images, and the brain parenchymal fraction. Gene expression profiling was determined in PBMC using microarrays, and validation of selected genes was performed by polymerase chain reaction (PCR). B-cell immunophenotyping was conducted by flow cytometry. Microarray analysis revealed the B-cell specific genes FCRL1, FCRL2, FCRL5 (Fc receptor-like 1, 2 and 5 respectively), and CD22 as the top differentially expressed genes between patients with high and low neurodegeneration. Levels for these genes were significantly down-regulated in PBMC from patients with MRI phenotypes characterized by high neurodegeneration and microarray findings were validated by PCR. In patients with high neurodegeneration, immunophenotyping showed a significant increase in the expression of the B-cell activation markers CD80 in naïve B cells (CD45+/CD19+/CD27-/IgD+), unswitched memory B cells (CD45+/CD19+/CD27+/IgD+), and switched memory B cells (CD45+/CD19+/CD27+/IgD-), and CD86 in naïve and switched memory B cells. These results suggest that RRMS patients with radiological phenotypes showing high neurodegeneration have changes in B cells characterized by down-regulation of B-cell-specific genes and increased activation status. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Micropatterned comet assay enables high throughput and sensitive DNA damage quantification

PubMed Central

Ge, Jing; Chow, Danielle N.; Fessler, Jessica L.; Weingeist, David M.; Wood, David K.; Engelward, Bevin P.

2015-01-01

The single cell gel electrophoresis assay, also known as the comet assay, is a versatile method for measuring many classes of DNA damage, including base damage, abasic sites, single strand breaks and double strand breaks. However, limited throughput and difficulties with reproducibility have limited its utility, particularly for clinical and epidemiological studies. To address these limitations, we created a microarray comet assay. The use of a micrometer scale array of cells increases the number of analysable comets per square centimetre and enables automated imaging and analysis. In addition, the platform is compatible with standard 24- and 96-well plate formats. Here, we have assessed the consistency and sensitivity of the microarray comet assay. We showed that the linear detection range for H2O2-induced DNA damage in human lymphoblastoid cells is between 30 and 100 μM, and that within this range, inter-sample coefficient of variance was between 5 and 10%. Importantly, only 20 comets were required to detect a statistically significant induction of DNA damage for doses within the linear range. We also evaluated sample-to-sample and experiment-to-experiment variation and found that for both conditions, the coefficient of variation was lower than what has been reported for the traditional comet assay. Finally, we also show that the assay can be performed using a 4× objective (rather than the standard 10× objective for the traditional assay). This adjustment combined with the microarray format makes it possible to capture more than 50 analysable comets in a single image, which can then be automatically analysed using in-house software. Overall, throughput is increased more than 100-fold compared to the traditional assay. Together, the results presented here demonstrate key advances in comet assay technology that improve the throughput, sensitivity, and robustness, thus enabling larger scale clinical and epidemiological studies. PMID:25527723

Micropatterned comet assay enables high throughput and sensitive DNA damage quantification.

PubMed

Ge, Jing; Chow, Danielle N; Fessler, Jessica L; Weingeist, David M; Wood, David K; Engelward, Bevin P

2015-01-01

The single cell gel electrophoresis assay, also known as the comet assay, is a versatile method for measuring many classes of DNA damage, including base damage, abasic sites, single strand breaks and double strand breaks. However, limited throughput and difficulties with reproducibility have limited its utility, particularly for clinical and epidemiological studies. To address these limitations, we created a microarray comet assay. The use of a micrometer scale array of cells increases the number of analysable comets per square centimetre and enables automated imaging and analysis. In addition, the platform is compatible with standard 24- and 96-well plate formats. Here, we have assessed the consistency and sensitivity of the microarray comet assay. We showed that the linear detection range for H2O2-induced DNA damage in human lymphoblastoid cells is between 30 and 100 μM, and that within this range, inter-sample coefficient of variance was between 5 and 10%. Importantly, only 20 comets were required to detect a statistically significant induction of DNA damage for doses within the linear range. We also evaluated sample-to-sample and experiment-to-experiment variation and found that for both conditions, the coefficient of variation was lower than what has been reported for the traditional comet assay. Finally, we also show that the assay can be performed using a 4× objective (rather than the standard 10× objective for the traditional assay). This adjustment combined with the microarray format makes it possible to capture more than 50 analysable comets in a single image, which can then be automatically analysed using in-house software. Overall, throughput is increased more than 100-fold compared to the traditional assay. Together, the results presented here demonstrate key advances in comet assay technology that improve the throughput, sensitivity, and robustness, thus enabling larger scale clinical and epidemiological studies. © The Author 2014. Published by Oxford University Press on behalf of the Mutagenesis Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Mining microarrays for metabolic meaning: nutritional regulation of hypothalamic gene expression.

PubMed

Mobbs, Charles V; Yen, Kelvin; Mastaitis, Jason; Nguyen, Ha; Watson, Elizabeth; Wurmbach, Elisa; Sealfon, Stuart C; Brooks, Andrew; Salton, Stephen R J

2004-06-01

DNA microarray analysis has been used to investigate relative changes in the level of gene expression in the CNS, including changes that are associated with disease, injury, psychiatric disorders, drug exposure or withdrawal, and memory formation. We have used oligonucleotide microarrays to identify hypothalamic genes that respond to nutritional manipulation. In addition to commonly used microarray analysis based on criteria such as fold-regulation, we have also found that simply carrying out multiple t tests then sorting by P value constitutes a highly reliable method to detect true regulation, as assessed by real-time polymerase chain reaction (PCR), even for relatively low abundance genes or relatively low magnitude of regulation. Such analyses directly suggested novel mechanisms that mediate effects of nutritional state on neuroendocrine function and are being used to identify regulated gene products that may elucidate the metabolic pathology of obese ob/ob, lean Vgf-/Vgf-, and other models with profound metabolic impairments.

Association of tumor-infiltrating T-cell density with molecular subtype, racial ancestry and clinical outcomes in prostate cancer.

PubMed

Kaur, Harsimar B; Guedes, Liana B; Lu, Jiayun; Maldonado, Laneisha; Reitz, Logan; Barber, John R; De Marzo, Angelo M; Tosoian, Jeffrey J; Tomlins, Scott A; Schaeffer, Edward M; Joshu, Corinne E; Sfanos, Karen S; Lotan, Tamara L

2018-05-30

The inflammatory microenvironment plays an important role in the pathogenesis and progression of tumors and may be associated with somatic genomic alterations. We examined the association of tumor-infiltrating T-cell density with clinical-pathologic variables, tumor molecular subtype, and oncologic outcomes in surgically treated primary prostate cancer occurring in patients of European-American or African-American ancestry. We evaluated 312 primary prostate tumors, enriched for patients with African-American ancestry and high grade disease. Tissue microarrays were immunostained for CD3, CD8, and FOXP3 and were previously immunostained for ERG and PTEN using genetically validated protocols. Image analysis for quantification of T-cell density in tissue microarray tumor spots was performed. Automated quantification of T-cell densities in tumor-containing regions of tissue microarray spots and standard histologic sections were correlated (r = 0.73, p < 0.00001) and there was good agreement between visual and automated T-cell density counts on tissue microarray spots (r = 0.93, p < 0.00001). There was a significant correlation between CD3+, CD8+, and FOXP3+ T-cell densities (p < 0.00001), but these were not associated with most clinical or pathologic variables. Increased T-cell density was significantly associated with ERG positivity (median 309 vs. 188 CD3+ T cells/mm 2 ; p = 0.0004) and also with PTEN loss (median 317 vs. 192 CD3+ T cells/mm 2 ; p = 0.001) in the combined cohort of matched European-American and African-American ancestry patients. The same association or a similar trend was present in patients of both ancestries when analyzed separately. When the African-American patients from the matched race set were combined with a separate high grade set of African-American cases, there was a weak association of increased FOXP3+ T-cell densities with increased risk of metastasis in multivariable analysis. Though high T-cell density is associated with specific molecular subclasses of prostate cancer, we did not find an association of T-cell density with racial ancestry.

Near-infrared fluorescence imaging of cancer mediated by tumor hypoxia and HIF1α/OATPs signaling axis

PubMed Central

Wu, Jason Boyang; Shao, Chen; Li, Xiangyan; Shi, Changhong; Li, Qinlong; Hu, Peizhen; Chen, Yi-Ting; Dou, Xiaoliang; Sahu, Divya; Li, Wei; Harada, Hiroshi; Zhang, Yi; Wang, Ruoxiang; Zhau, Haiyen E.; Chung, Leland W.K.

2014-01-01

Near-infrared fluorescence (NIRF) imaging agents are promising tools for noninvasive cancer imaging. Here, we explored the mechanistic properties of a specific group of NIR heptamethine carbocyanines including MHI-148 dye we identified and synthesized, and demonstrated these dyes to achieve cancer-specific imaging and targeting via a hypoxia-mediated mechanism. We found that cancer cells and tumor xenografts exhibited hypoxia-dependent MHI-148 dye uptake in vitro and in vivo, which was directly mediated by hypoxia-inducible factor 1α (HIF1α). Microarray analysis and dye uptake assay further revealed a group of hypoxia-inducible organic anion-transporting polypeptides (OATPs) responsible for dye uptake, and the correlation between OATPs and HIF1α was manifested in progressive clinical cancer specimens. Finally, we demonstrated increased uptake of MHI-148 dye in situ in perfused clinical tumor samples with activated HIF1α/OATPs signaling. Our results establish these NIRF dyes as potential tumor hypoxia-dependent cancer-targeting agents and provide a mechanistic rationale for continued development of NIRF imaging agents for improved cancer detection, prognosis and therapy. PMID:24957295

Multiplexed immunosensing and kinetics monitoring in nanofluidic devices with highly enhanced target capture efficiency

PubMed Central

Lin, Yii-Lih; Huang, Yen-Jun; Teerapanich, Pattamon; Leïchlé, Thierry

2016-01-01

Nanofluidic devices promise high reaction efficiency and fast kinetic responses due to the spatial constriction of transported biomolecules with confined molecular diffusion. However, parallel detection of multiple biomolecules, particularly proteins, in highly confined space remains challenging. This study integrates extended nanofluidics with embedded protein microarray to achieve multiplexed real-time biosensing and kinetics monitoring. Implementation of embedded standard-sized antibody microarray is attained by epoxy-silane surface modification and a room-temperature low-aspect-ratio bonding technique. An effective sample transport is achieved by electrokinetic pumping via electroosmotic flow. Through the nanoslit-based spatial confinement, the antigen-antibody binding reaction is enhanced with ∼100% efficiency and may be directly observed with fluorescence microscopy without the requirement of intermediate washing steps. The image-based data provide numerous spatially distributed reaction kinetic curves and are collectively modeled using a simple one-dimensional convection-reaction model. This study represents an integrated nanofluidic solution for real-time multiplexed immunosensing and kinetics monitoring, starting from device fabrication, protein immobilization, device bonding, sample transport, to data analysis at Péclet number less than 1. PMID:27375819

Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

PubMed

Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

2016-09-19

Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data.

PubMed

Bikel, Shirley; Jacobo-Albavera, Leonor; Sánchez-Muñoz, Fausto; Cornejo-Granados, Fernanda; Canizales-Quinteros, Samuel; Soberón, Xavier; Sotelo-Mundo, Rogerio R; Del Río-Navarro, Blanca E; Mendoza-Vargas, Alfredo; Sánchez, Filiberto; Ochoa-Leyva, Adrian

2017-01-01

In spite of the emergence of RNA sequencing (RNA-seq), microarrays remain in widespread use for gene expression analysis in the clinic. There are over 767,000 RNA microarrays from human samples in public repositories, which are an invaluable resource for biomedical research and personalized medicine. The absolute gene expression analysis allows the transcriptome profiling of all expressed genes under a specific biological condition without the need of a reference sample. However, the background fluorescence represents a challenge to determine the absolute gene expression in microarrays. Given that the Y chromosome is absent in female subjects, we used it as a new approach for absolute gene expression analysis in which the fluorescence of the Y chromosome genes of female subjects was used as the background fluorescence for all the probes in the microarray. This fluorescence was used to establish an absolute gene expression threshold, allowing the differentiation between expressed and non-expressed genes in microarrays. We extracted the RNA from 16 children leukocyte samples (nine males and seven females, ages 6-10 years). An Affymetrix Gene Chip Human Gene 1.0 ST Array was carried out for each sample and the fluorescence of 124 genes of the Y chromosome was used to calculate the absolute gene expression threshold. After that, several expressed and non-expressed genes according to our absolute gene expression threshold were compared against the expression obtained using real-time quantitative polymerase chain reaction (RT-qPCR). From the 124 genes of the Y chromosome, three genes (DDX3Y, TXLNG2P and EIF1AY) that displayed significant differences between sexes were used to calculate the absolute gene expression threshold. Using this threshold, we selected 13 expressed and non-expressed genes and confirmed their expression level by RT-qPCR. Then, we selected the top 5% most expressed genes and found that several KEGG pathways were significantly enriched. Interestingly, these pathways were related to the typical functions of leukocytes cells, such as antigen processing and presentation and natural killer cell mediated cytotoxicity. We also applied this method to obtain the absolute gene expression threshold in already published microarray data of liver cells, where the top 5% expressed genes showed an enrichment of typical KEGG pathways for liver cells. Our results suggest that the three selected genes of the Y chromosome can be used to calculate an absolute gene expression threshold, allowing a transcriptome profiling of microarray data without the need of an additional reference experiment. Our approach based on the establishment of a threshold for absolute gene expression analysis will allow a new way to analyze thousands of microarrays from public databases. This allows the study of different human diseases without the need of having additional samples for relative expression experiments.

A microarray analysis of retinal transcripts that are controlled by image contrast in mice.

PubMed

Brand, Christine; Schaeffel, Frank; Feldkaemper, Marita Pauline

2007-06-18

The development of myopia is controlled by still largely unknown retinal signals. The aim of this study was to investigate the changes in retinal mRNA expression after different periods of visual deprivation in mice, while controlling for retinal illuminance. Each group consisted of three male C57BL/6 mice. Treatment periods were 30 min, 4 h, and 6+6 h. High spatial frequencies were filtered from the retinal image by frosted diffusers over one eye while the fellow eyes were covered by clear neutral density (ND) filters that exhibited similar light attenuating properties (0.1 log units) as the diffusers. For the final 30 min of the respective treatment period mice were individually placed in a clear Perspex cylinder that was positioned in the center of a rotating (60 degrees) large drum. The inside of the drum was covered with a 0.1 cyc/degree vertical square wave grating. This visual environment was chosen to standardize illuminances and contrasts seen by the mice. Labeled cRNA was prepared and hybridized to Affymetrix GeneChip Mouse Genome 430 2.0 arrays. Alterations in mRNA expression levels of candidate genes with potential biological relevance were confirmed by semi-quantitative real-time reverse transcription polymerase chain reaction (RT-PCR). In all groups, Egr-1 mRNA expression was reduced in diffuser-treated eyes. Furthermore, the degradation of the spatial frequency spectrum also changed the cFos mRNA level, with reduced expression after 4 h of diffuser treatment. Other interesting candidates were Akt2, which was up-regulated after 30 min of deprivation and Mapk8ip3, a neuron specific JNK binding and scaffolding protein that was temporally regulated in the diffuser-treated eyes only. The microarray analysis demonstrated a pattern of differential transcriptional changes, even though differences in the retinal images were restricted to spatial features. The candidate genes may provide further insight into the biochemical short-term changes following retinal image degradation in mice. Because deprivation of spatial vision leads to increased eye growth and myopia in both animals and humans, it is believed some of the identified genes play a role in myopia development.

Chipster: user-friendly analysis software for microarray and other high-throughput data.

PubMed

Kallio, M Aleksi; Tuimala, Jarno T; Hupponen, Taavi; Klemelä, Petri; Gentile, Massimiliano; Scheinin, Ilari; Koski, Mikko; Käki, Janne; Korpelainen, Eija I

2011-10-14

The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. Chipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available.

Chipster: user-friendly analysis software for microarray and other high-throughput data

PubMed Central

2011-01-01

Background The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software. Results Chipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies. Conclusions Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available. PMID:21999641

A database for the analysis of immunity genes in Drosophila: PADMA database.

PubMed

Lee, Mark J; Mondal, Ariful; Small, Chiyedza; Paddibhatla, Indira; Kawaguchi, Akira; Govind, Shubha

2011-01-01

While microarray experiments generate voluminous data, discerning trends that support an existing or alternative paradigm is challenging. To synergize hypothesis building and testing, we designed the Pathogen Associated Drosophila MicroArray (PADMA) database for easy retrieval and comparison of microarray results from immunity-related experiments (www.padmadatabase.org). PADMA also allows biologists to upload their microarray-results and compare it with datasets housed within PADMA. We tested PADMA using a preliminary dataset from Ganaspis xanthopoda-infected fly larvae, and uncovered unexpected trends in gene expression, reshaping our hypothesis. Thus, the PADMA database will be a useful resource to fly researchers to evaluate, revise, and refine hypotheses.

Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

ERIC Educational Resources Information Center

Tra, Yolande V.; Evans, Irene M.

2010-01-01

"BIO2010" put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on…

Diagnostic Yield of Chromosomal Microarray Analysis in a Cohort of Patients with Autism Spectrum Disorders from a Highly Consanguineous Population

ERIC Educational Resources Information Center

Al-Mamari, Watfa; Al-Saegh, Abeer; Al-Kindy, Adila; Bruwer, Zandre; Al-Murshedi, Fathiya; Al-Thihli, Khalid

2015-01-01

Autism Spectrum Disorders are a complicated group of disorders characterized with heterogeneous genetic etiologies. The genetic investigations for this group of disorders have expanded considerably over the past decade. In our study we designed a tired approach and studied the diagnostic yield of chromosomal microarray analysis on patients…

Immunological Targeting of Tumor Initiating Prostate Cancer Cells

DTIC Science & Technology

2014-10-01

clinically using well-accepted immuno-competent animal models. 2) Keywords: Prostate Cancer, Lymphocyte, Vaccine, Antibody 3) Overall Project Summary...castrate animals . Task 1: Identify and verify antigenic targets from CAstrate Resistant Luminal Epithelial Cells (CRLEC) (months 1-16... animals per group will be processed to derive sufficient RNA for microarray analysis; the experiment will be repeated x 3. Microarray analysis will

MiMiR – an integrated platform for microarray data sharing, mining and analysis

PubMed Central

Tomlinson, Chris; Thimma, Manjula; Alexandrakis, Stelios; Castillo, Tito; Dennis, Jayne L; Brooks, Anthony; Bradley, Thomas; Turnbull, Carly; Blaveri, Ekaterini; Barton, Geraint; Chiba, Norie; Maratou, Klio; Soutter, Pat; Aitman, Tim; Game, Laurence

2008-01-01

Background Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data. Results A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package. Conclusion The new MiMiR suite of software enables systematic and effective capture of extensive experimental and clinical information with the highest MIAME score, and secure data sharing prior to publication. MiMiR currently contains more than 150 experiments corresponding to over 3000 hybridisations and supports the Microarray Centre's large microarray user community and two international consortia. The MiMiR flexible and scalable hardware and software architecture enables secure warehousing of thousands of datasets, including clinical studies, from microarray and potentially other -omics technologies. PMID:18801157

MiMiR--an integrated platform for microarray data sharing, mining and analysis.

PubMed

Tomlinson, Chris; Thimma, Manjula; Alexandrakis, Stelios; Castillo, Tito; Dennis, Jayne L; Brooks, Anthony; Bradley, Thomas; Turnbull, Carly; Blaveri, Ekaterini; Barton, Geraint; Chiba, Norie; Maratou, Klio; Soutter, Pat; Aitman, Tim; Game, Laurence

2008-09-18

Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data. A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package. The new MiMiR suite of software enables systematic and effective capture of extensive experimental and clinical information with the highest MIAME score, and secure data sharing prior to publication. MiMiR currently contains more than 150 experiments corresponding to over 3000 hybridisations and supports the Microarray Centre's large microarray user community and two international consortia. The MiMiR flexible and scalable hardware and software architecture enables secure warehousing of thousands of datasets, including clinical studies, from microarray and potentially other -omics technologies.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andersen, G.L.; He, Z.; DeSantis, T.Z.

Microarrays have proven to be a useful and high-throughput method to provide targeted DNA sequence information for up to many thousands of specific genetic regions in a single test. A microarray consists of multiple DNA oligonucleotide probes that, under high stringency conditions, hybridize only to specific complementary nucleic acid sequences (targets). A fluorescent signal indicates the presence and, in many cases, the abundance of genetic regions of interest. In this chapter we will look at how microarrays are used in microbial ecology, especially with the recent increase in microbial community DNA sequence data. Of particular interest to microbial ecologists, phylogeneticmore » microarrays are used for the analysis of phylotypes in a community and functional gene arrays are used for the analysis of functional genes, and, by inference, phylotypes in environmental samples. A phylogenetic microarray that has been developed by the Andersen laboratory, the PhyloChip, will be discussed as an example of a microarray that targets the known diversity within the 16S rRNA gene to determine microbial community composition. Using multiple, confirmatory probes to increase the confidence of detection and a mismatch probe for every perfect match probe to minimize the effect of cross-hybridization by non-target regions, the PhyloChip is able to simultaneously identify any of thousands of taxa present in an environmental sample. The PhyloChip is shown to reveal greater diversity within a community than rRNA gene sequencing due to the placement of the entire gene product on the microarray compared with the analysis of up to thousands of individual molecules by traditional sequencing methods. A functional gene array that has been developed by the Zhou laboratory, the GeoChip, will be discussed as an example of a microarray that dynamically identifies functional activities of multiple members within a community. The recent version of GeoChip contains more than 24,000 50mer oligonucleotide probes and covers more than 10,000 gene sequences in 150 gene categories involved in carbon, nitrogen, sulfur, and phosphorus cycling, metal resistance and reduction, and organic contaminant degradation. GeoChip can be used as a generic tool for microbial community analysis, and also link microbial community structure to ecosystem functioning. Examples of the application of both arrays in different environmental samples will be described in the two subsequent sections.« less

Evaluation of a low density DNA microarray for small B-cell non-Hodgkin lymphoma differential diagnosis.

PubMed

Gillet, Jean-Pierre; Molina, Thierry Jo; Jamart, Jacques; Gaulard, Philippe; Leroy, Karen; Briere, Josette; Theate, Ivan; Thieblemont, Catherine; Bosly, Andre; Herin, Michel; Hamels, Jacques; Remacle, Jose

2009-03-01

Lymphomas are classified according to the World Health Organisation (WHO) classification which defines subtypes on the basis of clinical, morphological, immunophenotypic, molecular and cytogenetic criteria. Differential diagnosis of the subtypes is sometimes difficult, especially for small B-cell lymphoma (SBCL). Standardisation of molecular genetic assays using multiple gene expression analysis by microarrays could be a useful complement to the current diagnosis. The aim of the present study was to develop a low density DNA microarray for the analysis of 107 genes associated with B-cell non-Hodgkin lymphoma and to evaluate its performance in the diagnosis of SBCL. A predictive tool based on Fisher discriminant analysis using a training set of 40 patients including four different subtypes (follicular lymphoma n = 15, mantle cell lymphoma n = 7, B-cell chronic lymphocytic leukemia n = 6 and splenic marginal zone lymphoma n = 12) was designed. A short additional preliminary analysis to gauge the accuracy of this signature was then performed on an external set of nine patients. Using this model, eight of nine of those samples were classified successfully. This pilot study demonstrates that such a microarray tool may be a promising diagnostic approach for small B-cell non-Hodgkin lymphoma.

MAGMA: analysis of two-channel microarrays made easy.

PubMed

Rehrauer, Hubert; Zoller, Stefan; Schlapbach, Ralph

2007-07-01

The web application MAGMA provides a simple and intuitive interface to identify differentially expressed genes from two-channel microarray data. While the underlying algorithms are not superior to those of similar web applications, MAGMA is particularly user friendly and can be used without prior training. The user interface guides the novice user through the most typical microarray analysis workflow consisting of data upload, annotation, normalization and statistical analysis. It automatically generates R-scripts that document MAGMA's entire data processing steps, thereby allowing the user to regenerate all results in his local R installation. The implementation of MAGMA follows the model-view-controller design pattern that strictly separates the R-based statistical data processing, the web-representation and the application logic. This modular design makes the application flexible and easily extendible by experts in one of the fields: statistical microarray analysis, web design or software development. State-of-the-art Java Server Faces technology was used to generate the web interface and to perform user input processing. MAGMA's object-oriented modular framework makes it easily extendible and applicable to other fields and demonstrates that modern Java technology is also suitable for rather small and concise academic projects. MAGMA is freely available at www.magma-fgcz.uzh.ch.

Tomato Expression Database (TED): a suite of data presentation and analysis tools

PubMed Central

Fei, Zhangjun; Tang, Xuemei; Alba, Rob; Giovannoni, James

2006-01-01

The Tomato Expression Database (TED) includes three integrated components. The Tomato Microarray Data Warehouse serves as a central repository for raw gene expression data derived from the public tomato cDNA microarray. In addition to expression data, TED stores experimental design and array information in compliance with the MIAME guidelines and provides web interfaces for researchers to retrieve data for their own analysis and use. The Tomato Microarray Expression Database contains normalized and processed microarray data for ten time points with nine pair-wise comparisons during fruit development and ripening in a normal tomato variety and nearly isogenic single gene mutants impacting fruit development and ripening. Finally, the Tomato Digital Expression Database contains raw and normalized digital expression (EST abundance) data derived from analysis of the complete public tomato EST collection containing >150 000 ESTs derived from 27 different non-normalized EST libraries. This last component also includes tools for the comparison of tomato and Arabidopsis digital expression data. A set of query interfaces and analysis, and visualization tools have been developed and incorporated into TED, which aid users in identifying and deciphering biologically important information from our datasets. TED can be accessed at . PMID:16381976

Tomato Expression Database (TED): a suite of data presentation and analysis tools.

PubMed

Fei, Zhangjun; Tang, Xuemei; Alba, Rob; Giovannoni, James

2006-01-01

The Tomato Expression Database (TED) includes three integrated components. The Tomato Microarray Data Warehouse serves as a central repository for raw gene expression data derived from the public tomato cDNA microarray. In addition to expression data, TED stores experimental design and array information in compliance with the MIAME guidelines and provides web interfaces for researchers to retrieve data for their own analysis and use. The Tomato Microarray Expression Database contains normalized and processed microarray data for ten time points with nine pair-wise comparisons during fruit development and ripening in a normal tomato variety and nearly isogenic single gene mutants impacting fruit development and ripening. Finally, the Tomato Digital Expression Database contains raw and normalized digital expression (EST abundance) data derived from analysis of the complete public tomato EST collection containing >150,000 ESTs derived from 27 different non-normalized EST libraries. This last component also includes tools for the comparison of tomato and Arabidopsis digital expression data. A set of query interfaces and analysis, and visualization tools have been developed and incorporated into TED, which aid users in identifying and deciphering biologically important information from our datasets. TED can be accessed at http://ted.bti.cornell.edu.

Development and application of a microarray meter tool to optimize microarray experiments

PubMed Central

Rouse, Richard JD; Field, Katrine; Lapira, Jennifer; Lee, Allen; Wick, Ivan; Eckhardt, Colleen; Bhasker, C Ramana; Soverchia, Laura; Hardiman, Gary

2008-01-01

Background Successful microarray experimentation requires a complex interplay between the slide chemistry, the printing pins, the nucleic acid probes and targets, and the hybridization milieu. Optimization of these parameters and a careful evaluation of emerging slide chemistries are a prerequisite to any large scale array fabrication effort. We have developed a 'microarray meter' tool which assesses the inherent variations associated with microarray measurement prior to embarking on large scale projects. Findings The microarray meter consists of nucleic acid targets (reference and dynamic range control) and probe components. Different plate designs containing identical probe material were formulated to accommodate different robotic and pin designs. We examined the variability in probe quality and quantity (as judged by the amount of DNA printed and remaining post-hybridization) using three robots equipped with capillary printing pins. Discussion The generation of microarray data with minimal variation requires consistent quality control of the (DNA microarray) manufacturing and experimental processes. Spot reproducibility is a measure primarily of the variations associated with printing. The microarray meter assesses array quality by measuring the DNA content for every feature. It provides a post-hybridization analysis of array quality by scoring probe performance using three metrics, a) a measure of variability in the signal intensities, b) a measure of the signal dynamic range and c) a measure of variability of the spot morphologies. PMID:18710498

High-throughput DNA microarray detection of pathogenic bacteria in shallow well groundwater in the Kathmandu Valley, Nepal.

PubMed

Inoue, Daisuke; Hinoura, Takuji; Suzuki, Noriko; Pang, Junqin; Malla, Rabin; Shrestha, Sadhana; Chapagain, Saroj Kumar; Matsuzawa, Hiroaki; Nakamura, Takashi; Tanaka, Yasuhiro; Ike, Michihiko; Nishida, Kei; Sei, Kazunari

2015-01-01

Because of heavy dependence on groundwater for drinking water and other domestic use, microbial contamination of groundwater is a serious problem in the Kathmandu Valley, Nepal. This study investigated comprehensively the occurrence of pathogenic bacteria in shallow well groundwater in the Kathmandu Valley by applying DNA microarray analysis targeting 941 pathogenic bacterial species/groups. Water quality measurements found significant coliform (fecal) contamination in 10 of the 11 investigated groundwater samples and significant nitrogen contamination in some samples. The results of DNA microarray analysis revealed the presence of 1-37 pathogen species/groups, including 1-27 biosafety level 2 ones, in 9 of the 11 groundwater samples. While the detected pathogens included several feces- and animal-related ones, those belonging to Legionella and Arthrobacter, which were considered not to be directly associated with feces, were detected prevalently. This study could provide a rough picture of overall pathogenic bacterial contamination in the Kathmandu Valley, and demonstrated the usefulness of DNA microarray analysis as a comprehensive screening tool of a wide variety of pathogenic bacteria.

Microarray Analysis of Long Noncoding RNAs in Female Diabetic Peripheral Neuropathy Patients.

PubMed

Luo, Lin; Ji, Lin-Dan; Cai, Jiang-Jia; Feng, Mei; Zhou, Mi; Hu, Su-Pei; Xu, Jin; Zhou, Wen-Hua

2018-01-01

Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus (DM). Because of its controversial pathogenesis, DPN is still not diagnosed or managed properly in most patients. In this study, human lncRNA microarrays were used to identify the differentially expressed lncRNAs in DM and DPN patients, and some of the discovered lncRNAs were further validated in additional 78 samples by quantitative realtime PCR (qRT-PCR). The microarray analysis identified 446 and 1327 differentially expressed lncRNAs in DM and DPN, respectively. The KEGG pathway analysis further revealed that the differentially expressed lncRNA-coexpressed mRNAs between DPN and DM groups were significantly enriched in the MAPK signaling pathway. The lncRNA/mRNA coexpression network indicated that BDNF and TRAF2 correlated with 6 lncRNAs. The qRT-PCR confirmed the initial microarray results. These findings demonstrated that the interplay between lncRNAs and mRNA may be involved in the pathogenesis of DPN, especially the neurotrophin-MAPK signaling pathway, thus providing relevant information for future studies. © 2018 The Author(s). Published by S. Karger AG, Basel.

MASQOT: a method for cDNA microarray spot quality control

PubMed Central

Bylesjö, Max; Eriksson, Daniel; Sjödin, Andreas; Sjöström, Michael; Jansson, Stefan; Antti, Henrik; Trygg, Johan

2005-01-01

Background cDNA microarray technology has emerged as a major player in the parallel detection of biomolecules, but still suffers from fundamental technical problems. Identifying and removing unreliable data is crucial to prevent the risk of receiving illusive analysis results. Visual assessment of spot quality is still a common procedure, despite the time-consuming work of manually inspecting spots in the range of hundreds of thousands or more. Results A novel methodology for cDNA microarray spot quality control is outlined. Multivariate discriminant analysis was used to assess spot quality based on existing and novel descriptors. The presented methodology displays high reproducibility and was found superior in identifying unreliable data compared to other evaluated methodologies. Conclusion The proposed methodology for cDNA microarray spot quality control generates non-discrete values of spot quality which can be utilized as weights in subsequent analysis procedures as well as to discard spots of undesired quality using the suggested threshold values. The MASQOT approach provides a consistent assessment of spot quality and can be considered an alternative to the labor-intensive manual quality assessment process. PMID:16223442

Clinical application of chromosomal microarray analysis for the prenatal diagnosis of chromosomal abnormalities and copy number variations in fetuses with congenital heart disease.

PubMed

Xia, Yu; Yang, Yongchao; Huang, Shufang; Wu, Yueheng; Li, Ping; Zhuang, Jian

2018-03-24

This study aimed to determine chromosomal abnormalities and copy number variations (CNVs) in fetuses with congenital heart disease (CHD) by chromosomal microarray analysis (CMA). One hundred and ten cases with CHD detected by prenatal echocardiography were enrolled in the study; 27 cases were simple CHDs, and 83 were complex CHDs. Chromosomal microarray analysis was performed on the Affymetrix CytoScan HD platform. All annotated CNVs were validated by quantitative PCR. Chromosomal microarray analysis identified 6 cases with chromosomal abnormalities, including 2 cases with trisomy 21, 2 cases with trisomy 18, 1 case with trisomy 13, and 1 unusual case of mosaic trisomy 21. Pathogenic CNVs were detected in 15.5% (17/110) of the fetuses with CHDs, including 13 cases with CHD-associated CNVs. We further identified 10 genes as likely novel CHD candidate genes through gene functional enrichment analysis. We also found that pathogenic CMA results impacted the rate of pregnancy termination. This study shows that CMA is particularly effective for identifying chromosomal abnormalities and CNVs in fetuses with CHDs as well as having an effect on obstetrical outcomes. The elucidation of the genetic basis of CHDs will continue to expand our understanding of the etiology of CHDs. © 2018 John Wiley & Sons, Ltd.

An evaluation of tyramide signal amplification and archived fixed and frozen tissue in microarray gene expression analysis

PubMed Central

Karsten, Stanislav L.; Van Deerlin, Vivianna M. D.; Sabatti, Chiara; Gill, Lisa H.; Geschwind, Daniel H.

2002-01-01

Archival formalin-fixed, paraffin-embedded and ethanol-fixed tissues represent a potentially invaluable resource for gene expression analysis, as they are the most widely available material for studies of human disease. Little data are available evaluating whether RNA obtained from fixed (archival) tissues could produce reliable and reproducible microarray expression data. Here we compare the use of RNA isolated from human archival tissues fixed in ethanol and formalin to frozen tissue in cDNA microarray experiments. Since an additional factor that can limit the utility of archival tissue is the often small quantities available, we also evaluate the use of the tyramide signal amplification method (TSA), which allows the use of small amounts of RNA. Detailed analysis indicates that TSA provides a consistent and reproducible signal amplification method for cDNA microarray analysis, across both arrays and the genes tested. Analysis of this method also highlights the importance of performing non-linear channel normalization and dye switching. Furthermore, archived, fixed specimens can perform well, but not surprisingly, produce more variable results than frozen tissues. Consistent results are more easily obtainable using ethanol-fixed tissues, whereas formalin-fixed tissue does not typically provide a useful substrate for cDNA synthesis and labeling. PMID:11788730

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

PubMed Central

2012-01-01

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings. PMID:16964229

Women's experiences receiving abnormal prenatal chromosomal microarray testing results.

PubMed

Bernhardt, Barbara A; Soucier, Danielle; Hanson, Karen; Savage, Melissa S; Jackson, Laird; Wapner, Ronald J

2013-02-01

Genomic microarrays can detect copy-number variants not detectable by conventional cytogenetics. This technology is diffusing rapidly into prenatal settings even though the clinical implications of many copy-number variants are currently unknown. We conducted a qualitative pilot study to explore the experiences of women receiving abnormal results from prenatal microarray testing performed in a research setting. Participants were a subset of women participating in a multicenter prospective study "Prenatal Cytogenetic Diagnosis by Array-based Copy Number Analysis." Telephone interviews were conducted with 23 women receiving abnormal prenatal microarray results. We found that five key elements dominated the experiences of women who had received abnormal prenatal microarray results: an offer too good to pass up, blindsided by the results, uncertainty and unquantifiable risks, need for support, and toxic knowledge. As prenatal microarray testing is increasingly used, uncertain findings will be common, resulting in greater need for careful pre- and posttest counseling, and more education of and resources for providers so they can adequately support the women who are undergoing testing.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays.

PubMed

Hajiloo, Mohsen; Rabiee, Hamid R; Anooshahpour, Mahdi

2013-01-01

The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification. Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data. Fuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients

PubMed Central

Haitsma, Jack J.; Furmli, Suleiman; Masoom, Hussain; Liu, Mingyao; Imai, Yumiko; Slutsky, Arthur S.; Beyene, Joseph; Greenwood, Celia M. T.; dos Santos, Claudia

2012-01-01

Objectives To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans. Methods We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients. Results Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis. Conclusion Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of “injury" gene predictors that can classify lung injury samples and identify patients at risk for clinically relevant lung injury complications. PMID:23071521

High-density fiber optic biosensor arrays

NASA Astrophysics Data System (ADS)

Epstein, Jason R.; Walt, David R.

2002-02-01

Novel approaches are required to coordinate the immense amounts of information derived from diverse genomes. This concept has influenced the expanded role of high-throughput DNA detection and analysis in the biological sciences. A high-density fiber optic DNA biosensor was developed consisting of oligonucleotide-functionalized, 3.1 mm diameter microspheres deposited into the etched wells on the distal face of a 500 micrometers imaging fiber bundle. Imaging fiber bundles containing thousands of optical fibers, each associated with a unique oligonucleotide probe sequence, were the foundation for an optically connected, individually addressable DNA detection platform. Different oligonucleotide-functionalized microspheres were combined in a stock solution, and randomly dispersed into the etched wells. Microsphere positions were registered from optical dyes incorporated onto the microspheres. The distribution process provided an inherent redundancy that increases the signal-to-noise ratio as the square root of the number of sensors examined. The representative amount of each probe-type in the array was dependent on their initial stock solution concentration, and as other sequences of interest arise, new microsphere elements can be added to arrays without altering the existing detection capabilities. The oligonucleotide probe sequences hybridize to fluorescently-labeled, complementary DNA target solutions. Fiber optic DNA microarray research has included DNA-protein interaction profiles, microbial strain differentiation, non-labeled target interrogation with molecular beacons, and single cell-based assays. This biosensor array is proficient in DNA detection linked to specific disease states, single nucleotide polymorphism (SNP's) discrimination, and gene expression analysis. This array platform permits multiple detection formats, provides smaller feature sizes, and enables sensor design flexibility. High-density fiber optic microarray biosensors provide a fast, reversible format with the detection limit of a few hundred molecules.

Addressable droplet microarrays for single cell protein analysis.

PubMed

Salehi-Reyhani, Ali; Burgin, Edward; Ces, Oscar; Willison, Keith R; Klug, David R

2014-11-07

Addressable droplet microarrays are potentially attractive as a way to achieve miniaturised, reduced volume, high sensitivity analyses without the need to fabricate microfluidic devices or small volume chambers. We report a practical method for producing oil-encapsulated addressable droplet microarrays which can be used for such analyses. To demonstrate their utility, we undertake a series of single cell analyses, to determine the variation in copy number of p53 proteins in cells of a human cancer cell line.

Synthetic Biomaterials to Rival Nature's Complexity-a Path Forward with Combinatorics, High-Throughput Discovery, and High-Content Analysis.

PubMed

Zhang, Douglas; Lee, Junmin; Kilian, Kristopher A

2017-10-01

Cells in tissue receive a host of soluble and insoluble signals in a context-dependent fashion, where integration of these cues through a complex network of signal transduction cascades will define a particular outcome. Biomaterials scientists and engineers are tasked with designing materials that can at least partially recreate this complex signaling milieu towards new materials for biomedical applications. In this progress report, recent advances in high throughput techniques and high content imaging approaches that are facilitating the discovery of efficacious biomaterials are described. From microarrays of synthetic polymers, peptides and full-length proteins, to designer cell culture systems that present multiple biophysical and biochemical cues in tandem, it is discussed how the integration of combinatorics with high content imaging and analysis is essential to extracting biologically meaningful information from large scale cellular screens to inform the design of next generation biomaterials. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Recurrent pregnancy loss evaluation combined with 24-chromosome microarray of miscarriage tissue provides a probable or definite cause of pregnancy loss in over 90% of patients.

PubMed

Popescu, F; Jaslow, C R; Kutteh, W H

2018-04-01

Will the addition of 24-chromosome microarray analysis on miscarriage tissue combined with the standard American Society for Reproductive Medicine (ASRM) evaluation for recurrent miscarriage explain most losses? Over 90% of patients with recurrent pregnancy loss (RPL) will have a probable or definitive cause identified when combining genetic testing on miscarriage tissue with the standard ASRM evaluation for recurrent miscarriage. RPL is estimated to occur in 2-4% of reproductive age couples. A probable cause can be identified in approximately 50% of patients after an ASRM recommended workup including an evaluation for parental chromosomal abnormalities, congenital and acquired uterine anomalies, endocrine imbalances and autoimmune factors including antiphospholipid syndrome. Single-center, prospective cohort study that included 100 patients seen in a private RPL clinic from 2014 to 2017. All 100 women had two or more pregnancy losses, a complete evaluation for RPL as defined by the ASRM, and miscarriage tissue evaluated by 24-chromosome microarray analysis after their second or subsequent miscarriage. Frequencies of abnormal results for evidence-based diagnostic tests considered definite or probable causes of RPL (karyotyping for parental chromosomal abnormalities, and 24-chromosome microarray evaluation for products of conception (POC); pelvic sonohysterography, hysterosalpingogram, or hysteroscopy for uterine anomalies; immunological tests for lupus anticoagulant and anticardiolipin antibodies; and blood tests for thyroid stimulating hormone (TSH), prolactin and hemoglobin A1c) were evaluated. We excluded cases where there was maternal cell contamination of the miscarriage tissue or if the ASRM evaluation was incomplete. A cost analysis for the evaluation of RPL was conducted to determine whether a proposed procedure of 24-chromome microarray evaluation followed by an ASRM RPL workup (for those RPL patients who had a normal 24-chromosome microarray evaluation) was more cost-efficient than conducting ASRM RPL workups on RPL patients followed by 24-chromosome microarray analysis (for those RPL patients who had a normal RPL workup). A definite or probable cause of pregnancy loss was identified in the vast majority (95/100; 95%) of RPL patients when a 24-chromosome pair microarray evaluation of POC testing is combined with the standard ASRM RPL workup evaluation at the time of the second or subsequent loss. The ASRM RPL workup identified an abnormality and a probable explanation for pregnancy loss in only 45/100 or 45% of all patients. A definite abnormality was identified in 67/100 patients or 67% when initial testing was performed using 24-chromosome microarray analyses on the miscarriage tissue. Only 5/100 (5%) patients, who had a euploid loss and a normal ASRM RPL workup, had a pregnancy loss without a probable or definitive cause identified. All other losses were explained by an abnormal 24-chromosome microarray analysis of the miscarriage tissue, an abnormal finding of the RPL workup, or a combination of both. Results from the cost analysis indicated that an initial approach of using a 24-chromosome microarray analysis on miscarriage tissue resulted in a 50% savings in cost to the health care system and to the patient. This is a single-center study on a small group of well-characterized women with RPL. There was an incomplete follow-up on subsequent pregnancy outcomes after evaluation, however this should not affect our principal results. The maternal age of patients varied from 26 to 45 years old. More aneuploid pregnancy losses would be expected in older women, particularly over the age of 35 years old. Evaluation of POC using 24-chromosome microarray analysis adds significantly to the ASRM recommended evaluation of RPL. Genetic evaluation on miscarriage tissue obtained at the time of the second and subsequent pregnancy losses should be offered to all couples with two or more consecutive pregnancy losses. The combination of a genetic evaluation on miscarriage tissue with an evidence-based evaluation for RPL will identify a probable or definitive cause in over 90% of miscarriages. No funding was received for this study and there are no conflicts of interest to declare. Not applicable.

A Platform for Combined DNA and Protein Microarrays Based on Total Internal Reflection Fluorescence

PubMed Central

Asanov, Alexander; Zepeda, Angélica; Vaca, Luis

2012-01-01

We have developed a novel microarray technology based on total internal reflection fluorescence (TIRF) in combination with DNA and protein bioassays immobilized at the TIRF surface. Unlike conventional microarrays that exhibit reduced signal-to-background ratio, require several stages of incubation, rinsing and stringency control, and measure only end-point results, our TIRF microarray technology provides several orders of magnitude better signal-to-background ratio, performs analysis rapidly in one step, and measures the entire course of association and dissociation kinetics between target DNA and protein molecules and the bioassays. In many practical cases detection of only DNA or protein markers alone does not provide the necessary accuracy for diagnosing a disease or detecting a pathogen. Here we describe TIRF microarrays that detect DNA and protein markers simultaneously, which reduces the probabilities of false responses. Supersensitive and multiplexed TIRF DNA and protein microarray technology may provide a platform for accurate diagnosis or enhanced research studies. Our TIRF microarray system can be mounted on upright or inverted microscopes or interfaced directly with CCD cameras equipped with a single objective, facilitating the development of portable devices. As proof-of-concept we applied TIRF microarrays for detecting molecular markers from Bacillus anthracis, the pathogen responsible for anthrax. PMID:22438738

Validation of MIMGO: a method to identify differentially expressed GO terms in a microarray dataset

PubMed Central

2012-01-01

Background We previously proposed an algorithm for the identification of GO terms that commonly annotate genes whose expression is upregulated or downregulated in some microarray data compared with in other microarray data. We call these “differentially expressed GO terms” and have named the algorithm “matrix-assisted identification method of differentially expressed GO terms” (MIMGO). MIMGO can also identify microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. However, MIMGO has not yet been validated on a real microarray dataset using all available GO terms. Findings We combined Gene Set Enrichment Analysis (GSEA) with MIMGO to identify differentially expressed GO terms in a yeast cell cycle microarray dataset. GSEA followed by MIMGO (GSEA + MIMGO) correctly identified (p < 0.05) microarray data in which genes annotated to differentially expressed GO terms are upregulated. We found that GSEA + MIMGO was slightly less effective than, or comparable to, GSEA (Pearson), a method that uses Pearson’s correlation as a metric, at detecting true differentially expressed GO terms. However, unlike other methods including GSEA (Pearson), GSEA + MIMGO can comprehensively identify the microarray data in which genes annotated with a differentially expressed GO term are upregulated or downregulated. Conclusions MIMGO is a reliable method to identify differentially expressed GO terms comprehensively. PMID:23232071

The use of open source bioinformatics tools to dissect transcriptomic data.

PubMed

Nitsche, Benjamin M; Ram, Arthur F J; Meyer, Vera

2012-01-01

Microarrays are a valuable technology to study fungal physiology on a transcriptomic level. Various microarray platforms are available comprising both single and two channel arrays. Despite different technologies, preprocessing of microarray data generally includes quality control, background correction, normalization, and summarization of probe level data. Subsequently, depending on the experimental design, diverse statistical analysis can be performed, including the identification of differentially expressed genes and the construction of gene coexpression networks.We describe how Bioconductor, a collection of open source and open development packages for the statistical programming language R, can be used for dissecting microarray data. We provide fundamental details that facilitate the process of getting started with R and Bioconductor. Using two publicly available microarray datasets from Aspergillus niger, we give detailed protocols on how to identify differentially expressed genes and how to construct gene coexpression networks.

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

PubMed Central

Peterson, Leif E

2002-01-01

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

Parallel characterization of anaerobic toluene- and ethylbenzene-degrading microbial consortia by PCR-denaturing gradient gel electrophoresis, RNA-DNA membrane hybridization, and DNA microarray technology

NASA Technical Reports Server (NTRS)

Koizumi, Yoshikazu; Kelly, John J.; Nakagawa, Tatsunori; Urakawa, Hidetoshi; El-Fantroussi, Said; Al-Muzaini, Saleh; Fukui, Manabu; Urushigawa, Yoshikuni; Stahl, David A.

2002-01-01

A mesophilic toluene-degrading consortium (TDC) and an ethylbenzene-degrading consortium (EDC) were established under sulfate-reducing conditions. These consortia were first characterized by denaturing gradient gel electrophoresis (DGGE) fingerprinting of PCR-amplified 16S rRNA gene fragments, followed by sequencing. The sequences of the major bands (T-1 and E-2) belonging to TDC and EDC, respectively, were affiliated with the family Desulfobacteriaceae. Another major band from EDC (E-1) was related to an uncultured non-sulfate-reducing soil bacterium. Oligonucleotide probes specific for the 16S rRNAs of target organisms corresponding to T-1, E-1, and E-2 were designed, and hybridization conditions were optimized for two analytical formats, membrane and DNA microarray hybridization. Both formats were used to characterize the TDC and EDC, and the results of both were consistent with DGGE analysis. In order to assess the utility of the microarray format for analysis of environmental samples, oil-contaminated sediments from the coast of Kuwait were analyzed. The DNA microarray successfully detected bacterial nucleic acids from these samples, but probes targeting specific groups of sulfate-reducing bacteria did not give positive signals. The results of this study demonstrate the limitations and the potential utility of DNA microarrays for microbial community analysis.

Parallel Characterization of Anaerobic Toluene- and Ethylbenzene-Degrading Microbial Consortia by PCR-Denaturing Gradient Gel Electrophoresis, RNA-DNA Membrane Hybridization, and DNA Microarray Technology

PubMed Central

Koizumi, Yoshikazu; Kelly, John J.; Nakagawa, Tatsunori; Urakawa, Hidetoshi; El-Fantroussi, Saïd; Al-Muzaini, Saleh; Fukui, Manabu; Urushigawa, Yoshikuni; Stahl, David A.

2002-01-01

A mesophilic toluene-degrading consortium (TDC) and an ethylbenzene-degrading consortium (EDC) were established under sulfate-reducing conditions. These consortia were first characterized by denaturing gradient gel electrophoresis (DGGE) fingerprinting of PCR-amplified 16S rRNA gene fragments, followed by sequencing. The sequences of the major bands (T-1 and E-2) belonging to TDC and EDC, respectively, were affiliated with the family Desulfobacteriaceae. Another major band from EDC (E-1) was related to an uncultured non-sulfate-reducing soil bacterium. Oligonucleotide probes specific for the 16S rRNAs of target organisms corresponding to T-1, E-1, and E-2 were designed, and hybridization conditions were optimized for two analytical formats, membrane and DNA microarray hybridization. Both formats were used to characterize the TDC and EDC, and the results of both were consistent with DGGE analysis. In order to assess the utility of the microarray format for analysis of environmental samples, oil-contaminated sediments from the coast of Kuwait were analyzed. The DNA microarray successfully detected bacterial nucleic acids from these samples, but probes targeting specific groups of sulfate-reducing bacteria did not give positive signals. The results of this study demonstrate the limitations and the potential utility of DNA microarrays for microbial community analysis. PMID:12088997

A 256 pixel magnetoresistive biosensor microarray in 0.18μm CMOS

PubMed Central

Hall, Drew A.; Gaster, Richard S.; Makinwa, Kofi; Wang, Shan X.; Murmann, Boris

2014-01-01

Magnetic nanotechnologies have shown significant potential in several areas of nanomedicine such as imaging, therapeutics, and early disease detection. Giant magnetoresistive spin-valve (GMR SV) sensors coupled with magnetic nanotags (MNTs) possess great promise as ultra-sensitive biosensors for diagnostics. We report an integrated sensor interface for an array of 256 GMR SV biosensors designed in 0.18 μm CMOS. Arranged like an imager, each of the 16 column level readout channels contains an analog front- end and a compact ΣΔ modulator (0.054 mm2) with 84 dB of dynamic range and an input referred noise of 49 nT/√Hz. Performance is demonstrated through detection of an ovarian cancer biomarker, secretory leukocyte peptidase inhibitor (SLPI), spiked at concentrations as low as 10 fM. This system is designed as a replacement for optical protein microarrays while also providing real-time kinetics monitoring. PMID:24761029

Design of an ultrasonic micro-array for near field sensing during retinal microsurgery.

PubMed

Clarke, Clyde; Etienne-Cummings, Ralph

2006-01-01

A method for obtaining the optimal and specific sensor parameters for a tool-tip mountable ultrasonic transducer micro-array is presented. The ultrasonic transducer array sensor parameters, such as frequency of operation, element size, inter-element spacing, number of elements and transducer geometry are obtained using a quadratic programming method to obtain a maximum directivity while being constrained to a total array size of 4 mm2 and the required resolution for retinal imaging. The technique is used to design a uniformly spaced NxN transducer array that is capable of resolving structures in the retina that are as small as 2 microm from a distance of 100 microm. The resultant 37x37 array of 16 microm transducers with 26 microm spacing will be realized as a Capacitive Micromachined Ultrasonic Transducer (CMUT) array and used for imaging and robotic guidance during retinal microsurgery.

Image Analysis Algorithms for Immunohistochemical Assessment of Cell Death Events and Fibrosis in Tissue Sections

PubMed Central

Krajewska, Maryla; Smith, Layton H.; Rong, Juan; Huang, Xianshu; Hyer, Marc L.; Zeps, Nikolajs; Iacopetta, Barry; Linke, Steven P.; Olson, Allen H.; Reed, John C.; Krajewski, Stan

2009-01-01

Cell death is of broad physiological and pathological importance, making quantification of biochemical events associated with cell demise a high priority for experimental pathology. Fibrosis is a common consequence of tissue injury involving necrotic cell death. Using tissue specimens from experimental mouse models of traumatic brain injury, cardiac fibrosis, and cancer, as well as human tumor specimens assembled in tissue microarray (TMA) format, we undertook computer-assisted quantification of specific immunohistochemical and histological parameters that characterize processes associated with cell death. In this study, we demonstrated the utility of image analysis algorithms for color deconvolution, colocalization, and nuclear morphometry to characterize cell death events in tissue specimens: (a) subjected to immunostaining for detecting cleaved caspase-3, cleaved poly(ADP-ribose)-polymerase, cleaved lamin-A, phosphorylated histone H2AX, and Bcl-2; (b) analyzed by terminal deoxyribonucleotidyl transferase–mediated dUTP nick end labeling assay to detect DNA fragmentation; and (c) evaluated with Masson's trichrome staining. We developed novel algorithm-based scoring methods and validated them using TMAs as a high-throughput format. The proposed computer-assisted scoring methods for digital images by brightfield microscopy permit linear quantification of immunohistochemical and histochemical stainings. Examples are provided of digital image analysis performed in automated or semiautomated fashion for successful quantification of molecular events associated with cell death in tissue sections. (J Histochem Cytochem 57:649–663, 2009) PMID:19289554

Assessment of Chitosan-Affected Metabolic Response by Peroxisome Proliferator-Activated Receptor Bioluminescent Imaging-Guided Transcriptomic Analysis

PubMed Central

Kao, Chia-Hung; Hsiang, Chien-Yun; Ho, Tin-Yun

2012-01-01

Chitosan has been widely used in food industry as a weight-loss aid and a cholesterol-lowering agent. Previous studies have shown that chitosan affects metabolic responses and contributes to anti-diabetic, hypocholesteremic, and blood glucose-lowering effects; however, the in vivo targeting sites and mechanisms of chitosan remain to be clarified. In this study, we constructed transgenic mice, which carried the luciferase genes driven by peroxisome proliferator-activated receptor (PPAR), a key regulator of fatty acid and glucose metabolism. Bioluminescent imaging of PPAR transgenic mice was applied to report the organs that chitosan acted on, and gene expression profiles of chitosan-targeted organs were further analyzed to elucidate the mechanisms of chitosan. Bioluminescent imaging showed that constitutive PPAR activities were detected in brain and gastrointestinal tract. Administration of chitosan significantly activated the PPAR activities in brain and stomach. Microarray analysis of brain and stomach showed that several pathways involved in lipid and glucose metabolism were regulated by chitosan. Moreover, the expression levels of metabolism-associated genes like apolipoprotein B (apoB) and ghrelin genes were down-regulated by chitosan. In conclusion, these findings suggested the feasibility of PPAR bioluminescent imaging-guided transcriptomic analysis on the evaluation of chitosan-affected metabolic responses in vivo. Moreover, we newly identified that downregulated expression of apoB and ghrelin genes were novel mechanisms for chitosan-affected metabolic responses in vivo. PMID:22496881

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation.

PubMed

Richard, Arianne C; Lyons, Paul A; Peters, James E; Biasci, Daniele; Flint, Shaun M; Lee, James C; McKinney, Eoin F; Siegel, Richard M; Smith, Kenneth G C

2014-08-04

Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study. Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this "gold-standard" comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues. Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

Genome Expression Pathway Analysis Tool – Analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context

PubMed Central

Weniger, Markus; Engelmann, Julia C; Schultz, Jörg

2007-01-01

Background Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation. Results We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at . Conclusion GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at . PMID:17543125

Development of DNA Microarrays for Metabolic Pathway and Bioprocess Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gregory Stephanopoulos

Transcriptional profiling experiments utilizing DNA microarrays to study the intracellular accumulation of PHB in Synechocystis has proved difficult in large part because strains that show significant differences in PHB which would justify global analysis of gene expression have not been isolated.

High-throughput quantum cascade laser (QCL) spectral histopathology: a practical approach towards clinical translation.

PubMed

Pilling, Michael J; Henderson, Alex; Bird, Benjamin; Brown, Mick D; Clarke, Noel W; Gardner, Peter

2016-06-23

Infrared microscopy has become one of the key techniques in the biomedical research field for interrogating tissue. In partnership with multivariate analysis and machine learning techniques, it has become widely accepted as a method that can distinguish between normal and cancerous tissue with both high sensitivity and high specificity. While spectral histopathology (SHP) is highly promising for improved clinical diagnosis, several practical barriers currently exist, which need to be addressed before successful implementation in the clinic. Sample throughput and speed of acquisition are key barriers and have been driven by the high volume of samples awaiting histopathological examination. FTIR chemical imaging utilising FPA technology is currently state-of-the-art for infrared chemical imaging, and recent advances in its technology have dramatically reduced acquisition times. Despite this, infrared microscopy measurements on a tissue microarray (TMA), often encompassing several million spectra, takes several hours to acquire. The problem lies with the vast quantities of data that FTIR collects; each pixel in a chemical image is derived from a full infrared spectrum, itself composed of thousands of individual data points. Furthermore, data management is quickly becoming a barrier to clinical translation and poses the question of how to store these incessantly growing data sets. Recently, doubts have been raised as to whether the full spectral range is actually required for accurate disease diagnosis using SHP. These studies suggest that once spectral biomarkers have been predetermined it may be possible to diagnose disease based on a limited number of discrete spectral features. In this current study, we explore the possibility of utilising discrete frequency chemical imaging for acquiring high-throughput, high-resolution chemical images. Utilising a quantum cascade laser imaging microscope with discrete frequency collection at key diagnostic wavelengths, we demonstrate that we can diagnose prostate cancer with high sensitivity and specificity. Finally we extend the study to a large patient dataset utilising tissue microarrays, and show that high sensitivity and specificity can be achieved using high-throughput, rapid data collection, thereby paving the way for practical implementation in the clinic.

Gene expression profiling in gill tissues of White spot syndrome virus infected black tiger shrimp Penaeus monodon by DNA microarray.

PubMed

Shekhar, M S; Gomathi, A; Gopikrishna, G; Ponniah, A G

2015-06-01

White spot syndrome virus (WSSV) continues to be the most devastating viral pathogen infecting penaeid shrimp the world over. The genome of WSSV has been deciphered and characterized from three geographical isolates and significant progress has been made in developing various molecular diagnostic methods to detect the virus. However, the information on host immune gene response to WSSV pathogenesis is limited. Microarray analysis was carried out as an approach to analyse the gene expression in black tiger shrimp Penaeus monodon in response to WSSV infection. Gill tissues collected from the WSSV infected shrimp at 6, 24, 48 h and moribund stage were analysed for differential gene expression. Shrimp cDNAs of 40,059 unique sequences were considered for designing the microarray chip. The Cy3-labeled cRNA derived from healthy and WSSV-infected shrimp was subjected to hybridization with all the DNA spots in the microarray which revealed 8,633 and 11,147 as up- and down-regulated genes respectively at different time intervals post infection. The altered expression of these numerous genes represented diverse functions such as immune response, osmoregulation, apoptosis, nucleic acid binding, energy and metabolism, signal transduction, stress response and molting. The changes in gene expression profiles observed by microarray analysis provides molecular insights and framework of genes which are up- and down-regulated at different time intervals during WSSV infection in shrimp. The microarray data was validated by Real Time analysis of four differentially expressed genes involved in apoptosis (translationally controlled tumor protein, inhibitor of apoptosis protein, ubiquitin conjugated enzyme E2 and caspase) for gene expression levels. The role of apoptosis related genes in WSSV infected shrimp is discussed herein.

Reuse of imputed data in microarray analysis increases imputation efficiency

PubMed Central

Kim, Ki-Yeol; Kim, Byoung-Jin; Yi, Gwan-Su

2004-01-01

Background The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked. Results We developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments. Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets. Conclusions Sequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data. PMID:15504240

Detection of clonal evolution in hematopoietic malignancies by combining comparative genomic hybridization and single nucleotide polymorphism arrays.

PubMed

Hartmann, Luise; Stephenson, Christine F; Verkamp, Stephanie R; Johnson, Krystal R; Burnworth, Bettina; Hammock, Kelle; Brodersen, Lisa Eidenschink; de Baca, Monica E; Wells, Denise A; Loken, Michael R; Zehentner, Barbara K

2014-12-01

Array comparative genomic hybridization (aCGH) has become a powerful tool for analyzing hematopoietic neoplasms and identifying genome-wide copy number changes in a single assay. aCGH also has superior resolution compared with fluorescence in situ hybridization (FISH) or conventional cytogenetics. Integration of single nucleotide polymorphism (SNP) probes with microarray analysis allows additional identification of acquired uniparental disomy, a copy neutral aberration with known potential to contribute to tumor pathogenesis. However, a limitation of microarray analysis has been the inability to detect clonal heterogeneity in a sample. This study comprised 16 samples (acute myeloid leukemia, myelodysplastic syndrome, chronic lymphocytic leukemia, plasma cell neoplasm) with complex cytogenetic features and evidence of clonal evolution. We used an integrated manual peak reassignment approach combining analysis of aCGH and SNP microarray data for characterization of subclonal abnormalities. We compared array findings with results obtained from conventional cytogenetic and FISH studies. Clonal heterogeneity was detected in 13 of 16 samples by microarray on the basis of log2 values. Use of the manual peak reassignment analysis approach improved resolution of the sample's clonal composition and genetic heterogeneity in 10 of 13 (77%) patients. Moreover, in 3 patients, clonal disease progression was revealed by array analysis that was not evident by cytogenetic or FISH studies. Genetic abnormalities originating from separate clonal subpopulations can be identified and further characterized by combining aCGH and SNP hybridization results from 1 integrated microarray chip by use of the manual peak reassignment technique. Its clinical utility in comparison to conventional cytogenetic or FISH studies is demonstrated. © 2014 American Association for Clinical Chemistry.

Analysis and modelling of septic shock microarray data using Singular Value Decomposition.

PubMed

Allanki, Srinivas; Dixit, Madhulika; Thangaraj, Paul; Sinha, Nandan Kumar

2017-06-01

Being a high throughput technique, enormous amounts of microarray data has been generated and there arises a need for more efficient techniques of analysis, in terms of speed and accuracy. Finding the differentially expressed genes based on just fold change and p-value might not extract all the vital biological signals that occur at a lower gene expression level. Besides this, numerous mathematical models have been generated to predict the clinical outcome from microarray data, while very few, if not none, aim at predicting the vital genes that are important in a disease progression. Such models help a basic researcher narrow down and concentrate on a promising set of genes which leads to the discovery of gene-based therapies. In this article, as a first objective, we have used the lesser known and used Singular Value Decomposition (SVD) technique to build a microarray data analysis tool that works with gene expression patterns and intrinsic structure of the data in an unsupervised manner. We have re-analysed a microarray data over the clinical course of Septic shock from Cazalis et al. (2014) and have shown that our proposed analysis provides additional information compared to the conventional method. As a second objective, we developed a novel mathematical model that predicts a set of vital genes in the disease progression that works by generating samples in the continuum between health and disease, using a simple normal-distribution-based random number generator. We also verify that most of the predicted genes are indeed related to septic shock. Copyright © 2017 Elsevier Inc. All rights reserved.

Biomarkers of the Hedgehog/Smoothened pathway in healthy volunteers

PubMed Central

Kadam, Sunil K; Patel, Bharvin K R; Jones, Emma; Nguyen, Tuan S; Verma, Lalit K; Landschulz, Katherine T; Stepaniants, Sergey; Li, Bin; Brandt, John T; Brail, Leslie H

2012-01-01

The Hedgehog (Hh) pathway is involved in oncogenic transformation and tumor maintenance. The primary objective of this study was to select surrogate tissue to measure messenger ribonucleic acid (mRNA) levels of Hh pathway genes for measurement of pharmacodynamic effect. Expression of Hh pathway specific genes was measured by quantitative real time polymerase chain reaction (qRT-PCR) and global gene expression using Affymetrix U133 microarrays. Correlations were made between the expression of specific genes determined by qRT-PCR and normalized microarray data. Gene ontology analysis using microarray data for a broader set of Hh pathway genes was performed to identify additional Hh pathway-related markers in the surrogate tissue. RNA extracted from blood, hair follicle, and skin obtained from healthy subjects was analyzed by qRT-PCR for 31 genes, whereas 8 samples were analyzed for a 7-gene subset. Twelve sample sets, each with ≤500 ng total RNA derived from hair, skin, and blood, were analyzed using Affymetrix U133 microarrays. Transcripts for several Hh pathway genes were undetectable in blood using qRT-PCR. Skin was the most desirable matrix, followed by hair follicle. Whether processed by robust multiarray average or microarray suite 5 (MAS5), expression patterns of individual samples showed co-clustered signals; both normalization methods were equally effective for unsupervised analysis. The MAS5- normalized probe sets appeared better suited for supervised analysis. This work provides the basis for selection of a surrogate tissue and an expression analysis-based approach to evaluate pathway-related genes as markers of pharmacodynamic effect with novel inhibitors of the Hh pathway. PMID:22611475

A novel approach for human whole transcriptome analysis based on absolute gene expression of microarray data

PubMed Central

Bikel, Shirley; Jacobo-Albavera, Leonor; Sánchez-Muñoz, Fausto; Cornejo-Granados, Fernanda; Canizales-Quinteros, Samuel; Soberón, Xavier; Sotelo-Mundo, Rogerio R.; del Río-Navarro, Blanca E.; Mendoza-Vargas, Alfredo; Sánchez, Filiberto

2017-01-01

Background In spite of the emergence of RNA sequencing (RNA-seq), microarrays remain in widespread use for gene expression analysis in the clinic. There are over 767,000 RNA microarrays from human samples in public repositories, which are an invaluable resource for biomedical research and personalized medicine. The absolute gene expression analysis allows the transcriptome profiling of all expressed genes under a specific biological condition without the need of a reference sample. However, the background fluorescence represents a challenge to determine the absolute gene expression in microarrays. Given that the Y chromosome is absent in female subjects, we used it as a new approach for absolute gene expression analysis in which the fluorescence of the Y chromosome genes of female subjects was used as the background fluorescence for all the probes in the microarray. This fluorescence was used to establish an absolute gene expression threshold, allowing the differentiation between expressed and non-expressed genes in microarrays. Methods We extracted the RNA from 16 children leukocyte samples (nine males and seven females, ages 6–10 years). An Affymetrix Gene Chip Human Gene 1.0 ST Array was carried out for each sample and the fluorescence of 124 genes of the Y chromosome was used to calculate the absolute gene expression threshold. After that, several expressed and non-expressed genes according to our absolute gene expression threshold were compared against the expression obtained using real-time quantitative polymerase chain reaction (RT-qPCR). Results From the 124 genes of the Y chromosome, three genes (DDX3Y, TXLNG2P and EIF1AY) that displayed significant differences between sexes were used to calculate the absolute gene expression threshold. Using this threshold, we selected 13 expressed and non-expressed genes and confirmed their expression level by RT-qPCR. Then, we selected the top 5% most expressed genes and found that several KEGG pathways were significantly enriched. Interestingly, these pathways were related to the typical functions of leukocytes cells, such as antigen processing and presentation and natural killer cell mediated cytotoxicity. We also applied this method to obtain the absolute gene expression threshold in already published microarray data of liver cells, where the top 5% expressed genes showed an enrichment of typical KEGG pathways for liver cells. Our results suggest that the three selected genes of the Y chromosome can be used to calculate an absolute gene expression threshold, allowing a transcriptome profiling of microarray data without the need of an additional reference experiment. Discussion Our approach based on the establishment of a threshold for absolute gene expression analysis will allow a new way to analyze thousands of microarrays from public databases. This allows the study of different human diseases without the need of having additional samples for relative expression experiments. PMID:29230367

DigOut: viewing differential expression genes as outliers.

PubMed

Yu, Hui; Tu, Kang; Xie, Lu; Li, Yuan-Yuan

2010-12-01

With regards to well-replicated two-conditional microarray datasets, the selection of differentially expressed (DE) genes is a well-studied computational topic, but for multi-conditional microarray datasets with limited or no replication, the same task is not properly addressed by previous studies. This paper adopts multivariate outlier analysis to analyze replication-lacking multi-conditional microarray datasets, finding that it performs significantly better than the widely used limit fold change (LFC) model in a simulated comparative experiment. Compared with the LFC model, the multivariate outlier analysis also demonstrates improved stability against sample variations in a series of manipulated real expression datasets. The reanalysis of a real non-replicated multi-conditional expression dataset series leads to satisfactory results. In conclusion, a multivariate outlier analysis algorithm, like DigOut, is particularly useful for selecting DE genes from non-replicated multi-conditional gene expression dataset.

A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt Lymphoma cells.

PubMed

Wolff, Alexander; Bayerlová, Michaela; Gaedcke, Jochen; Kube, Dieter; Beißbarth, Tim

2018-01-01

Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies for the dataset at hand. Such pipelines for RNA-Seq data should include mapping of reads, counting and differential gene expression analysis or preprocessing, normalization and differential gene expression in case of microarray analysis, in order to give a global insight into pipeline performances. Four commonly used RNA-Seq pipelines (STAR/HTSeq-Count/edgeR, STAR/RSEM/edgeR, Sailfish/edgeR, TopHat2/Cufflinks/CuffDiff)) were investigated on multiple levels (alignment and counting) and cross-compared with the microarray counterpart on the level of gene expression and gene ontology enrichment. For these comparisons we generated two matched microarray and RNA-Seq datasets: Burkitt Lymphoma cell line data and rectal cancer patient data. The overall mapping rate of STAR was 98.98% for the cell line dataset and 98.49% for the patient dataset. Tophat's overall mapping rate was 97.02% and 96.73%, respectively, while Sailfish had only an overall mapping rate of 84.81% and 54.44%. The correlation of gene expression in microarray and RNA-Seq data was moderately worse for the patient dataset (ρ = 0.67-0.69) than for the cell line dataset (ρ = 0.87-0.88). An exception were the correlation results of Cufflinks, which were substantially lower (ρ = 0.21-0.29 and 0.34-0.53). For both datasets we identified very low numbers of differentially expressed genes using the microarray platform. For RNA-Seq we checked the agreement of differentially expressed genes identified in the different pipelines and of GO-term enrichment results. In conclusion the combination of STAR aligner with HTSeq-Count followed by STAR aligner with RSEM and Sailfish generated differentially expressed genes best suited for the dataset at hand and in agreement with most of the other transcriptomics pipelines.

Prediction of regulatory gene pairs using dynamic time warping and gene ontology.

PubMed

Yang, Andy C; Hsu, Hui-Huang; Lu, Ming-Da; Tseng, Vincent S; Shih, Timothy K

2014-01-01

Selecting informative genes is the most important task for data analysis on microarray gene expression data. In this work, we aim at identifying regulatory gene pairs from microarray gene expression data. However, microarray data often contain multiple missing expression values. Missing value imputation is thus needed before further processing for regulatory gene pairs becomes possible. We develop a novel approach to first impute missing values in microarray time series data by combining k-Nearest Neighbour (KNN), Dynamic Time Warping (DTW) and Gene Ontology (GO). After missing values are imputed, we then perform gene regulation prediction based on our proposed DTW-GO distance measurement of gene pairs. Experimental results show that our approach is more accurate when compared with existing missing value imputation methods on real microarray data sets. Furthermore, our approach can also discover more regulatory gene pairs that are known in the literature than other methods.

Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

PubMed Central

Sysoev, Victor V.; Kiselev, Ilya; Frietsch, Markus; Goschnick, Joachim

2004-01-01

The paper presents results concerning the effect of spatial inhomogeneous operating temperature on the gas discrimination power of a gas-sensor microarray, with the latter based on a thin SnO2 film employed in the KAMINA electronic nose. Three different temperature distributions over the substrate are discussed: a nearly homogeneous one and two temperature gradients, equal to approx. 3.3 °C/mm and 6.7 °C/mm, applied across the sensor elements (segments) of the array. The gas discrimination power of the microarray is judged by using the Mahalanobis distance in the LDA (Linear Discrimination Analysis) coordinate system between the data clusters obtained by the response of the microarray to four target vapors: ethanol, acetone, propanol and ammonia. It is shown that the application of a temperature gradient increases the gas discrimination power of the microarray by up to 35 %.

Chondrocyte channel transcriptomics

PubMed Central

Lewis, Rebecca; May, Hannah; Mobasheri, Ali; Barrett-Jolley, Richard

2013-01-01

To date, a range of ion channels have been identified in chondrocytes using a number of different techniques, predominantly electrophysiological and/or biomolecular; each of these has its advantages and disadvantages. Here we aim to compare and contrast the data available from biophysical and microarray experiments. This letter analyses recent transcriptomics datasets from chondrocytes, accessible from the European Bioinformatics Institute (EBI). We discuss whether such bioinformatic analysis of microarray datasets can potentially accelerate identification and discovery of ion channels in chondrocytes. The ion channels which appear most frequently across these microarray datasets are discussed, along with their possible functions. We discuss whether functional or protein data exist which support the microarray data. A microarray experiment comparing gene expression in osteoarthritis and healthy cartilage is also discussed and we verify the differential expression of 2 of these genes, namely the genes encoding large calcium-activated potassium (BK) and aquaporin channels. PMID:23995703

Integration of Network Biology and Imaging to Study Cancer Phenotypes and Responses.

PubMed

Tian, Ye; Wang, Sean S; Zhang, Zhen; Rodriguez, Olga C; Petricoin, Emanuel; Shih, Ie-Ming; Chan, Daniel; Avantaggiati, Maria; Yu, Guoqiang; Ye, Shaozhen; Clarke, Robert; Wang, Chao; Zhang, Bai; Wang, Yue; Albanese, Chris

2014-01-01

Ever growing "omics" data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations. Here we introduced a novel integration of network biology and imaging to study cancer phenotypes and responses to treatments at the molecular systems level. Specifically, Differential Dependence Network (DDN) analysis was used to detect statistically significant topological rewiring in molecular networks between two phenotypic conditions, and in vivo Magnetic Resonance Imaging (MRI) was used to more accurately define phenotypic sample groups for such differential analysis. We applied DDN to analyze two distinct phenotypic groups of breast cancer and study how genomic instability affects the molecular network topologies in high-grade ovarian cancer. Further, FDA-approved arsenic trioxide (ATO) and the ND2-SmoA1 mouse model of Medulloblastoma (MB) were used to extend our analyses of combined MRI and Reverse Phase Protein Microarray (RPMA) data to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology.

Transcriptome Profiling of In-Vivo Produced Bovine Pre-implantation Embryos Using Two-color Microarray Platform.

PubMed

Salehi, Reza; Tsoi, Stephen C M; Colazo, Marcos G; Ambrose, Divakar J; Robert, Claude; Dyck, Michael K

2017-01-30

Early embryonic loss is a large contributor to infertility in cattle. Moreover, bovine becomes an interesting model to study human preimplantation embryo development due to their similar developmental process. Although genetic factors are known to affect early embryonic development, the discovery of such factors has been a serious challenge. Microarray technology allows quantitative measurement and gene expression profiling of transcript levels on a genome-wide basis. One of the main decisions that have to be made when planning a microarray experiment is whether to use a one- or two-color approach. Two-color design increases technical replication, minimizes variability, improves sensitivity and accuracy as well as allows having loop designs, defining the common reference samples. Although microarray is a powerful biological tool, there are potential pitfalls that can attenuate its power. Hence, in this technical paper we demonstrate an optimized protocol for RNA extraction, amplification, labeling, hybridization of the labeled amplified RNA to the array, array scanning and data analysis using the two-color analysis strategy.

Clustering gene expression data based on predicted differential effects of GV interaction.

PubMed

Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

2005-02-01

Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images.

PubMed

Spagnolo, Daniel M; Al-Kofahi, Yousef; Zhu, Peihong; Lezon, Timothy R; Gough, Albert; Stern, Andrew M; Lee, Adrian V; Ginty, Fiona; Sarachan, Brion; Taylor, D Lansing; Chennubhotla, S Chakra

2017-11-01

We introduce THRIVE (Tumor Heterogeneity Research Interactive Visualization Environment), an open-source tool developed to assist cancer researchers in interactive hypothesis testing. The focus of this tool is to quantify spatial intratumoral heterogeneity (ITH), and the interactions between different cell phenotypes and noncellular constituents. Specifically, we foresee applications in phenotyping cells within tumor microenvironments, recognizing tumor boundaries, identifying degrees of immune infiltration and epithelial/stromal separation, and identification of heterotypic signaling networks underlying microdomains. The THRIVE platform provides an integrated workflow for analyzing whole-slide immunofluorescence images and tissue microarrays, including algorithms for segmentation, quantification, and heterogeneity analysis. THRIVE promotes flexible deployment, a maintainable code base using open-source libraries, and an extensible framework for customizing algorithms with ease. THRIVE was designed with highly multiplexed immunofluorescence images in mind, and, by providing a platform to efficiently analyze high-dimensional immunofluorescence signals, we hope to advance these data toward mainstream adoption in cancer research. Cancer Res; 77(21); e71-74. ©2017 AACR . ©2017 American Association for Cancer Research.

GenePublisher: Automated analysis of DNA microarray data.

PubMed

Knudsen, Steen; Workman, Christopher; Sicheritz-Ponten, Thomas; Friis, Carsten

2003-07-01

GenePublisher, a system for automatic analysis of data from DNA microarray experiments, has been implemented with a web interface at http://www.cbs.dtu.dk/services/GenePublisher. Raw data are uploaded to the server together with a specification of the data. The server performs normalization, statistical analysis and visualization of the data. The results are run against databases of signal transduction pathways, metabolic pathways and promoter sequences in order to extract more information. The results of the entire analysis are summarized in report form and returned to the user.

Microarray characterization of gene expression changes in blood during acute ethanol exposure

PubMed Central

2013-01-01

Background As part of the civil aviation safety program to define the adverse effects of ethanol on flying performance, we performed a DNA microarray analysis of human whole blood samples from a five-time point study of subjects administered ethanol orally, followed by breathalyzer analysis, to monitor blood alcohol concentration (BAC) to discover significant gene expression changes in response to the ethanol exposure. Methods Subjects were administered either orange juice or orange juice with ethanol. Blood samples were taken based on BAC and total RNA was isolated from PaxGene™ blood tubes. The amplified cDNA was used in microarray and quantitative real-time polymerase chain reaction (RT-qPCR) analyses to evaluate differential gene expression. Microarray data was analyzed in a pipeline fashion to summarize and normalize and the results evaluated for relative expression across time points with multiple methods. Candidate genes showing distinctive expression patterns in response to ethanol were clustered by pattern and further analyzed for related function, pathway membership and common transcription factor binding within and across clusters. RT-qPCR was used with representative genes to confirm relative transcript levels across time to those detected in microarrays. Results Microarray analysis of samples representing 0%, 0.04%, 0.08%, return to 0.04%, and 0.02% wt/vol BAC showed that changes in gene expression could be detected across the time course. The expression changes were verified by qRT-PCR. The candidate genes of interest (GOI) identified from the microarray analysis and clustered by expression pattern across the five BAC points showed seven coordinately expressed groups. Analysis showed function-based networks, shared transcription factor binding sites and signaling pathways for members of the clusters. These include hematological functions, innate immunity and inflammation functions, metabolic functions expected of ethanol metabolism, and pancreatic and hepatic function. Five of the seven clusters showed links to the p38 MAPK pathway. Conclusions The results of this study provide a first look at changing gene expression patterns in human blood during an acute rise in blood ethanol concentration and its depletion because of metabolism and excretion, and demonstrate that it is possible to detect changes in gene expression using total RNA isolated from whole blood. The analysis approach for this study serves as a workflow to investigate the biology linked to expression changes across a time course and from these changes, to identify target genes that could serve as biomarkers linked to pilot performance. PMID:23883607

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies

PubMed Central

Zlobec, Inti; Suter, Guido; Perren, Aurel; Lugli, Alessandro

2014-01-01

Biomarker research relies on tissue microarrays (TMA). TMAs are produced by repeated transfer of small tissue cores from a ‘donor’ block into a ‘recipient’ block and then used for a variety of biomarker applications. The construction of conventional TMAs is labor intensive, imprecise, and time-consuming. Here, a protocol using next-generation Tissue Microarrays (ngTMA) is outlined. ngTMA is based on TMA planning and design, digital pathology, and automated tissue microarraying. The protocol is illustrated using an example of 134 metastatic colorectal cancer patients. Histological, statistical and logistical aspects are considered, such as the tissue type, specific histological regions, and cell types for inclusion in the TMA, the number of tissue spots, sample size, statistical analysis, and number of TMA copies. Histological slides for each patient are scanned and uploaded onto a web-based digital platform. There, they are viewed and annotated (marked) using a 0.6-2.0 mm diameter tool, multiple times using various colors to distinguish tissue areas. Donor blocks and 12 ‘recipient’ blocks are loaded into the instrument. Digital slides are retrieved and matched to donor block images. Repeated arraying of annotated regions is automatically performed resulting in an ngTMA. In this example, six ngTMAs are planned containing six different tissue types/histological zones. Two copies of the ngTMAs are desired. Three to four slides for each patient are scanned; 3 scan runs are necessary and performed overnight. All slides are annotated; different colors are used to represent the different tissues/zones, namely tumor center, invasion front, tumor/stroma, lymph node metastases, liver metastases, and normal tissue. 17 annotations/case are made; time for annotation is 2-3 min/case. 12 ngTMAs are produced containing 4,556 spots. Arraying time is 15-20 hr. Due to its precision, flexibility and speed, ngTMA is a powerful tool to further improve the quality of TMAs used in clinical and translational research. PMID:25285857

Multiple epitope presentation and surface density control enabled by chemoselective immobilization lead to enhanced performance in IgE-binding fingerprinting on peptide microarrays.

PubMed

Gori, Alessandro; Cretich, Marina; Vanna, Renzo; Sola, Laura; Gagni, Paola; Bruni, Giulia; Liprino, Marta; Gramatica, Furio; Burastero, Samuele; Chiari, Marcella

2017-08-29

Multiple ligand presentation is a powerful strategy to enhance the affinity of a probe for its corresponding target. A promising application of this concept lies in the analytical field, where surface immobilized probes interact with their corresponding targets in the context of complex biological samples. Here we investigate the effect of multiple epitope presentation (MEP) in the challenging context of IgE-detection in serum samples using peptide microarrays, and evaluate the influence of probes surface density on the assay results. Using the milk allergen alpha-lactalbumin as a model, we have synthesized three immunoreactive epitope sequences in a linear, branched and tandem form and exploited a chemoselective click strategy (CuAAC) for their immobilization on the surface of two biosensors, a microarray and an SPR chip both modified with the same clickable polymeric coating. We first demonstrated that a fine tuning of the surface peptide density plays a crucial role to fully exploit the potential of oriented and multiple peptide display. We then compared the three multiple epitope presentations in a microarray assay using sera samples from milk allergic patients, confirming that a multiple presentation, in particular that of the tandem construct, allows for a more efficient characterization of IgE-binding fingerprints at a statistically significant level. To gain insights on the binding parameters that characterize antibody/epitopes affinity, we selected the most reactive epitope of the series (LAC1) and performed a Surface Plasmon Resonance Imaging (SPRi) analysis comparing different epitope architectures (linear versus branched versus tandem). We demonstrated that the tandem peptide provides an approximately twofold increased binding capacity with respect to the linear and branched peptides, that could be attributed to a lower rate of dissociation (K d ). Copyright © 2017 Elsevier B.V. All rights reserved.

Single-Nucleotide Polymorphism-Microarray Ploidy Analysis of Paraffin-Embedded Products of Conception in Recurrent Pregnancy Loss Evaluations.

PubMed

Maslow, Bat-Sheva L; Budinetz, Tara; Sueldo, Carolina; Anspach, Erica; Engmann, Lawrence; Benadiva, Claudio; Nulsen, John C

2015-07-01

To compare the analysis of chromosome number from paraffin-embedded products of conception using single-nucleotide polymorphism (SNP) microarray with the recommended screening for the evaluation of couples presenting with recurrent pregnancy loss who do not have previous fetal cytogenetic data. We performed a retrospective cohort study including all women who presented for a new evaluation of recurrent pregnancy loss over a 2-year period (January 1, 2012, to December 31, 2013). All participants had at least two documented first-trimester losses and both the recommended screening tests and SNP microarray performed on at least one paraffin-embedded products of conception sample. Single-nucleotide polymorphism microarray identifies all 24 chromosomes (22 autosomes, X, and Y). Forty-two women with a total of 178 losses were included in the study. Paraffin-embedded products of conception from 62 losses were sent for SNP microarray. Single-nucleotide polymorphism microarray successfully diagnosed fetal chromosome number in 71% (44/62) of samples, of which 43% (19/44) were euploid and 57% (25/44) were noneuploid. Seven of 42 (17%) participants had abnormalities on recurrent pregnancy loss screening. The per-person detection rate for a cause of pregnancy loss was significantly higher in the SNP microarray (0.50; 95% confidence interval [CI] 0.36-0.64) compared with recurrent pregnancy loss evaluation (0.17; 95% CI 0.08-0.31) (P=.002). Participants with one or more euploid loss identified on paraffin-embedded products of conception were significantly more likely to have an abnormality on recurrent pregnancy loss screening than those with only noneuploid results (P=.028). The significance remained when controlling for age, number of losses, number of samples, and total pregnancies. These results suggest that SNP microarray testing of paraffin-embedded products of conception is a valuable tool for the evaluation of recurrent pregnancy loss in patients without prior fetal cytogenetic results. Recommended recurrent pregnancy loss screening was unnecessary in almost half the patients in our study. II.

eSensor: an electrochemical detection-based DNA microarray technology enabling sample-to-answer molecular diagnostics

NASA Astrophysics Data System (ADS)

Liu, Robin H.; Longiaru, Mathew

2009-05-01

DNA microarrays are becoming a widespread tool used in life science and drug screening due to its many benefits of miniaturization and integration. Microarrays permit a highly multiplexed DNA analysis. Recently, the development of new detection methods and simplified methodologies has rapidly expanded the use of microarray technologies from predominantly gene expression analysis into the arena of diagnostics. Osmetech's eSensor® is an electrochemical detection platform based on a low-to- medium density DNA hybridization array on a cost-effective printed circuit board substrate. eSensor® has been cleared by FDA for Warfarin sensitivity test and Cystic Fibrosis Carrier Detection. Other genetic-based diagnostic and infectious disease detection tests are under development. The eSensor® platform eliminates the need for an expensive laser-based optical system and fluorescent reagents. It allows one to perform hybridization and detection in a single and small instrument without any fluidic processing and handling. Furthermore, the eSensor® platform is readily adaptable to on-chip sample-to-answer genetic analyses using microfluidics technology. The eSensor® platform provides a cost-effective solution to direct sample-to-answer genetic analysis, and thus have a potential impact in the fields of point-of-care genetic analysis, environmental testing, and biological warfare agent detection.

Towards the integration, annotation and association of historical microarray experiments with RNA-seq.

PubMed

Chavan, Shweta S; Bauer, Michael A; Peterson, Erich A; Heuck, Christoph J; Johann, Donald J

2013-01-01

Transcriptome analysis by microarrays has produced important advances in biomedicine. For instance in multiple myeloma (MM), microarray approaches led to the development of an effective disease subtyping via cluster assignment, and a 70 gene risk score. Both enabled an improved molecular understanding of MM, and have provided prognostic information for the purposes of clinical management. Many researchers are now transitioning to Next Generation Sequencing (NGS) approaches and RNA-seq in particular, due to its discovery-based nature, improved sensitivity, and dynamic range. Additionally, RNA-seq allows for the analysis of gene isoforms, splice variants, and novel gene fusions. Given the voluminous amounts of historical microarray data, there is now a need to associate and integrate microarray and RNA-seq data via advanced bioinformatic approaches. Custom software was developed following a model-view-controller (MVC) approach to integrate Affymetrix probe set-IDs, and gene annotation information from a variety of sources. The tool/approach employs an assortment of strategies to integrate, cross reference, and associate microarray and RNA-seq datasets. Output from a variety of transcriptome reconstruction and quantitation tools (e.g., Cufflinks) can be directly integrated, and/or associated with Affymetrix probe set data, as well as necessary gene identifiers and/or symbols from a diversity of sources. Strategies are employed to maximize the annotation and cross referencing process. Custom gene sets (e.g., MM 70 risk score (GEP-70)) can be specified, and the tool can be directly assimilated into an RNA-seq pipeline. A novel bioinformatic approach to aid in the facilitation of both annotation and association of historic microarray data, in conjunction with richer RNA-seq data, is now assisting with the study of MM cancer biology.

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards.

PubMed

Bruland, Torunn; Anderssen, Endre; Doseth, Berit; Bergum, Hallgeir; Beisvag, Vidar; Laegreid, Astrid

2007-10-18

The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards. We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real. The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish.

Optimization of cDNA microarrays procedures using criteria that do not rely on external standards

PubMed Central

Bruland, Torunn; Anderssen, Endre; Doseth, Berit; Bergum, Hallgeir; Beisvag, Vidar; Lægreid, Astrid

2007-01-01

Background The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards. Results We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real. Conclusion The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish. PMID:17949480

ArraySolver: an algorithm for colour-coded graphical display and Wilcoxon signed-rank statistics for comparing microarray gene expression data.

PubMed

Khan, Haseeb Ahmad

2004-01-01

The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for transferring data from one platform to other. Various statistical methods, including the t-test, analysis of variance, Pearson test and Mann-Whitney U test, have been reported for comparing microarray data, whereas the utilization of the Wilcoxon signed-rank test, which is an appropriate test for two-groups comparison of gene expression data, has largely been neglected in microarray studies. The aim of this investigation was to build an integrated tool, ArraySolver, for colour-coded graphical display and comparison of gene expression data using the Wilcoxon signed-rank test. The results of software validation showed similar outputs with ArraySolver and SPSS for large datasets. Whereas the former program appeared to be more accurate for 25 or fewer pairs (n < or = 25), suggesting its potential application in analysing molecular signatures that usually contain small numbers of genes. The main advantages of ArraySolver are easy data selection, convenient report format, accurate statistics and the familiar Excel platform.

ArraySolver: An Algorithm for Colour-Coded Graphical Display and Wilcoxon Signed-Rank Statistics for Comparing Microarray Gene Expression Data

PubMed Central

2004-01-01

The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for transferring data from one platform to other. Various statistical methods, including the t-test, analysis of variance, Pearson test and Mann–Whitney U test, have been reported for comparing microarray data, whereas the utilization of the Wilcoxon signed-rank test, which is an appropriate test for two-groups comparison of gene expression data, has largely been neglected in microarray studies. The aim of this investigation was to build an integrated tool, ArraySolver, for colour-coded graphical display and comparison of gene expression data using the Wilcoxon signed-rank test. The results of software validation showed similar outputs with ArraySolver and SPSS for large datasets. Whereas the former program appeared to be more accurate for 25 or fewer pairs (n ≤ 25), suggesting its potential application in analysing molecular signatures that usually contain small numbers of genes. The main advantages of ArraySolver are easy data selection, convenient report format, accurate statistics and the familiar Excel platform. PMID:18629036

RECOVERING FILTER-BASED MICROARRAY DATA FOR PATHWAYS ANALYSIS USING A MULTIPOINT ALIGNMENT STRATEGY

EPA Science Inventory

The use of commercial microarrays are rapidly becoming the method of choice for profiling gene expression and assessing various disease states. Research Genetics has provided a series of well defined biological and software tools to the research community for these analyses. Th...

Microarray expression technology: from start to finish.

PubMed

Elvidge, Gareth

2006-01-01

The recent introduction of new microarray expression technologies and the further development of established platforms ensure that the researcher is presented with a range of options for performing an experiment. Whilst this has opened up the possibilities for future applications, such as exon-specific arrays, increased sample throughput and 'chromatin immunoprecipitation (ChIP) on chip' experiments, the initial decision processes and experiment planning are made more difficult. This review will give an overview of the various technologies that are available to perform a microarray expression experiment, from the initial planning stages through to the final data analysis. Both practical aspects and data analysis options will be considered. The relative advantages and disadvantages will be discussed with insights provided for future directions of the technology.

Dual-color Proteomic Profiling of Complex Samples with a Microarray of 810 Cancer-related Antibodies*

PubMed Central

Schröder, Christoph; Jacob, Anette; Tonack, Sarah; Radon, Tomasz P.; Sill, Martin; Zucknick, Manuela; Rüffer, Sven; Costello, Eithne; Neoptolemos, John P.; Crnogorac-Jurcevic, Tatjana; Bauer, Andrea; Fellenberg, Kurt; Hoheisel, Jörg D.

2010-01-01

Antibody microarrays have the potential to enable comprehensive proteomic analysis of small amounts of sample material. Here, protocols are presented for the production, quality assessment, and reproducible application of antibody microarrays in a two-color mode with an array of 1,800 features, representing 810 antibodies that were directed at 741 cancer-related proteins. In addition to measures of array quality, we implemented indicators for the accuracy and significance of dual-color detection. Dual-color measurements outperform a single-color approach concerning assay reproducibility and discriminative power. In the analysis of serum samples, depletion of high-abundance proteins did not improve technical assay quality. On the contrary, depletion introduced a strong bias in protein representation. In an initial study, we demonstrated the applicability of the protocols to proteins derived from urine samples. We identified differences between urine samples from pancreatic cancer patients and healthy subjects and between sexes. This study demonstrates that biomedically relevant data can be produced. As demonstrated by the thorough quality analysis, the dual-color antibody array approach proved to be competitive with other proteomic techniques and comparable in performance to transcriptional microarray analyses. PMID:20164060

Estimating differential expression from multiple indicators

PubMed Central

Ilmjärv, Sten; Hundahl, Christian Ansgar; Reimets, Riin; Niitsoo, Margus; Kolde, Raivo; Vilo, Jaak; Vasar, Eero; Luuk, Hendrik

2014-01-01

Regardless of the advent of high-throughput sequencing, microarrays remain central in current biomedical research. Conventional microarray analysis pipelines apply data reduction before the estimation of differential expression, which is likely to render the estimates susceptible to noise from signal summarization and reduce statistical power. We present a probe-level framework, which capitalizes on the high number of concurrent measurements to provide more robust differential expression estimates. The framework naturally extends to various experimental designs and target categories (e.g. transcripts, genes, genomic regions) as well as small sample sizes. Benchmarking in relation to popular microarray and RNA-sequencing data-analysis pipelines indicated high and stable performance on the Microarray Quality Control dataset and in a cell-culture model of hypoxia. Experimental-data-exhibiting long-range epigenetic silencing of gene expression was used to demonstrate the efficacy of detecting differential expression of genomic regions, a level of analysis not embraced by conventional workflows. Finally, we designed and conducted an experiment to identify hypothermia-responsive genes in terms of monotonic time-response. As a novel insight, hypothermia-dependent up-regulation of multiple genes of two major antioxidant pathways was identified and verified by quantitative real-time PCR. PMID:24586062

The effect of column purification on cDNA indirect labelling for microarrays

PubMed Central

Molas, M Lia; Kiss, John Z

2007-01-01

Background The success of the microarray reproducibility is dependent upon the performance of standardized procedures. Since the introduction of microarray technology for the analysis of global gene expression, reproducibility of results among different laboratories has been a major problem. Two of the main contributors to this variability are the use of different microarray platforms and different laboratory practices. In this paper, we address the latter question in terms of how variation in one of the steps of a labelling procedure affects the cDNA product prior to microarray hybridization. Results We used a standard procedure to label cDNA for microarray hybridization and employed different types of column chromatography for cDNA purification. After purifying labelled cDNA, we used the Agilent 2100 Bioanalyzer and agarose gel electrophoresis to assess the quality of the labelled cDNA before its hybridization onto a microarray platform. There were major differences in the cDNA profile (i.e. cDNA fragment lengths and abundance) as a result of using four different columns for purification. In addition, different columns have different efficiencies to remove rRNA contamination. This study indicates that the appropriate column to use in this type of protocol has to be experimentally determined. Finally, we present new evidence establishing the importance of testing the method of purification used during an indirect labelling procedure. Our results confirm the importance of assessing the quality of the sample in the labelling procedure prior to hybridization onto a microarray platform. Conclusion Standardization of column purification systems to be used in labelling procedures will improve the reproducibility of microarray results among different laboratories. In addition, implementation of a quality control check point of the labelled samples prior to microarray hybridization will prevent hybridizing a poor quality sample to expensive micorarrays. PMID:17597522

The effect of column purification on cDNA indirect labelling for microarrays.

PubMed

Molas, M Lia; Kiss, John Z

2007-06-27

The success of the microarray reproducibility is dependent upon the performance of standardized procedures. Since the introduction of microarray technology for the analysis of global gene expression, reproducibility of results among different laboratories has been a major problem. Two of the main contributors to this variability are the use of different microarray platforms and different laboratory practices. In this paper, we address the latter question in terms of how variation in one of the steps of a labelling procedure affects the cDNA product prior to microarray hybridization. We used a standard procedure to label cDNA for microarray hybridization and employed different types of column chromatography for cDNA purification. After purifying labelled cDNA, we used the Agilent 2100 Bioanalyzer and agarose gel electrophoresis to assess the quality of the labelled cDNA before its hybridization onto a microarray platform. There were major differences in the cDNA profile (i.e. cDNA fragment lengths and abundance) as a result of using four different columns for purification. In addition, different columns have different efficiencies to remove rRNA contamination. This study indicates that the appropriate column to use in this type of protocol has to be experimentally determined. Finally, we present new evidence establishing the importance of testing the method of purification used during an indirect labelling procedure. Our results confirm the importance of assessing the quality of the sample in the labelling procedure prior to hybridization onto a microarray platform. Standardization of column purification systems to be used in labelling procedures will improve the reproducibility of microarray results among different laboratories. In addition, implementation of a quality control check point of the labelled samples prior to microarray hybridization will prevent hybridizing a poor quality sample to expensive micorarrays.

Functional interaction analysis of GM1-related carbohydrates and Vibrio cholerae toxins using carbohydrate microarray.

PubMed

Kim, Chang Sup; Seo, Jeong Hyun; Cha, Hyung Joon

2012-08-07

The development of analytical tools is important for understanding the infection mechanisms of pathogenic bacteria or viruses. In the present work, a functional carbohydrate microarray combined with a fluorescence immunoassay was developed to analyze the interactions of Vibrio cholerae toxin (ctx) proteins and GM1-related carbohydrates. Ctx proteins were loaded onto the surface-immobilized GM1 pentasaccharide and six related carbohydrates, and their binding affinities were detected immunologically. The analysis of the ctx-carbohydrate interactions revealed that the intrinsic selectivity of ctx was GM1 pentasaccharide ≫ GM2 tetrasaccharide > asialo GM1 tetrasaccharide ≥ GM3trisaccharide, indicating that a two-finger grip formation and the terminal monosaccharides play important roles in the ctx-GM1 interaction. In addition, whole cholera toxin (ctxAB(5)) had a stricter substrate specificity and a stronger binding affinity than only the cholera toxin B subunit (ctxB). On the basis of the quantitative analysis, the carbohydrate microarray showed the sensitivity of detection of the ctxAB(5)-GM1 interaction with a limit-of-detection (LOD) of 2 ng mL(-1) (23 pM), which is comparable to other reported high sensitivity assay tools. In addition, the carbohydrate microarray successfully detected the actual toxin directly secreted from V. cholerae, without showing cross-reactivity to other bacteria. Collectively, these results demonstrate that the functional carbohydrate microarray is suitable for analyzing toxin protein-carbohydrate interactions and can be applied as a biosensor for toxin detection.

A universal reference sample derived from clone vector for improved detection of differential gene expression

PubMed Central

Khan, Rishi L; Gonye, Gregory E; Gao, Guang; Schwaber, James S

2006-01-01

Background Using microarrays by co-hybridizing two samples labeled with different dyes enables differential gene expression measurements and comparisons across slides while controlling for within-slide variability. Typically one dye produces weaker signal intensities than the other often causing signals to be undetectable. In addition, undetectable spots represent a large problem for two-color microarray designs and most arrays contain at least 40% undetectable spots even when labeled with reference samples such as Stratagene's Universal Reference RNAs™. Results We introduce a novel universal reference sample that produces strong signal for all spots on the array, increasing the average fraction of detectable spots to 97%. Maximizing detectable spots on the reference image channel also decreases the variability of microarray data allowing for reliable detection of smaller differential gene expression changes. The reference sample is derived from sequence contained in the parental EST clone vector pT7T3D-Pac and is called vector RNA (vRNA). We show that vRNA can also be used for quality control of microarray printing and PCR product quality, detection of hybridization anomalies, and simplification of spot finding and segmentation tasks. This reference sample can be made inexpensively in large quantities as a renewable resource that is consistent across experiments. Conclusion Results of this study show that vRNA provides a useful universal reference that yields high signal for almost all spots on a microarray, reduces variation and allows for comparisons between experiments and laboratories. Further, it can be used for quality control of microarray printing and PCR product quality, detection of hybridization anomalies, and simplification of spot finding and segmentation tasks. This type of reference allows for detection of small changes in differential expression while reference designs in general allow for large-scale multivariate experimental designs. vRNA in combination with reference designs enable systems biology microarray experiments of small physiologically relevant changes. PMID:16677381

Using computer assisted image analysis to determine the optimal Ki67 threshold for predicting outcome of invasive breast cancer

PubMed Central

Tay, Timothy Kwang Yong; Thike, Aye Aye; Pathmanathan, Nirmala; Jara-Lazaro, Ana Richelia; Iqbal, Jabed; Sng, Adeline Shi Hui; Ye, Heng Seow; Lim, Jeffrey Chun Tatt; Koh, Valerie Cui Yun; Tan, Jane Sie Yong; Yeong, Joe Poh Sheng; Chow, Zi Long; Li, Hui Hua; Cheng, Chee Leong; Tan, Puay Hoon

2018-01-01

Background Ki67 positivity in invasive breast cancers has an inverse correlation with survival outcomes and serves as an immunohistochemical surrogate for molecular subtyping of breast cancer, particularly ER positive breast cancer. The optimal threshold of Ki67 in both settings, however, remains elusive. We use computer assisted image analysis (CAIA) to determine the optimal threshold for Ki67 in predicting survival outcomes and differentiating luminal B from luminal A breast cancers. Methods Quantitative scoring of Ki67 on tissue microarray (TMA) sections of 440 invasive breast cancers was performed using Aperio ePathology ImmunoHistochemistry Nuclear Image Analysis algorithm, with TMA slides digitally scanned via Aperio ScanScope XT System. Results On multivariate analysis, tumours with Ki67 ≥14% had an increased likelihood of recurrence (HR 1.941, p=0.021) and shorter overall survival (HR 2.201, p=0.016). Similar findings were observed in the subset of 343 ER positive breast cancers (HR 2.409, p=0.012 and HR 2.787, p=0.012 respectively). The value of Ki67 associated with ER+HER2-PR<20% tumours (Luminal B subtype) was found to be <17%. Conclusion Using CAIA, we found optimal thresholds for Ki67 that predict a poorer prognosis and an association with the Luminal B subtype of breast cancer. Further investigation and validation of these thresholds are recommended. PMID:29545924

Detection of pathogenic Vibrio spp. in shellfish by using multiplex PCR and DNA microarrays.

PubMed

Panicker, Gitika; Call, Douglas R; Krug, Melissa J; Bej, Asim K

2004-12-01

This study describes the development of a gene-specific DNA microarray coupled with multiplex PCR for the comprehensive detection of pathogenic vibrios that are natural inhabitants of warm coastal waters and shellfish. Multiplex PCR with vvh and viuB for Vibrio vulnificus, with ompU, toxR, tcpI, and hlyA for V. cholerae, and with tlh, tdh, trh, and open reading frame 8 for V. parahaemolyticus helped to ensure that total and pathogenic strains, including subtypes of the three Vibrio spp., could be detected and discriminated. For DNA microarrays, oligonucleotide probes for these targeted genes were deposited onto epoxysilane-derivatized, 12-well, Teflon-masked slides by using a MicroGrid II arrayer. Amplified PCR products were hybridized to arrays at 50 degrees C and detected by using tyramide signal amplification with Alexa Fluor 546 fluorescent dye. Slides were imaged by using an arrayWoRx scanner. The detection sensitivity for pure cultures without enrichment was 10(2) to 10(3) CFU/ml, and the specificity was 100%. However, 5 h of sample enrichment followed by DNA extraction with Instagene matrix and multiplex PCR with microarray hybridization resulted in the detection of 1 CFU in 1 g of oyster tissue homogenate. Thus, enrichment of the bacterial pathogens permitted higher sensitivity in compliance with the Interstate Shellfish Sanitation Conference guideline. Application of the DNA microarray methodology to natural oysters revealed the presence of V. vulnificus (100%) and V. parahaemolyticus (83%). However, V. cholerae was not detected in natural oysters. An assay involving a combination of multiplex PCR and DNA microarray hybridization would help to ensure rapid and accurate detection of pathogenic vibrios in shellfish, thereby improving the microbiological safety of shellfish for consumers.

Detection of Pathogenic Vibrio spp. in Shellfish by Using Multiplex PCR and DNA Microarrays

PubMed Central

Panicker, Gitika; Call, Douglas R.; Krug, Melissa J.; Bej, Asim K.

2004-01-01

This study describes the development of a gene-specific DNA microarray coupled with multiplex PCR for the comprehensive detection of pathogenic vibrios that are natural inhabitants of warm coastal waters and shellfish. Multiplex PCR with vvh and viuB for Vibrio vulnificus, with ompU, toxR, tcpI, and hlyA for V. cholerae, and with tlh, tdh, trh, and open reading frame 8 for V. parahaemolyticus helped to ensure that total and pathogenic strains, including subtypes of the three Vibrio spp., could be detected and discriminated. For DNA microarrays, oligonucleotide probes for these targeted genes were deposited onto epoxysilane-derivatized, 12-well, Teflon-masked slides by using a MicroGrid II arrayer. Amplified PCR products were hybridized to arrays at 50°C and detected by using tyramide signal amplification with Alexa Fluor 546 fluorescent dye. Slides were imaged by using an arrayWoRx scanner. The detection sensitivity for pure cultures without enrichment was 102 to 103 CFU/ml, and the specificity was 100%. However, 5 h of sample enrichment followed by DNA extraction with Instagene matrix and multiplex PCR with microarray hybridization resulted in the detection of 1 CFU in 1 g of oyster tissue homogenate. Thus, enrichment of the bacterial pathogens permitted higher sensitivity in compliance with the Interstate Shellfish Sanitation Conference guideline. Application of the DNA microarray methodology to natural oysters revealed the presence of V. vulnificus (100%) and V. parahaemolyticus (83%). However, V. cholerae was not detected in natural oysters. An assay involving a combination of multiplex PCR and DNA microarray hybridization would help to ensure rapid and accurate detection of pathogenic vibrios in shellfish, thereby improving the microbiological safety of shellfish for consumers. PMID:15574946

Development and application of antibody microarray for lymphocystis disease virus detection in fish.

PubMed

Sheng, Xiuzhen; Xu, Xiaoli; Zhan, Wenbin

2013-05-01

Lymphocystis disease virus (LCDV) is the causative agent of lymphocystis disease affecting marine and freshwater fish worldwide. Here an antibody microarray was developed and employed to detect LCDV in fish. Rabbit anti-LCDV serum was arrayed on agarose gel-modified slides as capture antibody, and Cy3-conjugated anti-LCDV monoclonal antibody (MAbs) was added as detection antibody. The signals were imaged with a laser chip scanner and analyzed by corresponding software. To improve the sensitivity, different substrate binders (poly-L-lysine, MPTS, aldehyde, APES and agarose gel modified slides, and commercially available amino-modified slides), markers (fluorescein isothiocyanate, Cy3, horseradish peroxidase, biotin or colloidal gold) conjugated to anti-LCDV Mabs, and storage time of the antibody were assessed. The results showed that the antibody microarrays based on agarose gel-modified slides gave a lower detection limit of 0.55μg/ml of LCDV when Cy3 and HRP conjugated anti-LCDV MAbs were used as detection antibody; and the lowest detectable LCDV protein concentration was 0.0686 μg/ml when streptavidin-biotin conjugated to anti-LCDV MAbs served as detection antibody. The developed antibody microarray proved to have a high specificity for LCDV detection and a shelf-life of more than 8 months at -20°C. Furthermore, the LCDV detection results of the microarray in fish gills or fins (n=50) presented a concordance rate of 100% with enzyme-linked immunosorbent assay (ELISA) and 98% with immunofluorescence assay technique (IFAT). These results revealed that the developed antibody microarray could serve as an effective tool for diagnostic and epidemiological studies of LCDV in fish. Copyright © 2013 Elsevier B.V. All rights reserved.

Archetypal analysis of diverse Pseudomonas aeruginosa transcriptomes reveals adaptation in cystic fibrosis airways

PubMed Central

2013-01-01

Background Analysis of global gene expression by DNA microarrays is widely used in experimental molecular biology. However, the complexity of such high-dimensional data sets makes it difficult to fully understand the underlying biological features present in the data. The aim of this study is to introduce a method for DNA microarray analysis that provides an intuitive interpretation of data through dimension reduction and pattern recognition. We present the first “Archetypal Analysis” of global gene expression. The analysis is based on microarray data from five integrated studies of Pseudomonas aeruginosa isolated from the airways of cystic fibrosis patients. Results Our analysis clustered samples into distinct groups with comprehensible characteristics since the archetypes representing the individual groups are closely related to samples present in the data set. Significant changes in gene expression between different groups identified adaptive changes of the bacteria residing in the cystic fibrosis lung. The analysis suggests a similar gene expression pattern between isolates with a high mutation rate (hypermutators) despite accumulation of different mutations for these isolates. This suggests positive selection in the cystic fibrosis lung environment, and changes in gene expression for these isolates are therefore most likely related to adaptation of the bacteria. Conclusions Archetypal analysis succeeded in identifying adaptive changes of P. aeruginosa. The combination of clustering and matrix factorization made it possible to reveal minor similarities among different groups of data, which other analytical methods failed to identify. We suggest that this analysis could be used to supplement current methods used to analyze DNA microarray data. PMID:24059747

Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

PubMed

Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

2013-01-01

DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

The COST Action IC0604 "Telepathology Network in Europe" (EURO-TELEPATH).

PubMed

García-Rojo, Marcial; Gonçalves, Luís; Blobel, Bernd

2012-01-01

The COST Action IC0604 "Telepathology Network in Europe" (EURO-TELEPATH) is a European COST Action that has been running from 2007 to 2011. COST Actions are funded by the COST (European Cooperation in the field of Scientific and Technical Research) Agency, supported by the Seventh Framework Programme for Research and Technological Development (FP7), of the European Union. EURO-TELEPATH's main objectives were evaluating and validating the common technological framework and communication standards required to access, transmit and manage digital medical records by pathologists and other medical professionals in a networked environment. The project was organized in four working groups. orking Group 1 "Business modeling in pathology" has designed main pathology processes - Frozen Study, Formalin Fixed Specimen Study, Telepathology, Cytology, and Autopsy -using Business Process Modeling Notation (BPMN). orking Group 2 "Informatics standards in pathology" has been dedicated to promoting the development and application of informatics standards in pathology, collaborating with Integrating the Healthcare Enterprise (IHE), Digital Imaging and Communications in Medicine (DICOM), Health Level Seven (HL7), and other standardization bodies. Working Group 3 "Images: Analysis, Processing, Retrieval and Management" worked on the use of virtual or digital slides that are fostering the use of image processing and analysis in pathology not only for research purposes, but also in daily practice. Working Group 4 "Technology and Automation in Pathology" was focused on studying the adequacy of current existing technical solutions, including, e.g., the quality of images obtained by slide scanners, or the efficiency of image analysis applications. Major outcome of this action are the collaboration with international health informatics standardization bodies to foster the development of standards for digital pathology, offering a new approach for workflow analysis, based in business process modeling. Health terminology standardization research has become a topic of high interest. Future research work should focus on standardization of automatic image analysis and tissue microarrays imaging.

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

PubMed Central

2010-01-01

Background The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data. PMID:20122245

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

PubMed

Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

2010-01-18

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

Gene expression profiling of whole blood: Comparison of target preparation methods for accurate and reproducible microarray analysis

PubMed Central

Vartanian, Kristina; Slottke, Rachel; Johnstone, Timothy; Casale, Amanda; Planck, Stephen R; Choi, Dongseok; Smith, Justine R; Rosenbaum, James T; Harrington, Christina A

2009-01-01

Background Peripheral blood is an accessible and informative source of transcriptomal information for many human disease and pharmacogenomic studies. While there can be significant advantages to analyzing RNA isolated from whole blood, particularly in clinical studies, the preparation of samples for microarray analysis is complicated by the need to minimize artifacts associated with highly abundant globin RNA transcripts. The impact of globin RNA transcripts on expression profiling data can potentially be reduced by using RNA preparation and labeling methods that remove or block globin RNA during the microarray assay. We compared four different methods for preparing microarray hybridization targets from human whole blood collected in PAXGene tubes. Three of the methods utilized the Affymetrix one-cycle cDNA synthesis/in vitro transcription protocol but varied treatment of input RNA as follows: i. no treatment; ii. treatment with GLOBINclear; or iii. treatment with globin PNA oligos. In the fourth method cDNA targets were prepared with the Ovation amplification and labeling system. Results We find that microarray targets generated with labeling methods that reduce globin mRNA levels or minimize the impact of globin transcripts during hybridization detect more transcripts in the microarray assay compared with the standard Affymetrix method. Comparison of microarray results with quantitative PCR analysis of a panel of genes from the NF-kappa B pathway shows good correlation of transcript measurements produced with all four target preparation methods, although method-specific differences in overall correlation were observed. The impact of freezing blood collected in PAXGene tubes on data reproducibility was also examined. Expression profiles show little or no difference when RNA is extracted from either fresh or frozen blood samples. Conclusion RNA preparation and labeling methods designed to reduce the impact of globin mRNA transcripts can significantly improve the sensitivity of the DNA microarray expression profiling assay for whole blood samples. While blockage of globin transcripts during first strand cDNA synthesis with globin PNAs resulted in the best overall performance in this study, we conclude that selection of a protocol for expression profiling studies in blood should depend on several factors, including implementation requirements of the method and study design. RNA isolated from either freshly collected or frozen blood samples stored in PAXGene tubes can be used without altering gene expression profiles. PMID:19123946

APPLICATION OF DNA MICROARRAYS TO REPRODUCTIVE TOXICOLOGY AND THE DEVELOPMENT OF A TESTIS ARRAY

EPA Science Inventory

With the advent of sequence information for entire mammalian genomes, it is now possible to analyze gene expression and gene polymorphisms on a genomic scale. The primary tool for analysis of gene expression is the DNA microarray. We have used commercially available cDNA micro...

Microarrays for Undergraduate Classes

ERIC Educational Resources Information Center

Hancock, Dale; Nguyen, Lisa L.; Denyer, Gareth S.; Johnston, Jill M.

2006-01-01

A microarray experiment is presented that, in six laboratory sessions, takes undergraduate students from the tissue sample right through to data analysis. The model chosen, the murine erythroleukemia cell line, can be easily cultured in sufficient quantities for class use. Large changes in gene expression can be induced in these cells by…

BIOMONITORING THE TOXICOGENOMIC RESPONSE TO ENDOCRINE DISRUPTING CHEMICALS IN HUMANS, LABORATORY SPECIES AND WILDLIFE

EPA Science Inventory

With the advent of sequence information for entire eukaryotic genomes, it is now possible to analyze gene expression on a genomic scale. The primary tool for genomic analysis of gene expression is the gene microarray. We have used commercially available and custom cDNA microarray...

CLOFIBRATE-INDUCED GENE EXPRESSION CHANGES IN RAT LIVER: A CROSS-LABORATORY ANALYSIS USING MEMBRANE CDNA ARRAYS

EPA Science Inventory

Microarrays have the potential to significantly impact our ability to identify toxic hazards by the identification of mechanistically-relevant markers of toxicity. To be useful for risk assessment however, microarray data must be challenged to determine its reliability and inter...

Comparative analysis of microarray data in Arabidopsis transcriptome during compatible interactions with plant viruses

USDA-ARS?s Scientific Manuscript database

To analyze transcriptome response to virus infection, we have assembled currently available microarray data on changes in gene expression levels in compatible Arabidopsis-virus interactions. We used the mean r (Pearson’s correlation coefficient) for neighboring pairs to estimate pairwise local simil...

Profiling In Situ Microbial Community Structure with an Amplification Microarray

PubMed Central

Knickerbocker, Christopher; Bryant, Lexi; Golova, Julia; Wiles, Cory; Williams, Kenneth H.; Peacock, Aaron D.; Long, Philip E.

2013-01-01

The objectives of this study were to unify amplification, labeling, and microarray hybridization chemistries within a single, closed microfluidic chamber (an amplification microarray) and verify technology performance on a series of groundwater samples from an in situ field experiment designed to compare U(VI) mobility under conditions of various alkalinities (as HCO3−) during stimulated microbial activity accompanying acetate amendment. Analytical limits of detection were between 2 and 200 cell equivalents of purified DNA. Amplification microarray signatures were well correlated with 16S rRNA-targeted quantitative PCR results and hybridization microarray signatures. The succession of the microbial community was evident with and consistent between the two microarray platforms. Amplification microarray analysis of acetate-treated groundwater showed elevated levels of iron-reducing bacteria (Flexibacter, Geobacter, Rhodoferax, and Shewanella) relative to the average background profile, as expected. Identical molecular signatures were evident in the transect treated with acetate plus NaHCO3, but at much lower signal intensities and with a much more rapid decline (to nondetection). Azoarcus, Thaurea, and Methylobacterium were responsive in the acetate-only transect but not in the presence of bicarbonate. Observed differences in microbial community composition or response to bicarbonate amendment likely had an effect on measured rates of U reduction, with higher rates probable in the part of the field experiment that was amended with bicarbonate. The simplification in microarray-based work flow is a significant technological advance toward entirely closed-amplicon microarray-based tests and is generally extensible to any number of environmental monitoring applications. PMID:23160129

Bioinformatics and Microarray Data Analysis on the Cloud.

PubMed

Calabrese, Barbara; Cannataro, Mario

2016-01-01

High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data storage and computing power. Cloud computing offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and thus, it may represent the key technology for facing those issues. In fact, in the recent years it has been adopted for the deployment of different bioinformatics solutions and services both in academia and in the industry. Although this, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This chapter reviews main academic and industrial cloud-based bioinformatics solutions; with a special focus on microarray data analysis solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.

Identification of differentially expressed genes and false discovery rate in microarray studies.

PubMed

Gusnanto, Arief; Calza, Stefano; Pawitan, Yudi

2007-04-01

To highlight the development in microarray data analysis for the identification of differentially expressed genes, particularly via control of false discovery rate. The emergence of high-throughput technology such as microarrays raises two fundamental statistical issues: multiplicity and sensitivity. We focus on the biological problem of identifying differentially expressed genes. First, multiplicity arises due to testing tens of thousands of hypotheses, rendering the standard P value meaningless. Second, known optimal single-test procedures such as the t-test perform poorly in the context of highly multiple tests. The standard approach of dealing with multiplicity is too conservative in the microarray context. The false discovery rate concept is fast becoming the key statistical assessment tool replacing the P value. We review the false discovery rate approach and argue that it is more sensible for microarray data. We also discuss some methods to take into account additional information from the microarrays to improve the false discovery rate. There is growing consensus on how to analyse microarray data using the false discovery rate framework in place of the classical P value. Further research is needed on the preprocessing of the raw data, such as the normalization step and filtering, and on finding the most sensitive test procedure.

Form-Deprivation Myopia in Chick Induces Limited Changes in Retinal Gene Expression

PubMed Central

McGlinn, Alice M.; Baldwin, Donald A.; Tobias, John W.; Budak, Murat T.; Khurana, Tejvir S.; Stone, Richard A.

2007-01-01

Purpose Evidence has implicated the retina as a principal controller of refractive development. In the present study, the retinal transcriptome was analyzed to identify alterations in gene expression and potential signaling pathways involved in form-deprivation myopia of the chick. Methods One-week-old white Leghorn chicks wore a unilateral image-degrading goggle for 6 hours or 3 days (n = 6 at each time). Total RNA from the retina/(retinal pigment epithelium) was used for expression profiling with chicken gene microarrays (Chicken GeneChips; Affymetrix, Santa Clara, CA). To identify gene expression level differences between goggled and contralateral nongoggled eyes, normalized microarray signal intensities were analyzed by the significance analysis of microarrays (SAM) approach. Differentially expressed genes were validated by real-time quantitative reverse transcription–polymerase chain reaction (qPCR) in independent biological replicates. Results Small changes were detected in differentially expressed genes in form-deprived eyes. In chickens that had 6 hours of goggle wear, downregulation of bone morphogenetic protein 2 and connective tissue growth factor was validated. In those with 3 days of goggle wear, downregulation of bone morphogenetic protein 2, vasoactive intestinal peptide, preopro-urotensin II–related peptide and mitogen-activated protein kinase phosphatase 2 was validated, and upregulation of endothelin receptor type B and interleukin-18 was validated. Conclusions Form-deprivation myopia, in its early stages, is associated with only minimal changes in retinal gene expression at the level of the transcriptome. While the list of validated genes is short, each merits further study for potential involvement in the signaling cascade mediating myopia development. PMID:17652709

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies

PubMed Central

2012-01-01

Background The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate. Results We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures. Conclusion T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially. PMID:22276688

Advantages of RNA-seq compared to RNA microarrays for transcriptome profiling of anterior cruciate ligament tears.

PubMed

Rai, Muhammad Farooq; Tycksen, Eric D; Sandell, Linda J; Brophy, Robert H

2018-01-01

Microarrays and RNA-seq are at the forefront of high throughput transcriptome analyses. Since these methodologies are based on different principles, there are concerns about the concordance of data between the two techniques. The concordance of RNA-seq and microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed in clinically derived ligament tissues. To demonstrate the concordance between RNA-seq and microarrays and to assess potential benefits of RNA-seq over microarrays, we assessed differences in transcript expression in anterior cruciate ligament (ACL) tissues based on time-from-injury. ACL remnants were collected from patients with an ACL tear at the time of ACL reconstruction. RNA prepared from torn ACL remnants was subjected to Agilent microarrays (N = 24) and RNA-seq (N = 8). The correlation of biological replicates in RNA-seq and microarrays data was similar (0.98 vs. 0.97), demonstrating that each platform has high internal reproducibility. Correlations between the RNA-seq data and the individual microarrays were low, but correlations between the RNA-seq values and the geometric mean of the microarrays values were moderate. The cross-platform concordance for differentially expressed transcripts or enriched pathways was linearly correlated (r = 0.64). RNA-Seq was superior in detecting low abundance transcripts and differentiating biologically critical isoforms. Additional independent validation of transcript expression was undertaken using microfluidic PCR for selected genes. PCR data showed 100% concordance (in expression pattern) with RNA-seq and microarrays data. These findings demonstrate that RNA-seq has advantages over microarrays for transcriptome profiling of ligament tissues when available and affordable. Furthermore, these findings are likely transferable to other musculoskeletal tissues where tissue collection is challenging and cells are in low abundance. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:484-497, 2018. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

A model of binding on DNA microarrays: understanding the combined effect of probe synthesis failure, cross-hybridization, DNA fragmentation and other experimental details of affymetrix arrays

PubMed Central

2012-01-01

Background DNA microarrays are used both for research and for diagnostics. In research, Affymetrix arrays are commonly used for genome wide association studies, resequencing, and for gene expression analysis. These arrays provide large amounts of data. This data is analyzed using statistical methods that quite often discard a large portion of the information. Most of the information that is lost comes from probes that systematically fail across chips and from batch effects. The aim of this study was to develop a comprehensive model for hybridization that predicts probe intensities for Affymetrix arrays and that could provide a basis for improved microarray analysis and probe development. The first part of the model calculates probe binding affinities to all the possible targets in the hybridization solution using the Langmuir isotherm. In the second part of the model we integrate details that are specific to each experiment and contribute to the differences between hybridization in solution and on the microarray. These details include fragmentation, wash stringency, temperature, salt concentration, and scanner settings. Furthermore, the model fits probe synthesis efficiency and target concentration parameters directly to the data. All the parameters used in the model have a well-established physical origin. Results For the 302 chips that were analyzed the mean correlation between expected and observed probe intensities was 0.701 with a range of 0.88 to 0.55. All available chips were included in the analysis regardless of the data quality. Our results show that batch effects arise from differences in probe synthesis, scanner settings, wash strength, and target fragmentation. We also show that probe synthesis efficiencies for different nucleotides are not uniform. Conclusions To date this is the most complete model for binding on microarrays. This is the first model that includes both probe synthesis efficiency and hybridization kinetics/cross-hybridization. These two factors are sequence dependent and have a large impact on probe intensity. The results presented here provide novel insight into the effect of probe synthesis errors on Affymetrix microarrays; furthermore, the algorithms developed in this work provide useful tools for the analysis of cross-hybridization, probe synthesis efficiency, fragmentation, wash stringency, temperature, and salt concentration on microarray intensities. PMID:23270536

Importance of the efficiency of double-stranded DNA formation in cDNA synthesis for the imprecision of microarray expression analysis.

PubMed

Thormar, Hans G; Gudmundsson, Bjarki; Eiriksdottir, Freyja; Kil, Siyoen; Gunnarsson, Gudmundur H; Magnusson, Magnus Karl; Hsu, Jason C; Jonsson, Jon J

2013-04-01

The causes of imprecision in microarray expression analysis are poorly understood, limiting the use of this technology in molecular diagnostics. Two-dimensional strandness-dependent electrophoresis (2D-SDE) separates nucleic acid molecules on the basis of length and strandness, i.e., double-stranded DNA (dsDNA), single-stranded DNA (ssDNA), and RNA·DNA hybrids. We used 2D-SDE to measure the efficiency of cDNA synthesis and its importance for the imprecision of an in vitro transcription-based microarray expression analysis. The relative amount of double-stranded cDNA formed in replicate experiments that used the same RNA sample template was highly variable, ranging between 0% and 72% of the total DNA. Microarray experiments showed an inverse relationship between the difference between sample pairs in probe variance and the relative amount of dsDNA. Approximately 15% of probes showed between-sample variation (P < 0.05) when the dsDNA percentage was between 12% and 35%. In contrast, only 3% of probes showed between-sample variation when the dsDNA percentage was 69% and 72%. Replication experiments of the 35% dsDNA and 72% dsDNA samples were used to separate sample variation from probe replication variation. The estimated SD of the sample-to-sample variation and of the probe replicates was lower in 72% dsDNA samples than in 35% dsDNA samples. Variation in the relative amount of double-stranded cDNA synthesized can be an important component of the imprecision in T7 RNA polymerase-based microarray expression analysis. © 2013 American Association for Clinical Chemistry

Quantitative Proteomic and Microarray Analysis of the Archaeon Methanosarcina Acetivorans Grown with Acetate Versus Methanol*

PubMed Central

Li, Lingyun; Li, Qingbo; Rohlin, Lars; Kim, UnMi; Salmon, Kirsty; Rejtar, Tomas; Gunsalus, Robert P.; Karger, Barry L.; Ferry, James G.

2008-01-01

Summary Methanosarcina acetivorans strain C2A is an acetate- and methanol-utilizing methane-producing organism for which the genome, the largest yet sequenced among the Archaea, reveals extensive physiological diversity. LC linear ion trap-FTICR mass spectrometry was employed to analyze acetate- vs. methanol-grown cells metabolically labeled with 14N vs. 15N, respectively, to obtain quantitative protein abundance ratios. DNA microarray analyses of acetate- vs. methanol-grown cells was also performed to determine gene expression ratios. The combined approaches were highly complementary, extending the physiological understanding of growth and methanogenesis. Of the 1081 proteins detected, 255 were ≥ 3-fold differentially abundant. DNA microarray analysis revealed 410 genes that were ≥ 2.5-fold differentially expressed of 1972 genes with detected expression. The ratios of differentially abundant proteins were in good agreement with expression ratios of the encoding genes. Taken together, the results suggest several novel roles for electron transport components specific to acetate-grown cells, including two flavodoxins each specific for growth on acetate or methanol. Protein abundance ratios indicated that duplicate CO dehydrogenase/acetyl-CoA complexes function in the conversion of acetate to methane. Surprisingly, the protein abundance and gene expression ratios indicated a general stress response in acetate- vs. methanol-grown cells that included enzymes specific for polyphosphate accumulation and oxidative stress. The microarray analysis identified transcripts of several genes encoding regulatory proteins with identity to the PhoU, MarR, GlnK, and TetR families commonly found in the Bacteria domain. An analysis of neighboring genes suggested roles in controlling phosphate metabolism (PhoU), ammonia assimilation (GlnK), and molybdopterin cofactor biosynthesis (TetR). Finally, the proteomic and microarray results suggested roles for two-component regulatory systems specific for each growth substrate. PMID:17269732

Integrated analysis of chromosome copy number variation and gene expression in cervical carcinoma

PubMed Central

Yan, Deng; Yi, Song; Chiu, Wang Chi; Qin, Liu Gui; Kin, Wong Hoi; Kwok Hung, Chung Tony; Linxiao, Han; Wai, Choy Kwong; Yi, Sui; Tao, Yang; Tao, Tang

2017-01-01

Objective This study was conducted to explore chromosomal copy number variations (CNV) and transcript expression and to examine pathways in cervical pathogenesis using genome-wide high resolution microarrays. Methods Genome-wide chromosomal CNVs were investigated in 6 cervical cancer cell lines by Human Genome CGH Microarray Kit (4x44K). Gene expression profiles in cervical cancer cell lines, primary cervical carcinoma and normal cervical epithelium tissues were also studied using the Whole Human Genome Microarray Kit (4x44K). Results Fifty common chromosomal CNVs were identified in the cervical cancer cell lines. Correlation analysis revealed that gene up-regulation or down-regulation is significantly correlated with genomic amplification (P=0.009) or deletion (P=0.006) events. Expression profiles were identified through cluster analysis. Gene annotation analysis pinpointed cell cycle pathways was significantly (P=1.15E-08) affected in cervical cancer. Common CNVs were associated with cervical cancer. Conclusion Chromosomal CNVs may contribute to their transcript expression in cervical cancer. PMID:29312578

A benchmark for statistical microarray data analysis that preserves actual biological and technical variance.

PubMed

De Hertogh, Benoît; De Meulder, Bertrand; Berger, Fabrice; Pierre, Michael; Bareke, Eric; Gaigneaux, Anthoula; Depiereux, Eric

2010-01-11

Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods. Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality. Performance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better. The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.

Integrated analysis of chromosome copy number variation and gene expression in cervical carcinoma.

PubMed

Yan, Deng; Yi, Song; Chiu, Wang Chi; Qin, Liu Gui; Kin, Wong Hoi; Kwok Hung, Chung Tony; Linxiao, Han; Wai, Choy Kwong; Yi, Sui; Tao, Yang; Tao, Tang

2017-12-12

This study was conducted to explore chromosomal copy number variations (CNV) and transcript expression and to examine pathways in cervical pathogenesis using genome-wide high resolution microarrays. Genome-wide chromosomal CNVs were investigated in 6 cervical cancer cell lines by Human Genome CGH Microarray Kit (4x44K). Gene expression profiles in cervical cancer cell lines, primary cervical carcinoma and normal cervical epithelium tissues were also studied using the Whole Human Genome Microarray Kit (4x44K). Fifty common chromosomal CNVs were identified in the cervical cancer cell lines. Correlation analysis revealed that gene up-regulation or down-regulation is significantly correlated with genomic amplification ( P =0.009) or deletion ( P =0.006) events. Expression profiles were identified through cluster analysis. Gene annotation analysis pinpointed cell cycle pathways was significantly ( P =1.15E-08) affected in cervical cancer. Common CNVs were associated with cervical cancer. Chromosomal CNVs may contribute to their transcript expression in cervical cancer.

Base pair mismatch recognition using plasmon resonant particle labels.

PubMed

Oldenburg, Steven J; Genick, Christine C; Clark, Keith A; Schultz, David A

2002-10-01

We demonstrate the use of silver plasmon resonant particles (PRPs), as reporter labels, in a microarray-based DNA hybridization assay in which we screen for a known polymorphic site in the breast cancer gene BRCA1. PRPs (40-100 nm in diameter) image as diffraction-limited points of colored light in a standard microscope equipped with dark-field illumination, and can be individually identified and discriminated against background scatter. Rather than overall intensity, the number of PRPs counted in a CCD image by a software algorithm serves as the signal in these assays. In a typical PRP hybridization assay, we achieve a detection sensitivity that is approximately 60 x greater than that achieved by using fluorescent labels. We conclude that single particle counting is robust, generally applicable to a wide variety of assay platforms, and can be integrated into low-cost and quantitative detection systems for single nucleotide polymorphism analysis.

[The problem of small "n" and big "P" in neuropsycho-pharmacology, or how to keep the rate of false discoveries under control].

PubMed

Petschner, Péter; Bagdy, György; Tóthfalusi, Laszló

2015-03-01

One of the characteristics of many methods used in neuropsychopharmacology is that a large number of parameters (P) are measured in relatively few subjects (n). Functional magnetic resonance imaging, electroencephalography (EEG) and genomic studies are typical examples. For example one microarray chip can contain thousands of probes. Therefore, in studies using microarray chips, P may be several thousand-fold larger than n. Statistical analysis of such studies is a challenging task and they are refereed to in the statistical literature such as the small "n" big "P" problem. The problem has many facets including the controversies associated with multiple hypothesis testing. A typical scenario in this context is, when two or more groups are compared by the individual attributes. If the increased classification error due to the multiple testing is neglected, then several highly significant differences will be discovered. But in reality, some of these significant differences are coincidental, not reproducible findings. Several methods were proposed to solve this problem. In this review we discuss two of the proposed solutions, algorithms to compare sets and statistical hypothesis tests controlling the false discovery rate.

Identifying novel glioma associated pathways based on systems biology level meta-analysis.

PubMed

Hu, Yangfan; Li, Jinquan; Yan, Wenying; Chen, Jiajia; Li, Yin; Hu, Guang; Shen, Bairong

2013-01-01

With recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued. In this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance. Our results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.

PhylArray: phylogenetic probe design algorithm for microarray.

PubMed

Militon, Cécile; Rimour, Sébastien; Missaoui, Mohieddine; Biderre, Corinne; Barra, Vincent; Hill, David; Moné, Anne; Gagne, Geneviève; Meier, Harald; Peyretaillade, Eric; Peyret, Pierre

2007-10-01

Microbial diversity is still largely unknown in most environments, such as soils. In order to get access to this microbial 'black-box', the development of powerful tools such as microarrays are necessary. However, the reliability of this approach relies on probe efficiency, in particular sensitivity, specificity and explorative power, in order to obtain an image of the microbial communities that is close to reality. We propose a new probe design algorithm that is able to select microarray probes targeting SSU rRNA at any phylogenetic level. This original approach, implemented in a program called 'PhylArray', designs a combination of degenerate and non-degenerate probes for each target taxon. Comparative experimental evaluations indicate that probes designed with PhylArray yield a higher sensitivity and specificity than those designed by conventional approaches. Applying the combined PhyArray/GoArrays strategy helps to optimize the hybridization performance of short probes. Finally, hybridizations with environmental targets have shown that the use of the PhylArray strategy can draw attention to even previously unknown bacteria.

High-Throughput Quantification of SH2 Domain-Phosphopeptide Interactions with Cellulose-Peptide Conjugate Microarrays.

PubMed

Engelmann, Brett W

2017-01-01

The Src Homology 2 (SH2) domain family primarily recognizes phosphorylated tyrosine (pY) containing peptide motifs. The relative affinity preferences among competing SH2 domains for phosphopeptide ligands define "specificity space," and underpins many functional pY mediated interactions within signaling networks. The degree of promiscuity exhibited and the dynamic range of affinities supported by individual domains or phosphopeptides is best resolved by a carefully executed and controlled quantitative high-throughput experiment. Here, I describe the fabrication and application of a cellulose-peptide conjugate microarray (CPCMA) platform to the quantitative analysis of SH2 domain specificity space. Included herein are instructions for optimal experimental design with special attention paid to common sources of systematic error, phosphopeptide SPOT synthesis, microarray fabrication, analyte titrations, data capture, and analysis.

Microbial Diagnostic Microarrays for the Detection and Typing of Food- and Water-Borne (Bacterial) Pathogens

PubMed Central

Kostić, Tanja; Sessitsch, Angela

2011-01-01

Reliable and sensitive pathogen detection in clinical and environmental (including food and water) samples is of greatest importance for public health. Standard microbiological methods have several limitations and improved alternatives are needed. Most important requirements for reliable analysis include: (i) specificity; (ii) sensitivity; (iii) multiplexing potential; (iv) robustness; (v) speed; (vi) automation potential; and (vii) low cost. Microarray technology can, through its very nature, fulfill many of these requirements directly and the remaining challenges have been tackled. In this review, we attempt to compare performance characteristics of the microbial diagnostic microarrays developed for the detection and typing of food and water pathogens, and discuss limitations, points still to be addressed and issues specific for the analysis of food, water and environmental samples. PMID:27605332

Combined image and genomic analysis of high-grade serous ovarian cancer reveals PTEN loss as a common driver event and prognostic classifier.

PubMed

Martins, Filipe C; Santiago, Ines de; Trinh, Anne; Xian, Jian; Guo, Anne; Sayal, Karen; Jimenez-Linan, Mercedes; Deen, Suha; Driver, Kristy; Mack, Marie; Aslop, Jennifer; Pharoah, Paul D; Markowetz, Florian; Brenton, James D

2014-12-17

TP53 and BRCA1/2 mutations are the main drivers in high-grade serous ovarian carcinoma (HGSOC). We hypothesise that combining tissue phenotypes from image analysis of tumour sections with genomic profiles could reveal other significant driver events. Automatic estimates of stromal content combined with genomic analysis of TCGA HGSOC tumours show that stroma strongly biases estimates of PTEN expression. Tumour-specific PTEN expression was tested in two independent cohorts using tissue microarrays containing 521 cases of HGSOC. PTEN loss or downregulation occurred in 77% of the first cohort by immunofluorescence and 52% of the validation group by immunohistochemistry, and is associated with worse survival in a multivariate Cox-regression model adjusted for study site, age, stage and grade. Reanalysis of TCGA data shows that hemizygous loss of PTEN is common (36%) and expression of PTEN and expression of androgen receptor are positively associated. Low androgen receptor expression was associated with reduced survival in data from TCGA and immunohistochemical analysis of the first cohort. PTEN loss is a common event in HGSOC and defines a subgroup with significantly worse prognosis, suggesting the rational use of drugs to target PI3K and androgen receptor pathways for HGSOC. This work shows that integrative approaches combining tissue phenotypes from images with genomic analysis can resolve confounding effects of tissue heterogeneity and should be used to identify new drivers in other cancers.

The pathogenesis shared between abdominal aortic aneurysms and intracranial aneurysms: a microarray analysis.

PubMed

Wang, Wen; Li, Hao; Zhao, Zheng; Wang, Haoyuan; Zhang, Dong; Zhang, Yan; Lan, Qing; Wang, Jiangfei; Cao, Yong; Zhao, Jizong

2018-04-01

Abdominal aortic aneurysms (AAAs) and intracranial saccular aneurysms (IAs) are the most common types of aneurysms. This study was to investigate the common pathogenesis shared between these two kinds of aneurysms. We collected 12 IAs samples and 12 control arteries from the Beijing Tiantan Hospital and performed microarray analysis. In addition, we utilized the microarray datasets of IAs and AAAs from the Gene Expression Omnibus (GEO), in combination with our microarray results, to generate messenger RNA expression profiles for both AAAs and IAs in our study. Functional exploration and protein-protein interaction (PPI) analysis were performed. A total of 727 common genes were differentially expressed (404 was upregulated; 323 was downregulated) for both AAAs and IAs. The GO and pathway analyses showed that the common dysregulated genes were mainly enriched in vascular smooth muscle contraction, muscle contraction, immune response, defense response, cell activation, IL-6 signaling and chemokine signaling pathways, etc. The further protein-protein analysis identified 35 hub nodes, including TNF, IL6, MAPK13, and CCL5. These hub node genes were enriched in inflammatory response, positive regulation of IL-6 production, chemokine signaling pathway, and T/B cell receptor signaling pathway. Our study will gain new insight into the molecular mechanisms for the pathogenesis of both types of aneurysms and provide new therapeutic targets for the patients harboring AAAs and IAs.

Cloud-scale genomic signals processing classification analysis for gene expression microarray data.

PubMed

Harvey, Benjamin; Soo-Yeon Ji

2014-01-01

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring inference though analysis of DNA/mRNA sequence data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological inference by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-scale classification analysis of microarray data using Wavelet thresholding in a Cloud environment to identify significantly expressed features. This paper proposes a novel methodology that uses Wavelet based Denoising to initialize a threshold for determination of significantly expressed genes for classification. Additionally, this research was implemented and encompassed within cloud-based distributed processing environment. The utilization of Cloud computing and Wavelet thresholding was used for the classification 14 tumor classes from the Global Cancer Map (GCM). The results proved to be more accurate than using a predefined p-value for differential expression classification. This novel methodology analyzed Wavelet based threshold features of gene expression in a Cloud environment, furthermore classifying the expression of samples by analyzing gene patterns, which inform us of biological processes. Moreover, enabling researchers to face the present and forthcoming challenges that may arise in the analysis of data in functional genomics of large microarray datasets.

[Differentially expressed genes of cell signal transduction associated with benzene poisoning by cDNA microarray].

PubMed

Wang, Hong; Bi, Yongyi; Tao, Ning; Wang, Chunhong

2005-08-01

To detect the differential expression of cell signal transduction genes associated with benzene poisoning, and to explore the pathogenic mechanisms of blood system damage induced by benzene. Peripheral white blood cell gene expression profile of 7 benzene poisoning patients, including one aplastic anemia, was determined by cDNA microarray. Seven chips from normal workers were served as controls. Cluster analysis of gene expression profile was performed. Among the 4265 target genes, 176 genes associated with cell signal transduction were differentially expressed. 35 up-regulated genes including PTPRC, STAT4, IFITM1 etc were found in at least 6 pieces of microarray; 45 down-regulated genes including ARHB, PPP3CB, CDC37 etc were found in at least 5 pieces of microarray. cDNA microarray technology is an effective technique for screening the differentially expressed genes of cell signal transduction. Disorder in cell signal transduction may play certain role in the pathogenic mechanism of benzene poisoning.

A study of metaheuristic algorithms for high dimensional feature selection on microarray data

NASA Astrophysics Data System (ADS)

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

2017-11-01

Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

Microarray analysis identified Puccinia striiformis f. sp. tritici genes involved in infection and sporulation.

USDA-ARS?s Scientific Manuscript database

Puccinia striiformis f. sp. tritici (Pst) causes stripe rust, one of the most important diseases of wheat worldwide. To identify Pst genes involved in infection and sporulation, a custom oligonucleotide Genechip was made using sequences of 442 genes selected from Pst cDNA libraries. Microarray analy...

Clear cell papillary renal cell carcinoma: a chromosomal microarray analysis of two cases using a novel Molecular Inversion Probe (MIP) technology.

PubMed

Alexiev, Borislav A; Zou, Ying S

2014-12-01

Chromosomal microarray analysis using novel Molecular Inversion Probe (MIP) technology demonstrated 2,570 kb copy neutral LOH of 10q11.22 in two clear cell papillary renal cell carcinomas. In addition, one of the tumors had a big 29,784 kb deletion of 13q11-q14.2. There were two variants of unknown significance, a 2,509 kb gain of Xp22.33 and a 257 kb homozygous deletion of 8p11.22. The somatic mutation panel containing 74 mutations in nine genes did not reveal any mutations. Besides identification of submicroscopic duplications or deletions, SNP microarrays can reveal abnormal allelic imbalances including LOH and copy neutral LOH, which cannot be recognized by chromosome, FISH, and non-SNP microarray arrays. To the best of our knowledge, this is the first study demonstrating copy neutral LOH of 10q11.22 in clear cell papillary renal cell carcinomas using the new MIP SNP OncoScan FFPE Assay Kit on formalin-fixed paraffin-embedded tumor samples. Copyright © 2014 Elsevier GmbH. All rights reserved.

A Unique Procedure to Identify Cell Surface Markers Through a Spherical Self-Organizing Map Applied to DNA Microarray Analysis.

PubMed

Sugii, Yuh; Kasai, Tomonari; Ikeda, Masashi; Vaidyanath, Arun; Kumon, Kazuki; Mizutani, Akifumi; Seno, Akimasa; Tokutaka, Heizo; Kudoh, Takayuki; Seno, Masaharu

2016-01-01

To identify cell-specific markers, we designed a DNA microarray platform with oligonucleotide probes for human membrane-anchored proteins. Human glioma cell lines were analyzed using microarray and compared with normal and fetal brain tissues. For the microarray analysis, we employed a spherical self-organizing map, which is a clustering method suitable for the conversion of multidimensional data into two-dimensional data and displays the relationship on a spherical surface. Based on the gene expression profile, the cell surface characteristics were successfully mirrored onto the spherical surface, thereby distinguishing normal brain tissue from the disease model based on the strength of gene expression. The clustered glioma-specific genes were further analyzed by polymerase chain reaction procedure and immunocytochemical staining of glioma cells. Our platform and the following procedure were successfully demonstrated to categorize the genes coding for cell surface proteins that are specific to glioma cells. Our assessment demonstrates that a spherical self-organizing map is a valuable tool for distinguishing cell surface markers and can be employed in marker discovery studies for the treatment of cancer.

Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes.

PubMed

Feng, Yinling; Wang, Xuefeng

2017-03-01

In order to investigate commonly disturbed genes and pathways in various brain regions of patients with Parkinson's disease (PD), microarray datasets from previous studies were collected and systematically analyzed. Different normalization methods were applied to microarray datasets from different platforms. A strategy combining gene co‑expression networks and clinical information was adopted, using weighted gene co‑expression network analysis (WGCNA) to screen for commonly disturbed genes in different brain regions of patients with PD. Functional enrichment analysis of commonly disturbed genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Co‑pathway relationships were identified with Pearson's correlation coefficient tests and a hypergeometric distribution‑based test. Common genes in pathway pairs were selected out and regarded as risk genes. A total of 17 microarray datasets from 7 platforms were retained for further analysis. Five gene coexpression modules were identified, containing 9,745, 736, 233, 101 and 93 genes, respectively. One module was significantly correlated with PD samples and thus the 736 genes it contained were considered to be candidate PD‑associated genes. Functional enrichment analysis demonstrated that these genes were implicated in oxidative phosphorylation and PD. A total of 44 pathway pairs and 52 risk genes were revealed, and a risk gene pathway relationship network was constructed. Eight modules were identified and were revealed to be associated with PD, cancers and metabolism. A number of disturbed pathways and risk genes were unveiled in PD, and these findings may help advance understanding of PD pathogenesis.

Mining the Salivary Proteome with Grating-Coupled Surface Plasmon Resonance Imaging and Surface Plasmon Coupled Emission Microarrays

PubMed Central

Molony, Ryan D.; Rice, James M.; Yuk, Jongseol; Shetty, Vivek; Dey, Dipak; Lawrence, David A.; Lynes, Michael A.

2012-01-01

Biological indicators have numerous and widespread utility in personalized medicine, but the measurement of these indicators also pose many technological and practical challenges. Blood/plasma has typically been used as the sample source with which to measure these indicators, but the invasiveness associated with procurement of samples has led to increased interest in saliva as an attractive alternative. However, there are unique issues associated with the measurement of saliva biomarkers. These issues are compounded by the imperfect correlation between saliva and plasma with respect to biomarker profiles. In this manuscript, we address the technical challenges associated with saliva biomarker quantification describe a high-content microarray assay that employs both grating-coupled surface plasmon resonance imaging surface plasmon coupled emission modalities in a highly sensitive assay that has a large dynamic range. This powerful approach provides the tools to map the proteome of saliva, which in turn should greatly enhance the utility of salivary biomarker profiles in personalized medicine. PMID:22896008

Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis.

PubMed

Kim, Hyunsoo; Park, Haesun

2007-06-15

Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high-dimensional data in a lower dimensional space. In this article, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithm via alternating non-negativity-constrained least squares. We apply our sparse NMF algorithm to cancer-class discovery and gene expression data analysis and offer biological analysis of the results obtained. Our experimental results illustrate that the proposed sparse NMF algorithm often achieves better clustering performance with shorter computing time compared to other existing NMF algorithms. The software is available as supplementary material.

Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline

PubMed Central

Rahmatallah, Yasir; Emmert-Streib, Frank

2016-01-01

Transcriptome sequencing (RNA-seq) is gradually replacing microarrays for high-throughput studies of gene expression. The main challenge of analyzing microarray data is not in finding differentially expressed genes, but in gaining insights into the biological processes underlying phenotypic differences. To interpret experimental results from microarrays, gene set analysis (GSA) has become the method of choice, in particular because it incorporates pre-existing biological knowledge (in a form of functionally related gene sets) into the analysis. Here we provide a brief review of several statistically different GSA approaches (competitive and self-contained) that can be adapted from microarrays practice as well as those specifically designed for RNA-seq. We evaluate their performance (in terms of Type I error rate, power, robustness to the sample size and heterogeneity, as well as the sensitivity to different types of selection biases) on simulated and real RNA-seq data. Not surprisingly, the performance of various GSA approaches depends only on the statistical hypothesis they test and does not depend on whether the test was developed for microarrays or RNA-seq data. Interestingly, we found that competitive methods have lower power as well as robustness to the samples heterogeneity than self-contained methods, leading to poor results reproducibility. We also found that the power of unsupervised competitive methods depends on the balance between up- and down-regulated genes in tested gene sets. These properties of competitive methods have been overlooked before. Our evaluation provides a concise guideline for selecting GSA approaches, best performing under particular experimental settings in the context of RNA-seq. PMID:26342128

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

PubMed

Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

2015-06-25

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

PubMed Central

2009-01-01

Background Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear. Results We have performed a comprehensive sensitivity analysis of microarray breast cancer classification under the two types of feature variability mentioned above. We used data from six state of the art preprocessing methods, using a compendium consisting of eight diferent datasets, involving 1131 hybridizations, containing data from both one and two-color array technology. For a wide range of classifiers, we performed a joint study on performance, concordance and stability. In the stability analysis we explicitly tested classifiers for their noise tolerance by using perturbed expression profiles that are based on uncertainty information directly related to the preprocessing methods. Our results indicate that signature composition is strongly influenced by feature variability, even if the array platform and the stratification of patient samples are identical. In addition, we show that there is often a high level of discordance between individual class assignments for signatures constructed on data coming from different preprocessing schemes, even if the actual signature composition is identical. Conclusion Feature variability can have a strong impact on breast cancer signature composition, as well as the classification of individual patient samples. We therefore strongly recommend that feature variability is considered in analyzing data from microarray breast cancer expression profiling experiments. PMID:19941644

Mutation analysis of 272 Spanish families affected by autosomal recessive retinitis pigmentosa using a genotyping microarray.

PubMed

Ávila-Fernández, Almudena; Cantalapiedra, Diego; Aller, Elena; Vallespín, Elena; Aguirre-Lambán, Jana; Blanco-Kelly, Fiona; Corton, M; Riveiro-Álvarez, Rosa; Allikmets, Rando; Trujillo-Tiebas, María José; Millán, José M; Cremers, Frans P M; Ayuso, Carmen

2010-12-03

Retinitis pigmentosa (RP) is a genetically heterogeneous disorder characterized by progressive loss of vision. The aim of this study was to identify the causative mutations in 272 Spanish families using a genotyping microarray. 272 unrelated Spanish families, 107 with autosomal recessive RP (arRP) and 165 with sporadic RP (sRP), were studied using the APEX genotyping microarray. The families were also classified by clinical criteria: 86 juveniles and 186 typical RP families. Haplotype and sequence analysis were performed to identify the second mutated allele. At least one-gene variant was found in 14% and 16% of the juvenile and typical RP groups respectively. Further study identified four new mutations, providing both causative changes in 11% of the families. Retinol Dehydrogenase 12 (RDH12) was the most frequently mutated gene in the juvenile RP group, and Usher Syndrome 2A (USH2A) and Ceramide Kinase-Like (CERKL) were the most frequently mutated genes in the typical RP group. The only variant found in CERKL was p.Arg257Stop, the most frequent mutation. The genotyping microarray combined with segregation and sequence analysis allowed us to identify the causative mutations in 11% of the families. Due to the low number of characterized families, this approach should be used in tandem with other techniques.

Curation of microarray oligonucleotides and corresponding ESTs/cDNAs used for gene expression analysis in zebra finches.

PubMed

Lovell, Peter V; Huizinga, Nicole A; Getachew, Abel; Mees, Brianna; Friedrich, Samantha R; Wirthlin, Morgan; Mello, Claudio V

2018-05-18

Zebra finches are a major model organism for investigating mechanisms of vocal learning, a trait that enables spoken language in humans. The development of cDNA collections with expressed sequence tags (ESTs) and microarrays has allowed for extensive molecular characterizations of circuitry underlying vocal learning and production. However, poor database curation can lead to errors in transcriptome and bioinformatics analyses, limiting the impact of these resources. Here we used genomic alignments and synteny analysis for orthology verification to curate and reannotate ~ 35% of the oligonucleotides and corresponding ESTs/cDNAs that make-up Agilent microarrays for gene expression analysis in finches. We found that: (1) 5475 out of 43,084 oligos (a) failed to align to the zebra finch genome, (b) aligned to multiple loci, or (c) aligned to Chr_un only, and thus need to be flagged until a better genome assembly is available, or (d) reflect cloning artifacts; (2) Out of 9635 valid oligos examined further, 3120 were incorrectly named, including 1533 with no known orthologs; and (3) 2635 oligos required name update. The resulting curated dataset provides a reference for correcting gene identification errors in previous finch microarrays studies, and avoiding such errors in future studies.

A microarray analysis of retinal transcripts that are controlled by image contrast in mice

PubMed Central

Brand, Christine; Schaeffel, Frank

2007-01-01

Purpose The development of myopia is controlled by still largely unknown retinal signals. The aim of this study was to investigate the changes in retinal mRNA expression after different periods of visual deprivation in mice, while controlling for retinal illuminance. Methods Each group consisted of three male C57BL/6 mice. Treatment periods were 30 min, 4 h, and 6+6 h. High spatial frequencies were filtered from the retinal image by frosted diffusers over one eye while the fellow eyes were covered by clear neutral density (ND) filters that exhibited similar light attenuating properties (0.1 log units) as the diffusers. For the final 30 min of the respective treatment period mice were individually placed in a clear Perspex cylinder that was positioned in the center of a rotating (60 degrees) large drum. The inside of the drum was covered with a 0.1 cyc/degree vertical square wave grating. This visual environment was chosen to standardize illuminances and contrasts seen by the mice. Labeled cRNA was prepared and hybridized to Affymetrix GeneChip® Mouse Genome 430 2.0 arrays. Alterations in mRNA expression levels of candidate genes with potential biological relevance were confirmed by semi-quantitative real-time reverse transcription polymerase chain reaction (RT-PCR). Results In all groups, Egr-1 mRNA expression was reduced in diffuser-treated eyes. Furthermore, the degradation of the spatial frequency spectrum also changed the cFos mRNA level, with reduced expression after 4 h of diffuser treatment. Other interesting candidates were Akt2, which was up-regulated after 30 min of deprivation and Mapk8ip3, a neuron specific JNK binding and scaffolding protein that was temporally regulated in the diffuser-treated eyes only. Conclusions The microarray analysis demonstrated a pattern of differential transcriptional changes, even though differences in the retinal images were restricted to spatial features. The candidate genes may provide further insight into the biochemical short-term changes following retinal image degradation in mice. Because deprivation of spatial vision leads to increased eye growth and myopia in both animals and humans, it is believed some of the identified genes play a role in myopia development. PMID:17653032

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

PubMed Central

Seo, Minseok; Shin, Su-kyung; Kwon, Eun-Young; Kim, Sung-Eun; Bae, Yun-Jung; Lee, Seungyeoun; Sung, Mi-Kyung; Choi, Myung-Sook; Park, Taesung

2016-01-01

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates. PMID:26964035

Analysis of Protein-DNA Interaction by Chromatin Immunoprecipitation and DNA Tiling Microarray (ChIP-on-chip).

PubMed

Gao, Hui; Zhao, Chunyan

2018-01-01

Chromatin immunoprecipitation (ChIP) has become the most effective and widely used tool to study the interactions between specific proteins or modified forms of proteins and a genomic DNA region. Combined with genome-wide profiling technologies, such as microarray hybridization (ChIP-on-chip) or massively parallel sequencing (ChIP-seq), ChIP could provide a genome-wide mapping of in vivo protein-DNA interactions in various organisms. Here, we describe a protocol of ChIP-on-chip that uses tiling microarray to obtain a genome-wide profiling of ChIPed DNA.

Cloud-Scale Genomic Signals Processing for Robust Large-Scale Cancer Genomic Microarray Data Analysis.

PubMed

Harvey, Benjamin Simeon; Ji, Soo-Yeon

2017-01-01

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring forth oncological inference to the bioinformatics community through the analysis of large-scale cancer genomic (LSCG) DNA and mRNA microarray data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological interpretation by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-scale distributed parallel (CSDP) separable 1-D wavelet decomposition technique for denoising through differential expression thresholding and classification of LSCG microarray data. This research presents a novel methodology that utilizes a CSDP separable 1-D method for wavelet-based transformation in order to initialize a threshold which will retain significantly expressed genes through the denoising process for robust classification of cancer patients. Additionally, the overall study was implemented and encompassed within CSDP environment. The utilization of cloud computing and wavelet-based thresholding for denoising was used for the classification of samples within the Global Cancer Map, Cancer Cell Line Encyclopedia, and The Cancer Genome Atlas. The results proved that separable 1-D parallel distributed wavelet denoising in the cloud and differential expression thresholding increased the computational performance and enabled the generation of higher quality LSCG microarray datasets, which led to more accurate classification results.

76 FR 4921 - Government-Owned Inventions; Availability for Licensing

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-27

... chemical imaging (IRCI) to detect nanostructure-based DNA microarrays, which can be utilized in the life science research arena to examine gene expression and single nucleotide polymorphisms (SNPs), as well as... can be applied to the areas of environmental sciences, agriculture research, bio-defense, diagnostics...

Creation of a digital slide and tissue microarray resource from a multi-institutional predictive toxicology study in the rat: an initial report from the PredTox group.

PubMed

Mulrane, Laoighse; Rexhepaj, Elton; Smart, Valerie; Callanan, John J; Orhan, Diclehan; Eldem, Türkan; Mally, Angela; Schroeder, Susanne; Meyer, Kirstin; Wendt, Maria; O'Shea, Donal; Gallagher, William M

2008-08-01

The widespread use of digital slides has only recently come to the fore with the development of high-throughput scanners and high performance viewing software. This development, along with the optimisation of compression standards and image transfer techniques, has allowed the technology to be used in wide reaching applications including integration of images into hospital information systems and histopathological training, as well as the development of automated image analysis algorithms for prediction of histological aberrations and quantification of immunohistochemical stains. Here, the use of this technology in the creation of a comprehensive library of images of preclinical toxicological relevance is demonstrated. The images, acquired using the Aperio ScanScope CS and XT slide acquisition systems, form part of the ongoing EU FP6 Integrated Project, Innovative Medicines for Europe (InnoMed). In more detail, PredTox (abbreviation for Predictive Toxicology) is a subproject of InnoMed and comprises a consortium of 15 industrial (13 large pharma, 1 technology provider and 1 SME) and three academic partners. The primary aim of this consortium is to assess the value of combining data generated from 'omics technologies (proteomics, transcriptomics, metabolomics) with the results from more conventional toxicology methods, to facilitate further informed decision making in preclinical safety evaluation. A library of 1709 scanned images was created of full-face sections of liver and kidney tissue specimens from male Wistar rats treated with 16 proprietary and reference compounds of known toxicity; additional biological materials from these treated animals were separately used to create 'omics data, that will ultimately be used to populate an integrated toxicological database. In respect to assessment of the digital slides, a web-enabled digital slide management system, Digital SlideServer (DSS), was employed to enable integration of the digital slide content into the 'omics database and to facilitate remote viewing by pathologists connected with the project. DSS also facilitated manual annotation of digital slides by the pathologists, specifically in relation to marking particular lesions of interest. Tissue microarrays (TMAs) were constructed from the specimens for the purpose of creating a repository of tissue from animals used in the study with a view to later-stage biomarker assessment. As the PredTox consortium itself aims to identify new biomarkers of toxicity, these TMAs will be a valuable means of validation. In summary, a large repository of histological images was created enabling the subsequent pathological analysis of samples through remote viewing and, along with the utilisation of TMA technology, will allow the validation of biomarkers identified by the PredTox consortium. The population of the PredTox database with these digitised images represents the creation of the first toxicological database integrating 'omics and preclinical data with histological images.

Microarray as a First Genetic Test in Global Developmental Delay: A Cost-Effectiveness Analysis

ERIC Educational Resources Information Center

Trakadis, Yannis; Shevell, Michael

2011-01-01

Aim: Microarray technology has a significantly higher clinical yield than karyotyping in individuals with global developmental delay (GDD). Despite this, it has not yet been routinely implemented as a screening test owing to the perception that this approach is more expensive. We aimed to evaluate the effect that replacing karyotype with…

Assessing probe-specific dye and slide biases in two-color microarray data

USDA-ARS?s Scientific Manuscript database

A primary reason for using two-color microarrays is that the use of two samples labeled with different dyes on the same slide and that bind to probes on the same spot is supposed to adjust for many factors that introduce noise and errors into the analysis. Most users assume that any differences bet...

Calling Biomarkers in Milk Using a Protein Microarray on Your Smartphone

PubMed Central

Ludwig, Susann K. J.; Tokarski, Christian; Lang, Stefan N.; van Ginkel, Leendert A.; Zhu, Hongying; Ozcan, Aydogan; Nielen, Michel W. F.

2015-01-01

Here we present the concept of a protein microarray-based fluorescence immunoassay for multiple biomarker detection in milk extracts by an ordinary smartphone. A multiplex immunoassay was designed on a microarray chip, having built-in positive and negative quality controls. After the immunoassay procedure, the 48 microspots were labelled with Quantum Dots (QD) depending on the protein biomarker levels in the sample. QD-fluorescence was subsequently detected by the smartphone camera under UV light excitation from LEDs embedded in a simple 3D-printed opto-mechanical smartphone attachment. The somewhat aberrant images obtained under such conditions, were corrected by newly developed Android-based software on the same smartphone, and protein biomarker profiles were calculated. The indirect detection of recombinant bovine somatotropin (rbST) in milk extracts based on altered biomarker profile of anti-rbST antibodies was selected as a real-life challenge. RbST-treated and untreated cows clearly showed reproducible treatment-dependent biomarker profiles in milk, in excellent agreement with results from a flow cytometer reference method. In a pilot experiment, anti-rbST antibody detection was multiplexed with the detection of another rbST-dependent biomarker, insulin-like growth factor 1 (IGF-1). Milk extract IGF-1 levels were found to be increased after rbST treatment and correlated with the results obtained from the reference method. These data clearly demonstrate the potential of the portable protein microarray concept towards simultaneous detection of multiple biomarkers. We envisage broad application of this ‘protein microarray on a smartphone’-concept for on-site testing, e.g., in food safety, environment and health monitoring. PMID:26308444

Exploratory Visual Analysis of Statistical Results from Microarray Experiments Comparing High and Low Grade Glioma

PubMed Central

Reif, David M.; Israel, Mark A.; Moore, Jason H.

2007-01-01

The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a flexible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occuring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profiles of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specific gene expression patterns having both statistical and biological significance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the flexibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org. PMID:19390666

Autoregressive-model-based missing value estimation for DNA microarray time series data.

PubMed

Choong, Miew Keen; Charbit, Maurice; Yan, Hong

2009-01-01

Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.

Segmentation and intensity estimation of microarray images using a gamma-t mixture model.

PubMed

Baek, Jangsun; Son, Young Sook; McLachlan, Geoffrey J

2007-02-15

We present a new approach to the analysis of images for complementary DNA microarray experiments. The image segmentation and intensity estimation are performed simultaneously by adopting a two-component mixture model. One component of this mixture corresponds to the distribution of the background intensity, while the other corresponds to the distribution of the foreground intensity. The intensity measurement is a bivariate vector consisting of red and green intensities. The background intensity component is modeled by the bivariate gamma distribution, whose marginal densities for the red and green intensities are independent three-parameter gamma distributions with different parameters. The foreground intensity component is taken to be the bivariate t distribution, with the constraint that the mean of the foreground is greater than that of the background for each of the two colors. The degrees of freedom of this t distribution are inferred from the data but they could be specified in advance to reduce the computation time. Also, the covariance matrix is not restricted to being diagonal and so it allows for nonzero correlation between R and G foreground intensities. This gamma-t mixture model is fitted by maximum likelihood via the EM algorithm. A final step is executed whereby nonparametric (kernel) smoothing is undertaken of the posterior probabilities of component membership. The main advantages of this approach are: (1) it enjoys the well-known strengths of a mixture model, namely flexibility and adaptability to the data; (2) it considers the segmentation and intensity simultaneously and not separately as in commonly used existing software, and it also works with the red and green intensities in a bivariate framework as opposed to their separate estimation via univariate methods; (3) the use of the three-parameter gamma distribution for the background red and green intensities provides a much better fit than the normal (log normal) or t distributions; (4) the use of the bivariate t distribution for the foreground intensity provides a model that is less sensitive to extreme observations; (5) as a consequence of the aforementioned properties, it allows segmentation to be undertaken for a wide range of spot shapes, including doughnut, sickle shape and artifacts. We apply our method for gridding, segmentation and estimation to cDNA microarray real images and artificial data. Our method provides better segmentation results in spot shapes as well as intensity estimation than Spot and spotSegmentation R language softwares. It detected blank spots as well as bright artifact for the real data, and estimated spot intensities with high-accuracy for the synthetic data. The algorithms were implemented in Matlab. The Matlab codes implementing both the gridding and segmentation/estimation are available upon request. Supplementary material is available at Bioinformatics online.

A microarray-based genotyping and genetic mapping approach for highly heterozygous outcrossing species enables localization of a large fraction of the unassembled Populus trichocarpa genome sequence.

PubMed

Drost, Derek R; Novaes, Evandro; Boaventura-Novaes, Carolina; Benedict, Catherine I; Brown, Ryan S; Yin, Tongming; Tuskan, Gerald A; Kirst, Matias

2009-06-01

Microarrays have demonstrated significant power for genome-wide analyses of gene expression, and recently have also revolutionized the genetic analysis of segregating populations by genotyping thousands of loci in a single assay. Although microarray-based genotyping approaches have been successfully applied in yeast and several inbred plant species, their power has not been proven in an outcrossing species with extensive genetic diversity. Here we have developed methods for high-throughput microarray-based genotyping in such species using a pseudo-backcross progeny of 154 individuals of Populus trichocarpa and P. deltoides analyzed with long-oligonucleotide in situ-synthesized microarray probes. Our analysis resulted in high-confidence genotypes for 719 single-feature polymorphism (SFP) and 1014 gene expression marker (GEM) candidates. Using these genotypes and an established microsatellite (SSR) framework map, we produced a high-density genetic map comprising over 600 SFPs, GEMs and SSRs. The abundance of gene-based markers allowed us to localize over 35 million base pairs of previously unplaced whole-genome shotgun (WGS) scaffold sequence to putative locations in the genome of P. trichocarpa. A high proportion of sampled scaffolds could be verified for their placement with independently mapped SSRs, demonstrating the previously un-utilized power that high-density genotyping can provide in the context of map-based WGS sequence reassembly. Our results provide a substantial contribution to the continued improvement of the Populus genome assembly, while demonstrating the feasibility of microarray-based genotyping in a highly heterozygous population. The strategies presented are applicable to genetic mapping efforts in all plant species with similarly high levels of genetic diversity.

Cruella: developing a scalable tissue microarray data management system.

PubMed

Cowan, James D; Rimm, David L; Tuck, David P

2006-06-01

Compared with DNA microarray technology, relatively little information is available concerning the special requirements, design influences, and implementation strategies of data systems for tissue microarray technology. These issues include the requirement to accommodate new and different data elements for each new project as well as the need to interact with pre-existing models for clinical, biological, and specimen-related data. To design and implement a flexible, scalable tissue microarray data storage and management system that could accommodate information regarding different disease types and different clinical investigators, and different clinical investigation questions, all of which could potentially contribute unforeseen data types that require dynamic integration with existing data. The unpredictability of the data elements combined with the novelty of automated analysis algorithms and controlled vocabulary standards in this area require flexible designs and practical decisions. Our design includes a custom Java-based persistence layer to mediate and facilitate interaction with an object-relational database model and a novel database schema. User interaction is provided through a Java Servlet-based Web interface. Cruella has become an indispensable resource and is used by dozens of researchers every day. The system stores millions of experimental values covering more than 300 biological markers and more than 30 disease types. The experimental data are merged with clinical data that has been aggregated from multiple sources and is available to the researchers for management, analysis, and export. Cruella addresses many of the special considerations for managing tissue microarray experimental data and the associated clinical information. A metadata-driven approach provides a practical solution to many of the unique issues inherent in tissue microarray research, and allows relatively straightforward interoperability with and accommodation of new data models.

Evaluation of the skin irritation using a DNA microarray on a reconstructed human epidermal model.

PubMed

Niwa, Makoto; Nagai, Kanji; Oike, Hideaki; Kobori, Masuko

2009-02-01

To avoid the need to use animals to test the skin irritancy potential of chemicals and cosmetics, it is important to establish an in vitro method based on the reconstructed human epidermal model. To evaluate skin irritancy efficiently and sensitively, we determined the gene expression induced by a topically-applied mild irritant sodium dodecyl sulfate (SDS) in a reconstructed human epidermal model LabCyte EPI-MODEL (LabCyte) using a DNA microarray carrying genes that were related to inflammation, immunity, stress and housekeeping. The expression and secretion of IL-1alpha in reconstructed human epidermal culture is known to be induced by irritation. We detected the induction of IL-1alpha expression and its secretion into the cell culture medium by treatment with 0.075% SDS for 18 h in LabCyte culture using DNA microarray, quantitative reverse-transcription polymerase chain reaction (RT-PCR) and ELISA. DNA microarray analysis indicated that the expression of 10 of the 205 genes carried on the DNA microarray was significantly induced in a LabCyte culture by 0.05% or 0.075% SDS irritation for 18 h. RT-PCR analysis confirmed that SDS treatment significantly induced the expressions of interleukin-1 receptor antagonist (IL-1RN), FOS-like antigen 1 (FOSL1), heat shock 70 kDa protein 1A (HSPA1) and myeloid differentiation primary response gene (88) (MYD88), as well as the known marker genes for irritation IL-1beta and IL-8 in a LabCyte culture. Our results showed that a DNA microarray is a useful tool for efficiently evaluating mild skin irritation using a reconstructed human epidermal model.

Development and experimental validation of a 20K Atlantic cod (Gadus morhua) oligonucleotide microarray based on a collection of over 150,000 ESTs.

PubMed

Booman, Marije; Borza, Tudor; Feng, Charles Y; Hori, Tiago S; Higgins, Brent; Culf, Adrian; Léger, Daniel; Chute, Ian C; Belkaid, Anissa; Rise, Marlies; Gamperl, A Kurt; Hubert, Sophie; Kimball, Jennifer; Ouellette, Rodney J; Johnson, Stewart C; Bowman, Sharen; Rise, Matthew L

2011-08-01

The collapse of Atlantic cod (Gadus morhua) wild populations strongly impacted the Atlantic cod fishery and led to the development of cod aquaculture. In order to improve aquaculture and broodstock quality, we need to gain knowledge of genes and pathways involved in Atlantic cod responses to pathogens and other stressors. The Atlantic Cod Genomics and Broodstock Development Project has generated over 150,000 expressed sequence tags from 42 cDNA libraries representing various tissues, developmental stages, and stimuli. We used this resource to develop an Atlantic cod oligonucleotide microarray containing 20,000 unique probes. Selection of sequences from the full range of cDNA libraries enables application of the microarray for a broad spectrum of Atlantic cod functional genomics studies. We included sequences that were highly abundant in suppression subtractive hybridization (SSH) libraries, which were enriched for transcripts responsive to pathogens or other stressors. These sequences represent genes that potentially play an important role in stress and/or immune responses, making the microarray particularly useful for studies of Atlantic cod gene expression responses to immune stimuli and other stressors. To demonstrate its value, we used the microarray to analyze the Atlantic cod spleen response to stimulation with formalin-killed, atypical Aeromonas salmonicida, resulting in a gene expression profile that indicates a strong innate immune response. These results were further validated by quantitative PCR analysis and comparison to results from previous analysis of an SSH library. This study shows that the Atlantic cod 20K oligonucleotide microarray is a valuable new tool for Atlantic cod functional genomics research.

Interim Report on SNP analysis and forensic microarray probe design for South American hemorrhagic fever viruses, tick-borne encephalitis virus, henipaviruses, Old World Arenaviruses, filoviruses, Crimean-Congo hemorrhagic fever viruses, Rift Valley fever

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jaing, C; Gardner, S

The goal of this project is to develop forensic genotyping assays for select agent viruses, enhancing the current capabilities for the viral bioforensics and law enforcement community. We used a multipronged approach combining bioinformatics analysis, PCR-enriched samples, microarrays and TaqMan assays to develop high resolution and cost effective genotyping methods for strain level forensic discrimination of viruses. We have leveraged substantial experience and efficiency gained through year 1 on software development, SNP discovery, TaqMan signature design and phylogenetic signature mapping to scale up the development of forensics signatures in year 2. In this report, we have summarized the whole genomemore » wide SNP analysis and microarray probe design for forensics characterization of South American hemorrhagic fever viruses, tick-borne encephalitis viruses and henipaviruses, Old World Arenaviruses, filoviruses, Crimean-Congo hemorrhagic fever virus, Rift Valley fever virus and Japanese encephalitis virus.« less

Dissection of the cis-2-decenoic acid signaling network in Pseudomonas aeruginosa using microarray technique

PubMed Central

Rahmani-Badi, Azadeh; Sepehr, Shayesteh; Fallahi, Hossein; Heidari-Keshel, Saeed

2015-01-01

Many bacterial pathogens use quorum-sensing (QS) signaling to regulate the expression of factors contributing to virulence and persistence. Bacteria produce signals of different chemical classes. The signal molecule, known as diffusible signal factor (DSF), is a cis-unsaturated fatty acid that was first described in the plant pathogen Xanthomonas campestris. Previous works have shown that human pathogen, Pseudomonas aeruginosa, also synthesizes a structurally related molecule, characterized as cis-2-decenoic acid (C10: Δ2, CDA) that induces biofilm dispersal by multiple types of bacteria. Furthermore, CDA has been shown to be involved in inter-kingdom signaling that modulates fungal behavior. Therefore, an understanding of its signaling mechanism could suggest strategies for interference, with consequences for disease control. To identify the components of CDA signaling pathway in this pathogen, a comparative transcritpome analysis was conducted, in the presence and absence of CDA. A protein-protein interaction (PPI) network for differentially expressed (DE) genes with known function was then constructed by STRING and Cytoscape. In addition, the effects of CDA in combination with antimicrobial agents on the biofilm surface area and bacteria viability were evaluated using fluorescence microscopy and digital image analysis. Microarray analysis identified 666 differentially expressed genes in the presence of CDA and gene ontology (GO) analysis revealed that in P. aeruginosa, CDA mediates dispersion of biofilms through signaling pathways, including enhanced motility, metabolic activity, virulence as well as persistence at different temperatures. PPI data suggested that a cluster of five genes (PA4978, PA4979, PA4980, PA4982, PA4983) is involved in the CDA synthesis and perception. Combined treatments using both CDA and antimicrobial agents showed that following exposure of the biofilms to CDA, remaining cells on the surface were easily removed and killed by antimicrobials. PMID:25972860

Multi-membership gene regulation in pathway based microarray analysis

PubMed Central

2011-01-01

Background Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. Results We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. Conclusions We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes. PMID:21939531

Multi-membership gene regulation in pathway based microarray analysis.

PubMed

Pavlidis, Stelios P; Payne, Annette M; Swift, Stephen M

2011-09-22

Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.

Quantitative comparison of microarray experiments with published leukemia related gene expression signatures.

PubMed

Klein, Hans-Ulrich; Ruckert, Christian; Kohlmann, Alexander; Bullinger, Lars; Thiede, Christian; Haferlach, Torsten; Dugas, Martin

2009-12-15

Multiple gene expression signatures derived from microarray experiments have been published in the field of leukemia research. A comparison of these signatures with results from new experiments is useful for verification as well as for interpretation of the results obtained. Currently, the percentage of overlapping genes is frequently used to compare published gene signatures against a signature derived from a new experiment. However, it has been shown that the percentage of overlapping genes is of limited use for comparing two experiments due to the variability of gene signatures caused by different array platforms or assay-specific influencing parameters. Here, we present a robust approach for a systematic and quantitative comparison of published gene expression signatures with an exemplary query dataset. A database storing 138 leukemia-related published gene signatures was designed. Each gene signature was manually annotated with terms according to a leukemia-specific taxonomy. Two analysis steps are implemented to compare a new microarray dataset with the results from previous experiments stored and curated in the database. First, the global test method is applied to assess gene signatures and to constitute a ranking among them. In a subsequent analysis step, the focus is shifted from single gene signatures to chromosomal aberrations or molecular mutations as modeled in the taxonomy. Potentially interesting disease characteristics are detected based on the ranking of gene signatures associated with these aberrations stored in the database. Two example analyses are presented. An implementation of the approach is freely available as web-based application. The presented approach helps researchers to systematically integrate the knowledge derived from numerous microarray experiments into the analysis of a new dataset. By means of example leukemia datasets we demonstrate that this approach detects related experiments as well as related molecular mutations and may help to interpret new microarray data.

ValWorkBench: an open source Java library for cluster validation, with applications to microarray data analysis.

PubMed

Giancarlo, R; Scaturro, D; Utro, F

2015-02-01

The prediction of the number of clusters in a dataset, in particular microarrays, is a fundamental task in biological data analysis, usually performed via validation measures. Unfortunately, it has received very little attention and in fact there is a growing need for software tools/libraries dedicated to it. Here we present ValWorkBench, a software library consisting of eleven well known validation measures, together with novel heuristic approximations for some of them. The main objective of this paper is to provide the interested researcher with the full software documentation of an open source cluster validation platform having the main features of being easily extendible in a homogeneous way and of offering software components that can be readily re-used. Consequently, the focus of the presentation is on the architecture of the library, since it provides an essential map that can be used to access the full software documentation, which is available at the supplementary material website [1]. The mentioned main features of ValWorkBench are also discussed and exemplified, with emphasis on software abstraction design and re-usability. A comparison with existing cluster validation software libraries, mainly in terms of the mentioned features, is also offered. It suggests that ValWorkBench is a much needed contribution to the microarray software development/algorithm engineering community. For completeness, it is important to mention that previous accurate algorithmic experimental analysis of the relative merits of each of the implemented measures [19,23,25], carried out specifically on microarray data, gives useful insights on the effectiveness of ValWorkBench for cluster validation to researchers in the microarray community interested in its use for the mentioned task. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Microarray analysis of the rat lacrimal gland following the loss of parasympathetic control of secretion

PubMed Central

Nguyen, Doan H.; Toshida, Hiroshi; Schurr, Jill; Beuerman, Roger W.

2010-01-01

Previous studies showed that loss of muscarinic parasympathetic input to the lacrimal gland (LG) leads to a dramatic reduction in tear secretion and profound changes to LG structure. In this study, we used DNA microarrays to examine the regulation of the gene expression of the genes for secretory function and organization of the LG. Long-Evans rats anesthetized with a mixture of ketamine/xylazine (80:10 mg/kg) underwent unilateral sectioning of the greater superficial petrosal nerve, the input to the pterygopalatine ganglion. After 7 days, tear secretion was measured, the animals were killed, and structural changes in the LG were examined by light microscopy. Total RNA from control and experimental LGs (n = 5) was used for DNA microarray analysis employing the U34A GeneChip. Three statistical algorithms (detection, change call, and signal log ratio) were used to determine differential gene expression using the Microarray Suite (5.0) and Data Mining Tools (3.0). Tear secretion was significantly reduced and corneal ulcers developed in all experimental eyes. Light microscopy showed breakdown of the acinar structure of the LG. DNA microarray analysis showed downregulation of genes associated with the endoplasmic reticulum and Golgi, including genes involved in protein folding and processing. Conversely, transcripts for cytoskeleton and extracellular matrix components, inflammation, and apoptosis were upregulated. The number of significantly upregulated genes (116) was substantially greater than the number of downregulated genes (49). Removal of the main secretory input to the rat LG resulted in clinical symptoms associated with severe dry eye. Components of the secretory pathway were negatively affected, and the increase in cell proliferation and inflammation may lead to loss of organization in the parasympathectomized lacrimal gland. PMID:15084711

Identification of novel and known oocyte-specific genes using complementary DNA subtraction and microarray analysis in three different species.

PubMed

Vallée, Maud; Gravel, Catherine; Palin, Marie-France; Reghenas, Hélène; Stothard, Paul; Wishart, David S; Sirard, Marc-André

2005-07-01

The main objective of the present study was to identify novel oocyte-specific genes in three different species: bovine, mouse, and Xenopus laevis. To achieve this goal, two powerful technologies were combined: a polymerase chain reaction (PCR)-based cDNA subtraction, and cDNA microarrays. Three subtractive libraries consisting of 3456 clones were established and enriched for oocyte-specific transcripts. Sequencing analysis of the positive insert-containing clones resulted in the following classification: 53% of the clones corresponded to known cDNAs, 26% were classified as uncharacterized cDNAs, and a final 9% were classified as novel sequences. All these clones were used for cDNA microarray preparation. Results from these microarray analyses revealed that in addition to already known oocyte-specific genes, such as GDF9, BMP15, and ZP, known genes with unknown function in the oocyte were identified, such as a MLF1-interacting protein (MLF1IP), B-cell translocation gene 4 (BTG4), and phosphotyrosine-binding protein (xPTB). Furthermore, 15 novel oocyte-specific genes were validated by reverse transcription-PCR to confirm their preferential expression in the oocyte compared to somatic tissues. The results obtained in the present study confirmed that microarray analysis is a robust technique to identify true positives from the suppressive subtractive hybridization experiment. Furthermore, obtaining oocyte-specific genes from three species simultaneously allowed us to look at important genes that are conserved across species. Further characterization of these novel oocyte-specific genes will lead to a better understanding of the molecular mechanisms related to the unique functions found in the oocyte.

Glycome Diagnosis of Human Induced Pluripotent Stem Cells Using Lectin Microarray*

PubMed Central

Tateno, Hiroaki; Toyota, Masashi; Saito, Shigeru; Onuma, Yasuko; Ito, Yuzuru; Hiemori, Keiko; Fukumura, Mihoko; Matsushima, Asako; Nakanishi, Mio; Ohnuma, Kiyoshi; Akutsu, Hidenori; Umezawa, Akihiro; Horimoto, Katsuhisa; Hirabayashi, Jun; Asashima, Makoto

2011-01-01

Induced pluripotent stem cells (iPSCs) can now be produced from various somatic cell (SC) lines by ectopic expression of the four transcription factors. Although the procedure has been demonstrated to induce global change in gene and microRNA expressions and even epigenetic modification, it remains largely unknown how this transcription factor-induced reprogramming affects the total glycan repertoire expressed on the cells. Here we performed a comprehensive glycan analysis using 114 types of human iPSCs generated from five different SCs and compared their glycomes with those of human embryonic stem cells (ESCs; nine cell types) using a high density lectin microarray. In unsupervised cluster analysis of the results obtained by lectin microarray, both undifferentiated iPSCs and ESCs were clustered as one large group. However, they were clearly separated from the group of differentiated SCs, whereas all of the four SCs had apparently distinct glycome profiles from one another, demonstrating that SCs with originally distinct glycan profiles have acquired those similar to ESCs upon induction of pluripotency. Thirty-eight lectins discriminating between SCs and iPSCs/ESCs were statistically selected, and characteristic features of the pluripotent state were then obtained at the level of the cellular glycome. The expression profiles of relevant glycosyltransferase genes agreed well with the results obtained by lectin microarray. Among the 38 lectins, rBC2LCN was found to detect only undifferentiated iPSCs/ESCs and not differentiated SCs. Hence, the high density lectin microarray has proved to be valid for not only comprehensive analysis of glycans but also diagnosis of stem cells under the concept of the cellular glycome. PMID:21471226

Microarray analysis of gene expression profiles in ripening pineapple fruits.

PubMed

Koia, Jonni H; Moyle, Richard L; Botella, Jose R

2012-12-18

Pineapple (Ananas comosus) is a tropical fruit crop of significant commercial importance. Although the physiological changes that occur during pineapple fruit development have been well characterized, little is known about the molecular events that occur during the fruit ripening process. Understanding the molecular basis of pineapple fruit ripening will aid the development of new varieties via molecular breeding or genetic modification. In this study we developed a 9277 element pineapple microarray and used it to profile gene expression changes that occur during pineapple fruit ripening. Microarray analyses identified 271 unique cDNAs differentially expressed at least 1.5-fold between the mature green and mature yellow stages of pineapple fruit ripening. Among these 271 sequences, 184 share significant homology with genes encoding proteins of known function, 53 share homology with genes encoding proteins of unknown function and 34 share no significant homology with any database accession. Of the 237 pineapple sequences with homologs, 160 were up-regulated and 77 were down-regulated during pineapple fruit ripening. DAVID Functional Annotation Cluster (FAC) analysis of all 237 sequences with homologs revealed confident enrichment scores for redox activity, organic acid metabolism, metalloenzyme activity, glycolysis, vitamin C biosynthesis, antioxidant activity and cysteine peptidase activity, indicating the functional significance and importance of these processes and pathways during pineapple fruit development. Quantitative real-time PCR analysis validated the microarray expression results for nine out of ten genes tested. This is the first report of a microarray based gene expression study undertaken in pineapple. Our bioinformatic analyses of the transcript profiles have identified a number of genes, processes and pathways with putative involvement in the pineapple fruit ripening process. This study extends our knowledge of the molecular basis of pineapple fruit ripening and non-climacteric fruit ripening in general.

Microarray analysis of gene expression profiles in ripening pineapple fruits

PubMed Central

2012-01-01

Background Pineapple (Ananas comosus) is a tropical fruit crop of significant commercial importance. Although the physiological changes that occur during pineapple fruit development have been well characterized, little is known about the molecular events that occur during the fruit ripening process. Understanding the molecular basis of pineapple fruit ripening will aid the development of new varieties via molecular breeding or genetic modification. In this study we developed a 9277 element pineapple microarray and used it to profile gene expression changes that occur during pineapple fruit ripening. Results Microarray analyses identified 271 unique cDNAs differentially expressed at least 1.5-fold between the mature green and mature yellow stages of pineapple fruit ripening. Among these 271 sequences, 184 share significant homology with genes encoding proteins of known function, 53 share homology with genes encoding proteins of unknown function and 34 share no significant homology with any database accession. Of the 237 pineapple sequences with homologs, 160 were up-regulated and 77 were down-regulated during pineapple fruit ripening. DAVID Functional Annotation Cluster (FAC) analysis of all 237 sequences with homologs revealed confident enrichment scores for redox activity, organic acid metabolism, metalloenzyme activity, glycolysis, vitamin C biosynthesis, antioxidant activity and cysteine peptidase activity, indicating the functional significance and importance of these processes and pathways during pineapple fruit development. Quantitative real-time PCR analysis validated the microarray expression results for nine out of ten genes tested. Conclusions This is the first report of a microarray based gene expression study undertaken in pineapple. Our bioinformatic analyses of the transcript profiles have identified a number of genes, processes and pathways with putative involvement in the pineapple fruit ripening process. This study extends our knowledge of the molecular basis of pineapple fruit ripening and non-climacteric fruit ripening in general. PMID:23245313

Controlling false-negative errors in microarray differential expression analysis: a PRIM approach.

PubMed

Cole, Steve W; Galic, Zoran; Zack, Jerome A

2003-09-22

Theoretical considerations suggest that current microarray screening algorithms may fail to detect many true differences in gene expression (Type II analytic errors). We assessed 'false negative' error rates in differential expression analyses by conventional linear statistical models (e.g. t-test), microarray-adapted variants (e.g. SAM, Cyber-T), and a novel strategy based on hold-out cross-validation. The latter approach employs the machine-learning algorithm Patient Rule Induction Method (PRIM) to infer minimum thresholds for reliable change in gene expression from Boolean conjunctions of fold-induction and raw fluorescence measurements. Monte Carlo analyses based on four empirical data sets show that conventional statistical models and their microarray-adapted variants overlook more than 50% of genes showing significant up-regulation. Conjoint PRIM prediction rules recover approximately twice as many differentially expressed transcripts while maintaining strong control over false-positive (Type I) errors. As a result, experimental replication rates increase and total analytic error rates decline. RT-PCR studies confirm that gene inductions detected by PRIM but overlooked by other methods represent true changes in mRNA levels. PRIM-based conjoint inference rules thus represent an improved strategy for high-sensitivity screening of DNA microarrays. Freestanding JAVA application at http://microarray.crump.ucla.edu/focus

Strategies for comparing gene expression profiles from different microarray platforms: application to a case-control experiment.

PubMed

Severgnini, Marco; Bicciato, Silvio; Mangano, Eleonora; Scarlatti, Francesca; Mezzelani, Alessandra; Mattioli, Michela; Ghidoni, Riccardo; Peano, Clelia; Bonnal, Raoul; Viti, Federica; Milanesi, Luciano; De Bellis, Gianluca; Battaglia, Cristina

2006-06-01

Meta-analysis of microarray data is increasingly important, considering both the availability of multiple platforms using disparate technologies and the accumulation in public repositories of data sets from different laboratories. We addressed the issue of comparing gene expression profiles from two microarray platforms by devising a standardized investigative strategy. We tested this procedure by studying MDA-MB-231 cells, which undergo apoptosis on treatment with resveratrol. Gene expression profiles were obtained using high-density, short-oligonucleotide, single-color microarray platforms: GeneChip (Affymetrix) and CodeLink (Amersham). Interplatform analyses were carried out on 8414 common transcripts represented on both platforms, as identified by LocusLink ID, representing 70.8% and 88.6% of annotated GeneChip and CodeLink features, respectively. We identified 105 differentially expressed genes (DEGs) on CodeLink and 42 DEGs on GeneChip. Among them, only 9 DEGs were commonly identified by both platforms. Multiple analyses (BLAST alignment of probes with target sequences, gene ontology, literature mining, and quantitative real-time PCR) permitted us to investigate the factors contributing to the generation of platform-dependent results in single-color microarray experiments. An effective approach to cross-platform comparison involves microarrays of similar technologies, samples prepared by identical methods, and a standardized battery of bioinformatic and statistical analyses.

Development and Comparison of Two Assay Formats for Parallel Detection of Four Biothreat Pathogens by Using Suspension Microarrays

PubMed Central

Janse, Ingmar; Bok, Jasper M.; Hamidjaja, Raditijo A.; Hodemaekers, Hennie M.; van Rotterdam, Bart J.

2012-01-01

Microarrays provide a powerful analytical tool for the simultaneous detection of multiple pathogens. We developed diagnostic suspension microarrays for sensitive and specific detection of the biothreat pathogens Bacillus anthracis, Yersinia pestis, Francisella tularensis and Coxiella burnetii. Two assay chemistries for amplification and labeling were developed, one method using direct hybridization and the other using target-specific primer extension, combined with hybridization to universal arrays. Asymmetric PCR products for both assay chemistries were produced by using a multiplex asymmetric PCR amplifying 16 DNA signatures (16-plex). The performances of both assay chemistries were compared and their advantages and disadvantages are discussed. The developed microarrays detected multiple signature sequences and an internal control which made it possible to confidently identify the targeted pathogens and assess their virulence potential. The microarrays were highly specific and detected various strains of the targeted pathogens. Detection limits for the different pathogen signatures were similar or slightly higher compared to real-time PCR. Probit analysis showed that even a few genomic copies could be detected with 95% confidence. The microarrays detected DNA from different pathogens mixed in different ratios and from spiked or naturally contaminated samples. The assays that were developed have a potential for application in surveillance and diagnostics. PMID:22355407

Microarray platform affords improved product analysis in mammalian cell growth studies

PubMed Central

Li, Lingyun; Migliore, Nicole; Schaefer, Eugene; Sharfstein, Susan T.; Dordick, Jonathan S.; Linhardt, Robert J.

2014-01-01

High throughput (HT) platforms serve as cost-efficient and rapid screening method for evaluating the effect of cell culture conditions and screening of chemicals. The aim of the current study was to develop a high-throughput cell-based microarray platform to assess the effect of culture conditions on Chinese hamster ovary (CHO) cells. Specifically, growth, transgene expression and metabolism of a GS/MSX CHO cell line, which produces a therapeutic monoclonal antibody, was examined using microarray system in conjunction with conventional shake flask platform in a non-proprietary medium. The microarray system consists of 60 nl spots of cells encapsulated in alginate and separated in groups via an 8-well chamber system attached to the chip. Results show the non-proprietary medium developed allows cell growth, production and normal glycosylation of recombinant antibody and metabolism of the recombinant CHO cells in both the microarray and shake flask platforms. In addition, 10.3 mM glutamate addition to the defined base media results in lactate metabolism shift in the recombinant GS/MSX CHO cells in the shake flask platform. Ultimately, the results demonstrate that the high-throughput microarray platform has the potential to be utilized for evaluating the impact of media additives on cellular processes, such as, cell growth, metabolism and productivity. PMID:24227746

Development and comparison of two assay formats for parallel detection of four biothreat pathogens by using suspension microarrays.

PubMed

Janse, Ingmar; Bok, Jasper M; Hamidjaja, Raditijo A; Hodemaekers, Hennie M; van Rotterdam, Bart J

2012-01-01

Microarrays provide a powerful analytical tool for the simultaneous detection of multiple pathogens. We developed diagnostic suspension microarrays for sensitive and specific detection of the biothreat pathogens Bacillus anthracis, Yersinia pestis, Francisella tularensis and Coxiella burnetii. Two assay chemistries for amplification and labeling were developed, one method using direct hybridization and the other using target-specific primer extension, combined with hybridization to universal arrays. Asymmetric PCR products for both assay chemistries were produced by using a multiplex asymmetric PCR amplifying 16 DNA signatures (16-plex). The performances of both assay chemistries were compared and their advantages and disadvantages are discussed. The developed microarrays detected multiple signature sequences and an internal control which made it possible to confidently identify the targeted pathogens and assess their virulence potential. The microarrays were highly specific and detected various strains of the targeted pathogens. Detection limits for the different pathogen signatures were similar or slightly higher compared to real-time PCR. Probit analysis showed that even a few genomic copies could be detected with 95% confidence. The microarrays detected DNA from different pathogens mixed in different ratios and from spiked or naturally contaminated samples. The assays that were developed have a potential for application in surveillance and diagnostics.

Assessment of data processing to improve reliability of microarray experiments using genomic DNA reference.

PubMed

Yang, Yunfeng; Zhu, Mengxia; Wu, Liyou; Zhou, Jizhong

2008-09-16

Using genomic DNA as common reference in microarray experiments has recently been tested by different laboratories. Conflicting results have been reported with regard to the reliability of microarray results using this method. To explain it, we hypothesize that data processing is a critical element that impacts the data quality. Microarray experiments were performed in a gamma-proteobacterium Shewanella oneidensis. Pair-wise comparison of three experimental conditions was obtained either with two labeled cDNA samples co-hybridized to the same array, or by employing Shewanella genomic DNA as a standard reference. Various data processing techniques were exploited to reduce the amount of inconsistency between both methods and the results were assessed. We discovered that data quality was significantly improved by imposing the constraint of minimal number of replicates, logarithmic transformation and random error analyses. These findings demonstrate that data processing significantly influences data quality, which provides an explanation for the conflicting evaluation in the literature. This work could serve as a guideline for microarray data analysis using genomic DNA as a standard reference.

Improved microarray methods for profiling the yeast knockout strain collection

PubMed Central

Yuan, Daniel S.; Pan, Xuewen; Ooi, Siew Loon; Peyser, Brian D.; Spencer, Forrest A.; Irizarry, Rafael A.; Boeke, Jef D.

2005-01-01

A remarkable feature of the Yeast Knockout strain collection is the presence of two unique 20mer TAG sequences in almost every strain. In principle, the relative abundances of strains in a complex mixture can be profiled swiftly and quantitatively by amplifying these sequences and hybridizing them to microarrays, but TAG microarrays have not been widely used. Here, we introduce a TAG microarray design with sophisticated controls and describe a robust method for hybridizing high concentrations of dye-labeled TAGs in single-stranded form. We also highlight the importance of avoiding PCR contamination and provide procedures for detection and eradication. Validation experiments using these methods yielded false positive (FP) and false negative (FN) rates for individual TAG detection of 3–6% and 15–18%, respectively. Analysis demonstrated that cross-hybridization was the chief source of FPs, while TAG amplification defects were the main cause of FNs. The materials, protocols, data and associated software described here comprise a suite of experimental resources that should facilitate the use of TAG microarrays for a wide variety of genetic screens. PMID:15994458

An efficient method to identify differentially expressed genes in microarray experiments

PubMed Central

Qin, Huaizhen; Feng, Tao; Harding, Scott A.; Tsai, Chung-Jui; Zhang, Shuanglin

2013-01-01

Motivation Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss. Results We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes. Availability The C++ code to implement the proposed method is available upon request for academic use. PMID:18453554

Clustering approaches to identifying gene expression patterns from DNA microarray data.

PubMed

Do, Jin Hwan; Choi, Dong-Kug

2008-04-30

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

Specific Discrimination of Three Pathogenic Salmonella enterica subsp. enterica Serotypes by carB-Based Oligonucleotide Microarray

PubMed Central

Shin, Hwa Hui; Hwang, Byeong Hee; Seo, Jeong Hyun

2014-01-01

It is important to rapidly and selectively detect and analyze pathogenic Salmonella enterica subsp. enterica in contaminated food to reduce the morbidity and mortality of Salmonella infection and to guarantee food safety. In the present work, we developed an oligonucleotide microarray containing duplicate specific capture probes based on the carB gene, which encodes the carbamoyl phosphate synthetase large subunit, as a competent biomarker evaluated by genetic analysis to selectively and efficiently detect and discriminate three S. enterica subsp. enterica serotypes: Choleraesuis, Enteritidis, and Typhimurium. Using the developed microarray system, three serotype targets were successfully analyzed in a range as low as 1.6 to 3.1 nM and were specifically discriminated from each other without nonspecific signals. In addition, the constructed microarray did not have cross-reactivity with other common pathogenic bacteria and even enabled the clear discrimination of the target Salmonella serotype from a bacterial mixture. Therefore, these results demonstrated that our novel carB-based oligonucleotide microarray can be used as an effective and specific detection system for S. enterica subsp. enterica serotypes. PMID:24185846

Specific discrimination of three pathogenic Salmonella enterica subsp. enterica serotypes by carB-based oligonucleotide microarray.

PubMed

Shin, Hwa Hui; Hwang, Byeong Hee; Seo, Jeong Hyun; Cha, Hyung Joon

2014-01-01

It is important to rapidly and selectively detect and analyze pathogenic Salmonella enterica subsp. enterica in contaminated food to reduce the morbidity and mortality of Salmonella infection and to guarantee food safety. In the present work, we developed an oligonucleotide microarray containing duplicate specific capture probes based on the carB gene, which encodes the carbamoyl phosphate synthetase large subunit, as a competent biomarker evaluated by genetic analysis to selectively and efficiently detect and discriminate three S. enterica subsp. enterica serotypes: Choleraesuis, Enteritidis, and Typhimurium. Using the developed microarray system, three serotype targets were successfully analyzed in a range as low as 1.6 to 3.1 nM and were specifically discriminated from each other without nonspecific signals. In addition, the constructed microarray did not have cross-reactivity with other common pathogenic bacteria and even enabled the clear discrimination of the target Salmonella serotype from a bacterial mixture. Therefore, these results demonstrated that our novel carB-based oligonucleotide microarray can be used as an effective and specific detection system for S. enterica subsp. enterica serotypes.

Gene expression profiling and pathway analysis in MCF-7 and MDA-MB-231 human breast cancer cell lines treated with dioscin

USDA-ARS?s Scientific Manuscript database

The long-term goal of our study is to understand the genetic and epigenetic mechanisms of breast cancer metastasis in human and to discover new possible genetic markers for use in clinical practice. We have used microarray technology (Human OneArray microarray, phylanxbiotech.com) to compare gene ex...

Microarray-Based Mapping for the Detection of Molecular Markers in Response to Aspergillus flavus Infection in Susceptible and Resistant Maize Lines

USDA-ARS?s Scientific Manuscript database

The objectives of this study were (1) to evaluate differential gene expression levels for resistance to A. flavus kernel infection in susceptible (Va35) and resistant (Mp313E) maize lines using Oligonucleotide and cDNA microarray analysis, (2) to evaluate differences in A. flavus accumulation betwee...

ABRF-MARG RESEARCH STUDY: EVALUATION OF SMALL SAMPLE NUCLEIC ACID AMPLIFICATION TECHNOLOGIES FOR GENE EXPRESSION PROFILING

EPA Science Inventory

Microarrays have had a significant impact on many areas of biology. However, there are still many fertile research areas that would benefit from microarray analysis but are limited by the amount of biological material that can be obtained (e.g. samples obtained by small biopsy, f...

Customizing microarrays for neuroscience drug discovery.

PubMed

Girgenti, Matthew J; Newton, Samuel S

2007-08-01

Microarray-based gene profiling has become the centerpiece of gene expression studies in the biological sciences. The ability to now interrogate the entire genome using a single chip demonstrates the progress in technology and instrumentation that has been made over the last two decades. Although this unbiased approach provides researchers with an immense quantity of data, obtaining meaningful insight is not possible without intensive data analysis and processing. Custom developed arrays have emerged as a viable and attractive alternative that can take advantage of this robust technology and tailor it to suit the needs and requirements of individual investigations. The ability to simplify data analysis, reduce noise and carefully optimize experimental conditions makes it a suitable tool that can be effectively utilized in neuroscience drug discovery efforts. Furthermore, incorporating recent advancements in fine focusing gene profiling to include specific cellular phenotypes can help resolve the complex cellular heterogeneity of the brain. This review surveys the use of microarray technology in neuroscience paying special attention to customized arrays and their potential in drug discovery. Novel applications of microarrays and ancillary techniques, such as laser microdissection, FAC sorting and RNA amplification, have also been discussed. The notion that a hypothesis-driven approach can be integrated into drug development programs is highlighted.

Methodological Challenges in Protein Microarray and Immunohistochemistry for the Discovery of Novel Autoantibodies in Paediatric Acute Disseminated Encephalomyelitis

PubMed Central

Peschl, Patrick; Ramberger, Melanie; Höftberger, Romana; Jöhrer, Karin; Baumann, Matthias; Rostásy, Kevin; Reindl, Markus

2017-01-01

Acute disseminated encephalomyelitis (ADEM) is a rare autoimmune-mediated demyelinating disease affecting mainly children and young adults. Differentiation to multiple sclerosis is not always possible, due to overlapping clinical symptoms and recurrent and multiphasic forms. Until now, immunoglobulins reactive to myelin oligodendrocyte glycoprotein (MOG antibodies) have been found in a subset of patients with ADEM. However, there are still patients lacking autoantibodies, necessitating the identification of new autoantibodies as biomarkers in those patients. Therefore, we aimed to identify novel autoantibody targets in ADEM patients. Sixteen ADEM patients (11 seronegative, 5 seropositive for MOG antibodies) were analysed for potential new biomarkers, using a protein microarray and immunohistochemistry on rat brain tissue to identify antibodies against intracellular and surface neuronal and glial antigens. Nine candidate antigens were identified in the protein microarray analysis in at least two patients per group. Immunohistochemistry on rat brain tissue did not reveal new target antigens. Although no new autoantibody targets could be found here, future studies should aim to identify new biomarkers for therapeutic and prognostic purposes. The microarray analysis and immunohistochemistry methods used here have several limitations, which should be considered in future searches for biomarkers. PMID:28327523

Microarray gene expression profiling analysis combined with bioinformatics in multiple sclerosis.

PubMed

Liu, Mingyuan; Hou, Xiaojun; Zhang, Ping; Hao, Yong; Yang, Yiting; Wu, Xiongfeng; Zhu, Desheng; Guan, Yangtai

2013-05-01

Multiple sclerosis (MS) is the most prevalent demyelinating disease and the principal cause of neurological disability in young adults. Recent microarray gene expression profiling studies have identified several genetic variants contributing to the complex pathogenesis of MS, however, expressional and functional studies are still required to further understand its molecular mechanism. The present study aimed to analyze the molecular mechanism of MS using microarray analysis combined with bioinformatics techniques. We downloaded the gene expression profile of MS from Gene Expression Omnibus (GEO) and analysed the microarray data using the differentially coexpressed genes (DCGs) and links package in R and Database for Annotation, Visualization and Integrated Discovery. The regulatory impact factor (RIF) algorithm was used to measure the impact factor of transcription factor. A total of 1,297 DCGs between MS patients and healthy controls were identified. Functional annotation indicated that these DCGs were associated with immune and neurological functions. Furthermore, the RIF result suggested that IKZF1, BACH1, CEBPB, EGR1, FOS may play central regulatory roles in controlling gene expression in the pathogenesis of MS. Our findings confirm the presence of multiple molecular alterations in MS and indicate the possibility for identifying prognostic factors associated with MS pathogenesis.

Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: application to measurement of oestrogen and progesterone receptor levels in breast cancer.

PubMed

Rexhepaj, Elton; Brennan, Donal J; Holloway, Peter; Kay, Elaine W; McCann, Amanda H; Landberg, Goran; Duffy, Michael J; Jirstrom, Karin; Gallagher, William M

2008-01-01

Manual interpretation of immunohistochemistry (IHC) is a subjective, time-consuming and variable process, with an inherent intra-observer and inter-observer variability. Automated image analysis approaches offer the possibility of developing rapid, uniform indicators of IHC staining. In the present article we describe the development of a novel approach for automatically quantifying oestrogen receptor (ER) and progesterone receptor (PR) protein expression assessed by IHC in primary breast cancer. Two cohorts of breast cancer patients (n = 743) were used in the study. Digital images of breast cancer tissue microarrays were captured using the Aperio ScanScope XT slide scanner (Aperio Technologies, Vista, CA, USA). Image analysis algorithms were developed using MatLab 7 (MathWorks, Apple Hill Drive, MA, USA). A fully automated nuclear algorithm was developed to discriminate tumour from normal tissue and to quantify ER and PR expression in both cohorts. Random forest clustering was employed to identify optimum thresholds for survival analysis. The accuracy of the nuclear algorithm was initially confirmed by a histopathologist, who validated the output in 18 representative images. In these 18 samples, an excellent correlation was evident between the results obtained by manual and automated analysis (Spearman's rho = 0.9, P < 0.001). Optimum thresholds for survival analysis were identified using random forest clustering. This revealed 7% positive tumour cells as the optimum threshold for the ER and 5% positive tumour cells for the PR. Moreover, a 7% cutoff level for the ER predicted a better response to tamoxifen than the currently used 10% threshold. Finally, linear regression was employed to demonstrate a more homogeneous pattern of expression for the ER (R = 0.860) than for the PR (R = 0.681). In summary, we present data on the automated quantification of the ER and the PR in 743 primary breast tumours using a novel unsupervised image analysis algorithm. This novel approach provides a useful tool for the quantification of biomarkers on tissue specimens, as well as for objective identification of appropriate cutoff thresholds for biomarker positivity. It also offers the potential to identify proteins with a homogeneous pattern of expression.

Mining microarray data at NCBI's Gene Expression Omnibus (GEO)*.

PubMed

Barrett, Tanya; Edgar, Ron

2006-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Enhancing the Sensitivity of DNA Microarray Using Dye-Doped Silica Nanoparticles: Detection of Human Papilloma Virus

NASA Astrophysics Data System (ADS)

Enrichi, F.; Riccò, R.; Meneghello, A.; Pierobon, R.; Canton, G.; Cretaio, E.

2010-10-01

DNA microarray is a high-throughput technology used for detection and quantification of nucleic acid molecules and others of biological interest. The analysis is based on the specific hybridization between probe sequences deposited in array and a target ss-DNA amplified by PCR and functionalized by a fluorescent dye. Organic labels have well known disadvantages like photobleaching and low signal intensities, which put a limitation to the lower amount of DNA material that can be detected. Therefore for trace analysis the development of more efficient biomarkers is required. With this aim we present in this paper the synthesis and application of alternative hybrid nanosystems obtained by incorporating standard fluorescent molecules into monodisperse silica nanoparticles. Efficient application to the detection of Human Papilloma Virus is demonstrated. This virus is associated to the formation of cervical cancer, a leading cause of death by cancer for women worldwide. It is shown that the use of the novel biomarkers increases the optical signal of about one order of magnitude with respect to the free dyes or quantum dots in conventional instruments. This is due to the high number of molecules that can be accommodated into each nanoparticle, to the reduced photobleaching and to the improved environmental protection of the dyes when encapsulated in the silica matrix. The cheap and easy synthesis of these luminescent particles, the stability in water, the surface functionalizability and bio-compatibility make them very promising for present and future bio-labeling and bio-imaging applications.

Current-Controlled Electrical Point-Source Stimulation of Embryonic Stem Cells

PubMed Central

Chen, Michael Q.; Xie, Xiaoyan; Wilson, Kitchener D.; Sun, Ning; Wu, Joseph C.; Giovangrandi, Laurent; Kovacs, Gregory T. A.

2010-01-01

Stem cell therapy is emerging as a promising clinical approach for myocardial repair. However, the interactions between the graft and host, resulting in inconsistent levels of integration, remain largely unknown. In particular, the influence of electrical activity of the surrounding host tissue on graft differentiation and integration is poorly understood. In order to study this influence under controlled conditions, an in vitro system was developed. Electrical pacing of differentiating murine embryonic stem (ES) cells was performed at physiologically relevant levels through direct contact with microelectrodes, simulating the local activation resulting from contact with surrounding electroactive tissue. Cells stimulated with a charged balanced voltage-controlled current source for up to 4 days were analyzed for cardiac and ES cell gene expression using real-time PCR, immunofluorescent imaging, and genome microarray analysis. Results varied between ES cells from three progressive differentiation stages and stimulation amplitudes (nine conditions), indicating a high sensitivity to electrical pacing. Conditions that maximally encouraged cardiomyocyte differentiation were found with Day 7 EBs stimulated at 30 µA. The resulting gene expression included a sixfold increase in troponin-T and a twofold increase in β-MHCwithout increasing ES cell proliferation marker Nanog. Subsequent genome microarray analysis revealed broad transcriptome changes after pacing. Concurrent to upregulation of mature gene programs including cardiovascular, neurological, and musculoskeletal systems is the apparent downregulation of important self-renewal and pluripotency genes. Overall, a robust system capable of long-term stimulation of ES cells is demonstrated, and specific conditions are outlined that most encourage cardiomyocyte differentiation. PMID:20652088

Gene Expression Profiling of Gastric Cancer

PubMed Central

Marimuthu, Arivusudar; Jacob, Harrys K.C.; Jakharia, Aniruddha; Subbannayya, Yashwanth; Keerthikumar, Shivakumar; Kashyap, Manoj Kumar; Goel, Renu; Balakrishnan, Lavanya; Dwivedi, Sutopa; Pathare, Swapnali; Dikshit, Jyoti Bajpai; Maharudraiah, Jagadeesha; Singh, Sujay; Sameer Kumar, Ghantasala S; Vijayakumar, M.; Veerendra Kumar, Kariyanakatte Veeraiah; Premalatha, Chennagiri Shrinivasamurthy; Tata, Pramila; Hariharan, Ramesh; Roa, Juan Carlos; Prasad, T.S.K; Chaerkady, Raghothama; Kumar, Rekha Vijay; Pandey, Akhilesh

2015-01-01

Gastric cancer is the second leading cause of cancer death worldwide, both in men and women. A genomewide gene expression analysis was carried out to identify differentially expressed genes in gastric adenocarcinoma tissues as compared to adjacent normal tissues. We used Agilent’s whole human genome oligonucleotide microarray platform representing ~41,000 genes to carry out gene expression analysis. Two-color microarray analysis was employed to directly compare the expression of genes between tumor and normal tissues. Through this approach, we identified several previously known candidate genes along with a number of novel candidate genes in gastric cancer. Testican-1 (SPOCK1) was one of the novel molecules that was 10-fold upregulated in tumors. Using tissue microarrays, we validated the expression of testican-1 by immunohistochemical staining. It was overexpressed in 56% (160/282) of the cases tested. Pathway analysis led to the identification of several networks in which SPOCK1 was among the topmost networks of interacting genes. By gene enrichment analysis, we identified several genes involved in cell adhesion and cell proliferation to be significantly upregulated while those corresponding to metabolic pathways were significantly downregulated. The differentially expressed genes identified in this study are candidate biomarkers for gastric adenoacarcinoma. PMID:27030788

Expression profiling and pathway analysis of Krüppel-like factor 4 in mouse embryonic fibroblasts

PubMed Central

Hagos, Engda G; Ghaleb, Amr M; Kumar, Amrita; Neish, Andrew S; Yang, Vincent W

2011-01-01

Background: Krüppel-like factor 4 (KLF4) is a zinc-finger transcription factor with diverse regulatory functions in proliferation, differentiation, and development. KLF4 also plays a role in inflammation, tumorigenesis, and reprogramming of somatic cells to induced pluripotent stem (iPS) cells. To gain insight into the mechanisms by which KLF4 regulates these processes, we conducted DNA microarray analyses to identify differentially expressed genes in mouse embryonic fibroblasts (MEFs) wild type and null for Klf4. Methods: Expression profiles of fibroblasts isolated from mouse embryos wild type or null for the Klf4 alleles were examined by DNA microarrays. Differentially expressed genes were subjected to the Database for Annotation, Visualization and Integrated Discovery (DAVID). The microarray data were also interrogated with the Ingenuity Pathway Analysis (IPA) and Gene Set Enrichment Analysis (GSEA) for pathway identification. Results obtained from the microarray analysis were confirmed by Western blotting for select genes with biological relevance to determine the correlation between mRNA and protein levels. Results: One hundred and sixty three up-regulated and 88 down-regulated genes were identified that demonstrated a fold-change of at least 1.5 and a P-value < 0.05 in Klf4-null MEFs compared to wild type MEFs. Many of the up-regulated genes in Klf4-null MEFs encode proto-oncogenes, growth factors, extracellular matrix, and cell cycle activators. In contrast, genes encoding tumor suppressors and those involved in JAK-STAT signaling pathways are down-regulated in Klf4-null MEFs. IPA and GSEA also identified various pathways that are regulated by KLF4. Lastly, Western blotting of select target genes confirmed the changes revealed by microarray data. Conclusions: These data are not only consistent with previous functional studies of KLF4's role in tumor suppression and somatic cell reprogramming, but also revealed novel target genes that mediate KLF4's functions. PMID:21892412

An efficient pseudomedian filter for tiling microrrays.

PubMed

Royce, Thomas E; Carriero, Nicholas J; Gerstein, Mark B

2007-06-07

Tiling microarrays are becoming an essential technology in the functional genomics toolbox. They have been applied to the tasks of novel transcript identification, elucidation of transcription factor binding sites, detection of methylated DNA and several other applications in several model organisms. These experiments are being conducted at increasingly finer resolutions as the microarray technology enjoys increasingly greater feature densities. The increased densities naturally lead to increased data analysis requirements. Specifically, the most widely employed algorithm for tiling array analysis involves smoothing observed signals by computing pseudomedians within sliding windows, a O(n2logn) calculation in each window. This poor time complexity is an issue for tiling array analysis and could prove to be a real bottleneck as tiling microarray experiments become grander in scope and finer in resolution. We therefore implemented Monahan's HLQEST algorithm that reduces the runtime complexity for computing the pseudomedian of n numbers to O(nlogn) from O(n2logn). For a representative tiling microarray dataset, this modification reduced the smoothing procedure's runtime by nearly 90%. We then leveraged the fact that elements within sliding windows remain largely unchanged in overlapping windows (as one slides across genomic space) to further reduce computation by an additional 43%. This was achieved by the application of skip lists to maintaining a sorted list of values from window to window. This sorted list could be maintained with simple O(log n) inserts and deletes. We illustrate the favorable scaling properties of our algorithms with both time complexity analysis and benchmarking on synthetic datasets. Tiling microarray analyses that rely upon a sliding window pseudomedian calculation can require many hours of computation. We have eased this requirement significantly by implementing efficient algorithms that scale well with genomic feature density. This result not only speeds the current standard analyses, but also makes possible ones where many iterations of the filter may be required, such as might be required in a bootstrap or parameter estimation setting. Source code and executables are available at http://tiling.gersteinlab.org/pseudomedian/.

An efficient pseudomedian filter for tiling microrrays

PubMed Central

Royce, Thomas E; Carriero, Nicholas J; Gerstein, Mark B

2007-01-01

Background Tiling microarrays are becoming an essential technology in the functional genomics toolbox. They have been applied to the tasks of novel transcript identification, elucidation of transcription factor binding sites, detection of methylated DNA and several other applications in several model organisms. These experiments are being conducted at increasingly finer resolutions as the microarray technology enjoys increasingly greater feature densities. The increased densities naturally lead to increased data analysis requirements. Specifically, the most widely employed algorithm for tiling array analysis involves smoothing observed signals by computing pseudomedians within sliding windows, a O(n2logn) calculation in each window. This poor time complexity is an issue for tiling array analysis and could prove to be a real bottleneck as tiling microarray experiments become grander in scope and finer in resolution. Results We therefore implemented Monahan's HLQEST algorithm that reduces the runtime complexity for computing the pseudomedian of n numbers to O(nlogn) from O(n2logn). For a representative tiling microarray dataset, this modification reduced the smoothing procedure's runtime by nearly 90%. We then leveraged the fact that elements within sliding windows remain largely unchanged in overlapping windows (as one slides across genomic space) to further reduce computation by an additional 43%. This was achieved by the application of skip lists to maintaining a sorted list of values from window to window. This sorted list could be maintained with simple O(log n) inserts and deletes. We illustrate the favorable scaling properties of our algorithms with both time complexity analysis and benchmarking on synthetic datasets. Conclusion Tiling microarray analyses that rely upon a sliding window pseudomedian calculation can require many hours of computation. We have eased this requirement significantly by implementing efficient algorithms that scale well with genomic feature density. This result not only speeds the current standard analyses, but also makes possible ones where many iterations of the filter may be required, such as might be required in a bootstrap or parameter estimation setting. Source code and executables are available at . PMID:17555595

GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms.

PubMed

Jani, Saurin D; Argraves, Gary L; Barth, Jeremy L; Argraves, W Scott

2010-04-01

An important objective of DNA microarray-based gene expression experimentation is determining inter-relationships that exist between differentially expressed genes and biological processes, molecular functions, cellular components, signaling pathways, physiologic processes and diseases. Here we describe GeneMesh, a web-based program that facilitates analysis of DNA microarray gene expression data. GeneMesh relates genes in a query set to categories available in the Medical Subject Headings (MeSH) hierarchical index. The interface enables hypothesis driven relational analysis to a specific MeSH subcategory (e.g., Cardiovascular System, Genetic Processes, Immune System Diseases etc.) or unbiased relational analysis to broader MeSH categories (e.g., Anatomy, Biological Sciences, Disease etc.). Genes found associated with a given MeSH category are dynamically linked to facilitate tabular and graphical depiction of Entrez Gene information, Gene Ontology information, KEGG metabolic pathway diagrams and intermolecular interaction information. Expression intensity values of groups of genes that cluster in relation to a given MeSH category, gene ontology or pathway can be displayed as heat maps of Z score-normalized values. GeneMesh operates on gene expression data derived from a number of commercial microarray platforms including Affymetrix, Agilent and Illumina. GeneMesh is a versatile web-based tool for testing and developing new hypotheses through relating genes in a query set (e.g., differentially expressed genes from a DNA microarray experiment) to descriptors making up the hierarchical structure of the National Library of Medicine controlled vocabulary thesaurus, MeSH. The system further enhances the discovery process by providing links between sets of genes associated with a given MeSH category to a rich set of html linked tabular and graphic information including Entrez Gene summaries, gene ontologies, intermolecular interactions, overlays of genes onto KEGG pathway diagrams and heatmaps of expression intensity values. GeneMesh is freely available online at http://proteogenomics.musc.edu/genemesh/.

High throughput gene expression profiling: a molecular approach to integrative physiology

PubMed Central

Liang, Mingyu; Cowley, Allen W; Greene, Andrew S

2004-01-01

Integrative physiology emphasizes the importance of understanding multiple pathways with overlapping, complementary, or opposing effects and their interactions in the context of intact organisms. The DNA microarray technology, the most commonly used method for high-throughput gene expression profiling, has been touted as an integrative tool that provides insights into regulatory pathways. However, the physiology community has been slow in acceptance of these techniques because of early failure in generating useful data and the lack of a cohesive theoretical framework in which experiments can be analysed. With recent advances in both technology and analysis, we propose a concept of multidimensional integration of physiology that incorporates data generated by DNA microarray and other functional, genomic, and proteomic approaches to achieve a truly integrative understanding of physiology. Analysis of several studies performed in simpler organisms or in mammalian model animals supports the feasibility of such multidimensional integration and demonstrates the power of DNA microarray as an indispensable molecular tool for such integration. Evaluation of DNA microarray techniques indicates that these techniques, despite limitations, have advanced to a point where the question-driven profiling research has become a feasible complement to the conventional, hypothesis-driven research. With a keen sense of homeostasis, global regulation, and quantitative analysis, integrative physiologists are uniquely positioned to apply these techniques to enhance the understanding of complex physiological functions. PMID:14678487

A Comparative Performance Analysis of Multispectral and RGB Imaging on HER2 Status Evaluation for the Prediction of Breast Cancer Prognosis.

PubMed

Liu, Wenlou; Wang, Linwei; Liu, Jiuyang; Yuan, Jingping; Chen, Jiamei; Wu, Han; Xiang, Qingming; Yang, Guifang; Li, Yan

2016-12-01

Despite the extensive application of multispectral imaging (MSI) in biomedical multidisciplinary researches, there is a paucity of data available regarding the implication of MSI in tumor prognosis prediction. We compared the behaviors of multispectral (MS) and conventional red-green-blue (RGB) images on assessment of human epidermal growth factor receptor 2 (HER2) immunohistochemistry to explore their impact on outcome in patients with invasive breast cancer (BC). Tissue microarrays containing 240 BC patients were introduced to compare the performance of MS and RGB imaging methods on the quantitative assessment of HER2 status and the prognostic value of 5-year disease-free survival (5-DFS). Both the total and average signal optical density values of HER2 MS and RGB images were analyzed, and all patients were divided into two groups based on the different 5-DFS. The quantification of HER2 MS images was negatively correlated with 5-DFS in lymph node-negative and -positive patients (P<.05), but RGB images were not in lymph node-positive patients (P=.101). Multivariate analysis indicated that the hazard ratio (HR) of HER2 MS was higher than that of HER2 RGB (HR=2.454; 95% confidence interval [CI], 1.636-3.681 vs HR=2.060; 95% CI, 1.361-3.119). Additionally, area under curve (AUC) by receiver operating characteristic analysis for HER2 MS was greater than that for HER2 RGB (AUC=0.649; 95% CI, 0.577-0.722 vs AUC=0.596; 95% CI, 0.522-0.670) in predicting the risk for recurrence. More importantly, the quantification of HER2 MS images has higher prediction accuracy than that of HER2 RGB images (69.6% vs 65.0%) on 5-DFS. Our study suggested that better information on BC prognosis could be obtained from the quantification of HER2 MS images and MS images might perform better in predicting BC prognosis than conventional RGB images. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis.

PubMed

Pan, Deyun; Sun, Ning; Cheung, Kei-Hoi; Guan, Zhong; Ma, Ligeng; Holford, Matthew; Deng, Xingwang; Zhao, Hongyu

2003-11-07

To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s).

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis

PubMed Central

Pan, Deyun; Sun, Ning; Cheung, Kei-Hoi; Guan, Zhong; Ma, Ligeng; Holford, Matthew; Deng, Xingwang; Zhao, Hongyu

2003-01-01

Background To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. Results We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. Conclusion PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s). PMID:14604444

A Versatile Microarray Platform for Capturing Rare Cells

NASA Astrophysics Data System (ADS)

Brinkmann, Falko; Hirtz, Michael; Haller, Anna; Gorges, Tobias M.; Vellekoop, Michael J.; Riethdorf, Sabine; Müller, Volkmar; Pantel, Klaus; Fuchs, Harald

2015-10-01

Analyses of rare events occurring at extremely low frequencies in body fluids are still challenging. We established a versatile microarray-based platform able to capture single target cells from large background populations. As use case we chose the challenging application of detecting circulating tumor cells (CTCs) - about one cell in a billion normal blood cells. After incubation with an antibody cocktail, targeted cells are extracted on a microarray in a microfluidic chip. The accessibility of our platform allows for subsequent recovery of targets for further analysis. The microarray facilitates exclusion of false positive capture events by co-localization allowing for detection without fluorescent labelling. Analyzing blood samples from cancer patients with our platform reached and partly outreached gold standard performance, demonstrating feasibility for clinical application. Clinical researchers free choice of antibody cocktail without need for altered chip manufacturing or incubation protocol, allows virtual arbitrary targeting of capture species and therefore wide spread applications in biomedical sciences.

LS-CAP: an algorithm for identifying cytogenetic aberrations in hepatocellular carcinoma using microarray data.

PubMed

He, Xianmin; Wei, Qing; Sun, Meiqian; Fu, Xuping; Fan, Sichang; Li, Yao

2006-05-01

Biological techniques such as Array-Comparative genomic hybridization (CGH), fluorescent in situ hybridization (FISH) and affymetrix single nucleotide pleomorphism (SNP) array have been used to detect cytogenetic aberrations. However, on genomic scale, these techniques are labor intensive and time consuming. Comparative genomic microarray analysis (CGMA) has been used to identify cytogenetic changes in hepatocellular carcinoma (HCC) using gene expression microarray data. However, CGMA algorithm can not give precise localization of aberrations, fails to identify small cytogenetic changes, and exhibits false negatives and positives. Locally un-weighted smoothing cytogenetic aberrations prediction (LS-CAP) based on local smoothing and binomial distribution can be expected to address these problems. LS-CAP algorithm was built and used on HCC microarray profiles. Eighteen cytogenetic abnormalities were identified, among them 5 were reported previously, and 12 were proven by CGH studies. LS-CAP effectively reduced the false negatives and positives, and precisely located small fragments with cytogenetic aberrations.

Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data

PubMed Central

2013-01-01

Background The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach. Results We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N > 2 groups. Conclusions The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies. PMID:23822712

Microarray analysis of genes associated with cell surface NIS protein levels in breast cancer.

PubMed

Beyer, Sasha J; Zhang, Xiaoli; Jimenez, Rafael E; Lee, Mei-Ling T; Richardson, Andrea L; Huang, Kun; Jhiang, Sissy M

2011-10-11

Na+/I- symporter (NIS)-mediated iodide uptake allows radioiodine therapy for thyroid cancer. NIS is also expressed in breast tumors, raising potential for radionuclide therapy of breast cancer. However, NIS expression in most breast cancers is low and may not be sufficient for radionuclide therapy. We aimed to identify biomarkers associated with NIS expression such that mechanisms underlying NIS modulation in human breast tumors may be elucidated. Published oligonucleotide microarray data within the National Center for Biotechnology Information Gene Expression Omnibus database were analyzed to identify gene expression tightly correlated with NIS mRNA level among human breast tumors. NIS immunostaining was performed in a tissue microarray composed of 28 human breast tumors which had corresponding oligonucleotide microarray data available for each tumor such that gene expression associated with cell surface NIS protein level could be identified. NIS mRNA levels do not vary among breast tumors or when compared to normal breast tissues when detected by Affymetrix oligonucleotide microarray platforms. Cell surface NIS protein levels are much more variable than their corresponding NIS mRNA levels. Despite a limited number of breast tumors examined, our analysis identified cysteinyl-tRNA synthetase as a biomarker that is highly associated with cell surface NIS protein levels in the ER-positive breast cancer subtype. Further investigation on genes associated with cell surface NIS protein levels within each breast cancer molecular subtype may lead to novel targets for selectively increasing NIS expression/function in a subset of breast cancers patients.

Nutrient control of gene expression in Drosophila: microarray analysis of starvation and sugar-dependent response

PubMed Central

Zinke, Ingo; Schütz, Christina S.; Katzenberger, Jörg D.; Bauer, Matthias; Pankratz, Michael J.

2002-01-01

We have identified genes regulated by starvation and sugar signals in Drosophila larvae using whole-genome microarrays. Based on expression profiles in the two nutrient conditions, they were organized into different categories that reflect distinct physiological pathways mediating sugar and fat metabolism, and cell growth. In the category of genes regulated in sugar-fed, but not in starved, animals, there is an upregulation of genes encoding key enzymes of the fat biosynthesis pathway and a downregulation of genes encoding lipases. The highest and earliest activated gene upon sugar ingestion is sugarbabe, a zinc finger protein that is induced in the gut and the fat body. Identification of potential targets using microarrays suggests that sugarbabe functions to repress genes involved in dietary fat breakdown and absorption. The current analysis provides a basis for studying the genetic mechanisms underlying nutrient signalling. PMID:12426388

Finding Groups in Gene Expression Data

PubMed Central

2005-01-01

The vast potential of the genomic insight offered by microarray technologies has led to their widespread use since they were introduced a decade ago. Application areas include gene function discovery, disease diagnosis, and inferring regulatory networks. Microarray experiments enable large-scale, high-throughput investigations of gene activity and have thus provided the data analyst with a distinctive, high-dimensional field of study. Many questions in this field relate to finding subgroups of data profiles which are very similar. A popular type of exploratory tool for finding subgroups is cluster analysis, and many different flavors of algorithms have been used and indeed tailored for microarray data. Cluster analysis, however, implies a partitioning of the entire data set, and this does not always match the objective. Sometimes pattern discovery or bump hunting tools are more appropriate. This paper reviews these various tools for finding interesting subgroups. PMID:16046827

An integrated bioinformatics approach to improve two-color microarray quality-control: impact on biological conclusions.

PubMed

van Haaften, Rachel I M; Luceri, Cristina; van Erk, Arie; Evelo, Chris T A

2009-06-01

Omics technology used for large-scale measurements of gene expression is rapidly evolving. This work pointed out the need of an extensive bioinformatics analyses for array quality assessment before and after gene expression clustering and pathway analysis. A study focused on the effect of red wine polyphenols on rat colon mucosa was used to test the impact of quality control and normalisation steps on the biological conclusions. The integration of data visualization, pathway analysis and clustering revealed an artifact problem that was solved with an adapted normalisation. We propose a possible point to point standard analysis procedure, based on a combination of clustering and data visualization for the analysis of microarray data.

MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis

PubMed Central

Chu, Vu T; Gottardo, Raphael; Raftery, Adrian E; Bumgarner, Roger E; Yeung, Ka Yee

2008-01-01

We present MeV+R, an integration of the JAVA MultiExperiment Viewer program with Bioconductor packages. This integration of MultiExperiment Viewer and R is easily extensible to other R packages and provides users with point and click access to traditionally command line driven tools written in R. We demonstrate the ability to use MultiExperiment Viewer as a graphical user interface for Bioconductor applications in microarray data analysis by incorporating three Bioconductor packages, RAMA, BRIDGE and iterativeBMA. PMID:18652698

ArrayWiki: an enabling technology for sharing public microarray data repositories and meta-analyses

PubMed Central

Stokes, Todd H; Torrance, JT; Li, Henry; Wang, May D

2008-01-01

Background A survey of microarray databases reveals that most of the repository contents and data models are heterogeneous (i.e., data obtained from different chip manufacturers), and that the repositories provide only basic biological keywords linking to PubMed. As a result, it is difficult to find datasets using research context or analysis parameters information beyond a few keywords. For example, to reduce the "curse-of-dimension" problem in microarray analysis, the number of samples is often increased by merging array data from different datasets. Knowing chip data parameters such as pre-processing steps (e.g., normalization, artefact removal, etc), and knowing any previous biological validation of the dataset is essential due to the heterogeneity of the data. However, most of the microarray repositories do not have meta-data information in the first place, and do not have a a mechanism to add or insert this information. Thus, there is a critical need to create "intelligent" microarray repositories that (1) enable update of meta-data with the raw array data, and (2) provide standardized archiving protocols to minimize bias from the raw data sources. Results To address the problems discussed, we have developed a community maintained system called ArrayWiki that unites disparate meta-data of microarray meta-experiments from multiple primary sources with four key features. First, ArrayWiki provides a user-friendly knowledge management interface in addition to a programmable interface using standards developed by Wikipedia. Second, ArrayWiki includes automated quality control processes (caCORRECT) and novel visualization methods (BioPNG, Gel Plots), which provide extra information about data quality unavailable in other microarray repositories. Third, it provides a user-curation capability through the familiar Wiki interface. Fourth, ArrayWiki provides users with simple text-based searches across all experiment meta-data, and exposes data to search engine crawlers (Semantic Agents) such as Google to further enhance data discovery. Conclusions Microarray data and meta information in ArrayWiki are distributed and visualized using a novel and compact data storage format, BioPNG. Also, they are open to the research community for curation, modification, and contribution. By making a small investment of time to learn the syntax and structure common to all sites running MediaWiki software, domain scientists and practioners can all contribute to make better use of microarray technologies in research and medical practices. ArrayWiki is available at . PMID:18541053

A prototype for unsupervised analysis of tissue microarrays for cancer research and diagnostics.

PubMed

Chen, Wenjin; Reiss, Michael; Foran, David J

2004-06-01

The tissue microarray (TMA) technique enables researchers to extract small cylinders of tissue from histological sections and arrange them in a matrix configuration on a recipient paraffin block such that hundreds can be analyzed simultaneously. TMA offers several advantages over traditional specimen preparation by maximizing limited tissue resources and providing a highly efficient means for visualizing molecular targets. By enabling researchers to reliably determine the protein expression profile for specific types of cancer, it may be possible to elucidate the mechanism by which healthy tissues are transformed into malignancies. Currently, the primary methods used to evaluate arrays involve the interactive review of TMA samples while they are viewed under a microscope, subjectively evaluated, and scored by a technician. This process is extremely slow, tedious, and prone to error. In order to facilitate large-scale, multi-institutional studies, a more automated and reliable means for analyzing TMAs is needed. We report here a web-based prototype which features automated imaging, registration, and distributed archiving of TMAs in multiuser network environments. The system utilizes a principal color decomposition approach to identify and characterize the predominant staining signatures of specimens in color space. This strategy was shown to be reliable for detecting and quantifying the immunohistochemical expression levels for TMAs.

Controllable Photovoltaic Effect of Microarray Derived from Epitaxial Tetragonal BiFeO3 Films.

PubMed

Lu, Zengxing; Li, Peilian; Wan, Jian-Guo; Huang, Zhifeng; Tian, Guo; Pan, Danfeng; Fan, Zhen; Gao, Xingsen; Liu, Jun-Ming

2017-08-16

Recently, the ferroelectric photovoltaic (FePV) effect has attracted great interest due to its potential in developing optoelectronic devices such as solar cell and electric-optical sensors. It is important for actual applications to realize a controllable photovoltaic process in ferroelectric-based materials. In this work, we prepared well-ordered microarrays based on epitaxially tetragonal BiFeO 3 (T-BFO) films by the pulsed laser deposition technique. The polarization-dependent photocurrent image was directly observed by a conductive atomic force microscope under ultraviolet illumination. By choosing a suitable buffer electrode layer and controlling the ferroelectric polarization in the T-BFO layer, we realized the manipulation of the photovoltaic process. Moreover, based on the analysis of the band structure, we revealed the mechanism of manipulating the photovoltaic process and attributed it to the competition between two key factors, i.e., the internal electric field caused by energy band alignments at interfaces and the depolarization field induced by the ferroelectric polarization in T-BFO. This work is very meaningful for deeply understanding the photovoltaic process of BiFeO 3 -based devices at the microscale and provides us a feasible avenue for developing data storage or logic switching microdevices based on the FePV effect.

Image-guided genomic analysis of tissue response to laser-induced thermal stress

NASA Astrophysics Data System (ADS)

Mackanos, Mark A.; Helms, Mike; Kalish, Flora; Contag, Christopher H.

2011-05-01

The cytoprotective response to thermal injury is characterized by transcriptional activation of ``heat shock proteins'' (hsp) and proinflammatory proteins. Expression of these proteins may predict cellular survival. Microarray analyses were performed to identify spatially distinct gene expression patterns responding to thermal injury. Laser injury zones were identified by expression of a transgene reporter comprised of the 70 kD hsp gene and the firefly luciferase coding sequence. Zones included the laser spot, the surrounding region where hsp70-luc expression was increased, and a region adjacent to the surrounding region. A total of 145 genes were up-regulated in the laser irradiated region, while 69 were up-regulated in the adjacent region. At 7 hours the chemokine Cxcl3 was the highest expressed gene in the laser spot (24 fold) and adjacent region (32 fold). Chemokines were the most common up-regulated genes identified. Microarray gene expression was successfully validated using qRT- polymerase chain reaction for selected genes of interest. The early response genes are likely involved in cytoprotection and initiation of the healing response. Their regulatory elements will benefit creating the next generation reporter mice and controlling expression of therapeutic proteins. The identified genes serve as drug development targets that may prevent acute tissue damage and accelerate healing.

Digital microarray analysis for digital artifact genomics

NASA Astrophysics Data System (ADS)

Jaenisch, Holger; Handley, James; Williams, Deborah

2013-06-01

We implement a Spatial Voting (SV) based analogy of microarray analysis for digital gene marker identification in malware code sections. We examine a famous set of malware formally analyzed by Mandiant and code named Advanced Persistent Threat (APT1). APT1 is a Chinese organization formed with specific intent to infiltrate and exploit US resources. Manidant provided a detailed behavior and sting analysis report for the 288 malware samples available. We performed an independent analysis using a new alternative to the traditional dynamic analysis and static analysis we call Spatial Analysis (SA). We perform unsupervised SA on the APT1 originating malware code sections and report our findings. We also show the results of SA performed on some members of the families associated by Manidant. We conclude that SV based SA is a practical fast alternative to dynamics analysis and static analysis.

Hybrid genetic algorithm-neural network: feature extraction for unpreprocessed microarray data.

PubMed

Tong, Dong Ling; Schierz, Amanda C

2011-09-01

Suitable techniques for microarray analysis have been widely researched, particularly for the study of marker genes expressed to a specific type of cancer. Most of the machine learning methods that have been applied to significant gene selection focus on the classification ability rather than the selection ability of the method. These methods also require the microarray data to be preprocessed before analysis takes place. The objective of this study is to develop a hybrid genetic algorithm-neural network (GANN) model that emphasises feature selection and can operate on unpreprocessed microarray data. The GANN is a hybrid model where the fitness value of the genetic algorithm (GA) is based upon the number of samples correctly labelled by a standard feedforward artificial neural network (ANN). The model is evaluated by using two benchmark microarray datasets with different array platforms and differing number of classes (a 2-class oligonucleotide microarray data for acute leukaemia and a 4-class complementary DNA (cDNA) microarray dataset for SRBCTs (small round blue cell tumours)). The underlying concept of the GANN algorithm is to select highly informative genes by co-evolving both the GA fitness function and the ANN weights at the same time. The novel GANN selected approximately 50% of the same genes as the original studies. This may indicate that these common genes are more biologically significant than other genes in the datasets. The remaining 50% of the significant genes identified were used to build predictive models and for both datasets, the models based on the set of genes extracted by the GANN method produced more accurate results. The results also suggest that the GANN method not only can detect genes that are exclusively associated with a single cancer type but can also explore the genes that are differentially expressed in multiple cancer types. The results show that the GANN model has successfully extracted statistically significant genes from the unpreprocessed microarray data as well as extracting known biologically significant genes. We also show that assessing the biological significance of genes based on classification accuracy may be misleading and though the GANN's set of extra genes prove to be more statistically significant than those selected by other methods, a biological assessment of these genes is highly recommended to confirm their functionality. Copyright © 2011 Elsevier B.V. All rights reserved.

arrayCGHbase: an analysis platform for comparative genomic hybridization microarrays

PubMed Central

Menten, Björn; Pattyn, Filip; De Preter, Katleen; Robbrecht, Piet; Michels, Evi; Buysse, Karen; Mortier, Geert; De Paepe, Anne; van Vooren, Steven; Vermeesch, Joris; Moreau, Yves; De Moor, Bart; Vermeulen, Stefan; Speleman, Frank; Vandesompele, Jo

2005-01-01

Background The availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of several platforms for analysis of DNA copy number changes, amongst others microarray comparative genomic hybridization (arrayCGH). One of the challenges inherent to this new technology is the management and analysis of large numbers of data points generated in each individual experiment. Results We have developed arrayCGHbase, a comprehensive analysis platform for arrayCGH experiments consisting of a MIAME (Minimal Information About a Microarray Experiment) supportive database using MySQL underlying a data mining web tool, to store, analyze, interpret, compare, and visualize arrayCGH results in a uniform and user-friendly format. Following its flexible design, arrayCGHbase is compatible with all existing and forthcoming arrayCGH platforms. Data can be exported in a multitude of formats, including BED files to map copy number information on the genome using the Ensembl or UCSC genome browser. Conclusion ArrayCGHbase is a web based and platform independent arrayCGH data analysis tool, that allows users to access the analysis suite through the internet or a local intranet after installation on a private server. ArrayCGHbase is available at . PMID:15910681

Factorial microarray analysis of zebra mussel (Dreissena polymorpha: Dreissenidae, Bivalvia) adhesion

PubMed Central

2010-01-01

Background The zebra mussel (Dreissena polymorpha) has been well known for its expertise in attaching to substances under the water. Studies in past decades on this underwater adhesion focused on the adhesive protein isolated from the byssogenesis apparatus of the zebra mussel. However, the mechanism of the initiation, maintenance, and determination of the attachment process remains largely unknown. Results In this study, we used a zebra mussel cDNA microarray previously developed in our lab and a factorial analysis to identify the genes that were involved in response to the changes of four factors: temperature (Factor A), current velocity (Factor B), dissolved oxygen (Factor C), and byssogenesis status (Factor D). Twenty probes in the microarray were found to be modified by one of the factors. The transcription products of four selected genes, DPFP-BG20_A01, EGP-BG97/192_B06, EGP-BG13_G05, and NH-BG17_C09 were unique to the zebra mussel foot based on the results of quantitative reverse transcription PCR (qRT-PCR). The expression profiles of these four genes under the attachment and non-attachment were also confirmed by qRT-PCR and the result is accordant to that from microarray assay. The in situ hybridization with the RNA probes of two identified genes DPFP-BG20_A01 and EGP-BG97/192_B06 indicated that both of them were expressed by a type of exocrine gland cell located in the middle part of the zebra mussel foot. Conclusions The results of this study suggested that the changes of D. polymorpha byssogenesis status and the environmental factors can dramatically affect the expression profiles of the genes unique to the foot. It turns out that the factorial design and analysis of the microarray experiment is a reliable method to identify the influence of multiple factors on the expression profiles of the probesets in the microarray; therein it provides a powerful tool to reveal the mechanism of zebra mussel underwater attachment. PMID:20509938

The FLIGHT Drosophila RNAi database

PubMed Central

Bursteinas, Borisas; Jain, Ekta; Gao, Qiong; Baum, Buzz; Zvelebil, Marketa

2010-01-01

FLIGHT (http://flight.icr.ac.uk/) is an online resource compiling data from high-throughput Drosophila in vivo and in vitro RNAi screens. FLIGHT includes details of RNAi reagents and their predicted off-target effects, alongside RNAi screen hits, scores and phenotypes, including images from high-content screens. The latest release of FLIGHT is designed to enable users to upload, analyze, integrate and share their own RNAi screens. Users can perform multiple normalizations, view quality control plots, detect and assign screen hits and compare hits from multiple screens using a variety of methods including hierarchical clustering. FLIGHT integrates RNAi screen data with microarray gene expression as well as genomic annotations and genetic/physical interaction datasets to provide a single interface for RNAi screen analysis and datamining in Drosophila. PMID:20855970

Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system

PubMed Central

Sunkin, Susan M.; Ng, Lydia; Lau, Chris; Dolbeare, Tim; Gilbert, Terri L.; Thompson, Carol L.; Hawrylycz, Michael; Dang, Chinh

2013-01-01

The Allen Brain Atlas (http://www.brain-map.org) provides a unique online public resource integrating extensive gene expression data, connectivity data and neuroanatomical information with powerful search and viewing tools for the adult and developing brain in mouse, human and non-human primate. Here, we review the resources available at the Allen Brain Atlas, describing each product and data type [such as in situ hybridization (ISH) and supporting histology, microarray, RNA sequencing, reference atlases, projection mapping and magnetic resonance imaging]. In addition, standardized and unique features in the web applications are described that enable users to search and mine the various data sets. Features include both simple and sophisticated methods for gene searches, colorimetric and fluorescent ISH image viewers, graphical displays of ISH, microarray and RNA sequencing data, Brain Explorer software for 3D navigation of anatomy and gene expression, and an interactive reference atlas viewer. In addition, cross data set searches enable users to query multiple Allen Brain Atlas data sets simultaneously. All of the Allen Brain Atlas resources can be accessed through the Allen Brain Atlas data portal. PMID:23193282

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

PubMed Central

Galfalvy, Hanga C; Erraji-Benchekroun, Loubna; Smyrniotopoulos, Peggy; Pavlidis, Paul; Ellis, Steven P; Mann, J John; Sibille, Etienne; Arango, Victoria

2003-01-01

Background Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods. Results Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression. Conclusion In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects. PMID:12962547

Sex genes for genomic analysis in human brain: internal controls for comparison of probe level data extraction.

PubMed

Galfalvy, Hanga C; Erraji-Benchekroun, Loubna; Smyrniotopoulos, Peggy; Pavlidis, Paul; Ellis, Steven P; Mann, J John; Sibille, Etienne; Arango, Victoria

2003-09-08

Genomic studies of complex tissues pose unique analytical challenges for assessment of data quality, performance of statistical methods used for data extraction, and detection of differentially expressed genes. Ideally, to assess the accuracy of gene expression analysis methods, one needs a set of genes which are known to be differentially expressed in the samples and which can be used as a "gold standard". We introduce the idea of using sex-chromosome genes as an alternative to spiked-in control genes or simulations for assessment of microarray data and analysis methods. Expression of sex-chromosome genes were used as true internal biological controls to compare alternate probe-level data extraction algorithms (Microarray Suite 5.0 [MAS5.0], Model Based Expression Index [MBEI] and Robust Multi-array Average [RMA]), to assess microarray data quality and to establish some statistical guidelines for analyzing large-scale gene expression. These approaches were implemented on a large new dataset of human brain samples. RMA-generated gene expression values were markedly less variable and more reliable than MAS5.0 and MBEI-derived values. A statistical technique controlling the false discovery rate was applied to adjust for multiple testing, as an alternative to the Bonferroni method, and showed no evidence of false negative results. Fourteen probesets, representing nine Y- and two X-chromosome linked genes, displayed significant sex differences in brain prefrontal cortex gene expression. In this study, we have demonstrated the use of sex genes as true biological internal controls for genomic analysis of complex tissues, and suggested analytical guidelines for testing alternate oligonucleotide microarray data extraction protocols and for adjusting multiple statistical analysis of differentially expressed genes. Our results also provided evidence for sex differences in gene expression in the brain prefrontal cortex, supporting the notion of a putative direct role of sex-chromosome genes in differentiation and maintenance of sexual dimorphism of the central nervous system. Importantly, these analytical approaches are applicable to all microarray studies that include male and female human or animal subjects.

Transcriptional Changes in Canine Distemper Virus-Induced Demyelinating Leukoencephalitis Favor a Biphasic Mode of Demyelination

PubMed Central

Ulrich, Reiner; Puff, Christina; Wewetzer, Konstantin; Kalkuhl, Arno; Deschl, Ulrich; Baumgärtner, Wolfgang

2014-01-01

Canine distemper virus (CDV)-induced demyelinating leukoencephalitis in dogs (Canis familiaris) is suggested to represent a naturally occurring translational model for subacute sclerosing panencephalitis and multiple sclerosis in humans. The aim of this study was a hypothesis-free microarray analysis of the transcriptional changes within cerebellar specimens of five cases of acute, six cases of subacute demyelinating, and three cases of chronic demyelinating and inflammatory CDV leukoencephalitis as compared to twelve non-infected control dogs. Frozen cerebellar specimens were used for analysis of histopathological changes including demyelination, transcriptional changes employing microarrays, and presence of CDV nucleoprotein RNA and protein using microarrays, RT-qPCR and immunohistochemistry. Microarray analysis revealed 780 differentially expressed probe sets. The dominating change was an up-regulation of genes related to the innate and the humoral immune response, and less distinct the cytotoxic T-cell-mediated immune response in all subtypes of CDV leukoencephalitis as compared to controls. Multiple myelin genes including myelin basic protein and proteolipid protein displayed a selective down-regulation in subacute CDV leukoencephalitis, suggestive of an oligodendrocyte dystrophy. In contrast, a marked up-regulation of multiple immunoglobulin-like expressed sequence tags and the delta polypeptide of the CD3 antigen was observed in chronic CDV leukoencephalitis, in agreement with the hypothesis of an immune-mediated demyelination in the late inflammatory phase of the disease. Analysis of pathways intimately linked to demyelination as determined by morphometry employing correlation-based Gene Set Enrichment Analysis highlighted the pathomechanistic importance of up-regulated genes comprised by the gene ontology terms “viral replication” and “humoral immune response” as well as down-regulated genes functionally related to “metabolite and energy generation”. PMID:24755553

Transcriptional changes in canine distemper virus-induced demyelinating leukoencephalitis favor a biphasic mode of demyelination.

PubMed

Ulrich, Reiner; Puff, Christina; Wewetzer, Konstantin; Kalkuhl, Arno; Deschl, Ulrich; Baumgärtner, Wolfgang

2014-01-01

Canine distemper virus (CDV)-induced demyelinating leukoencephalitis in dogs (Canis familiaris) is suggested to represent a naturally occurring translational model for subacute sclerosing panencephalitis and multiple sclerosis in humans. The aim of this study was a hypothesis-free microarray analysis of the transcriptional changes within cerebellar specimens of five cases of acute, six cases of subacute demyelinating, and three cases of chronic demyelinating and inflammatory CDV leukoencephalitis as compared to twelve non-infected control dogs. Frozen cerebellar specimens were used for analysis of histopathological changes including demyelination, transcriptional changes employing microarrays, and presence of CDV nucleoprotein RNA and protein using microarrays, RT-qPCR and immunohistochemistry. Microarray analysis revealed 780 differentially expressed probe sets. The dominating change was an up-regulation of genes related to the innate and the humoral immune response, and less distinct the cytotoxic T-cell-mediated immune response in all subtypes of CDV leukoencephalitis as compared to controls. Multiple myelin genes including myelin basic protein and proteolipid protein displayed a selective down-regulation in subacute CDV leukoencephalitis, suggestive of an oligodendrocyte dystrophy. In contrast, a marked up-regulation of multiple immunoglobulin-like expressed sequence tags and the delta polypeptide of the CD3 antigen was observed in chronic CDV leukoencephalitis, in agreement with the hypothesis of an immune-mediated demyelination in the late inflammatory phase of the disease. Analysis of pathways intimately linked to demyelination as determined by morphometry employing correlation-based Gene Set Enrichment Analysis highlighted the pathomechanistic importance of up-regulated genes comprised by the gene ontology terms "viral replication" and "humoral immune response" as well as down-regulated genes functionally related to "metabolite and energy generation".

Microarray analysis of gene expression patterns in the leaf during potato tuberization in the potato somatic hybrid Solanum tuberosum and Solanum etuberosum.

PubMed

Tiwari, Jagesh Kumar; Devi, Sapna; Sundaresha, S; Chandel, Poonam; Ali, Nilofer; Singh, Brajesh; Bhardwaj, Vinay; Singh, Bir Pal

2015-06-01

Genes involved in photoassimilate partitioning and changes in hormonal balance are important for potato tuberization. In the present study, we investigated gene expression patterns in the tuber-bearing potato somatic hybrid (E1-3) and control non-tuberous wild species Solanum etuberosum (Etb) by microarray. Plants were grown under controlled conditions and leaves were collected at eight tuber developmental stages for microarray analysis. A t-test analysis identified a total of 468 genes (94 up-regulated and 374 down-regulated) that were statistically significant (p ≤ 0.05) and differentially expressed in E1-3 and Etb. Gene Ontology (GO) characterization of the 468 genes revealed that 145 were annotated and 323 were of unknown function. Further, these 145 genes were grouped based on GO biological processes followed by molecular function and (or) PGSC description into 15 gene sets, namely (1) transport, (2) metabolic process, (3) biological process, (4) photosynthesis, (5) oxidation-reduction, (6) transcription, (7) translation, (8) binding, (9) protein phosphorylation, (10) protein folding, (11) ubiquitin-dependent protein catabolic process, (12) RNA processing, (13) negative regulation of protein, (14) methylation, and (15) mitosis. RT-PCR analysis of 10 selected highly significant genes (p ≤ 0.01) confirmed the microarray results. Overall, we show that candidate genes induced in leaves of E1-3 were implicated in tuberization processes such as transport, carbohydrate metabolism, phytohormones, and transcription/translation/binding functions. Hence, our results provide an insight into the candidate genes induced in leaf tissues during tuberization in E1-3.

Informatic selection of a neural crest-melanocyte cDNA set for microarray analysis

PubMed Central

Loftus, S. K.; Chen, Y.; Gooden, G.; Ryan, J. F.; Birznieks, G.; Hilliard, M.; Baxevanis, A. D.; Bittner, M.; Meltzer, P.; Trent, J.; Pavan, W.

1999-01-01

With cDNA microarrays, it is now possible to compare the expression of many genes simultaneously. To maximize the likelihood of finding genes whose expression is altered under the experimental conditions, it would be advantageous to be able to select clones for tissue-appropriate cDNA sets. We have taken advantage of the extensive sequence information in the dbEST expressed sequence tag (EST) database to identify a neural crest-derived melanocyte cDNA set for microarray analysis. Analysis of characterized genes with dbEST identified one library that contained ESTs representing 21 neural crest-expressed genes (library 198). The distribution of the ESTs corresponding to these genes was biased toward being derived from library 198. This is in contrast to the EST distribution profile for a set of control genes, characterized to be more ubiquitously expressed in multiple tissues (P < 1 × 10−9). From library 198, a subset of 852 clustered ESTs were selected that have a library distribution profile similar to that of the 21 neural crest-expressed genes. Microarray analysis demonstrated the majority of the neural crest-selected 852 ESTs (Mel1 array) were differentially expressed in melanoma cell lines compared with a non-neural crest kidney epithelial cell line (P < 1 × 10−8). This was not observed with an array of 1,238 ESTs that was selected without library origin bias (P = 0.204). This study presents an approach for selecting tissue-appropriate cDNAs that can be used to examine the expression profiles of developmental processes and diseases. PMID:10430933

Separate-channel analysis of two-channel microarrays: recovering inter-spot information.

PubMed

Smyth, Gordon K; Altman, Naomi S

2013-05-26

Two-channel (or two-color) microarrays are cost-effective platforms for comparative analysis of gene expression. They are traditionally analysed in terms of the log-ratios (M-values) of the two channel intensities at each spot, but this analysis does not use all the information available in the separate channel observations. Mixed models have been proposed to analyse intensities from the two channels as separate observations, but such models can be complex to use and the gain in efficiency over the log-ratio analysis is difficult to quantify. Mixed models yield test statistics for the null distributions can be specified only approximately, and some approaches do not borrow strength between genes. This article reformulates the mixed model to clarify the relationship with the traditional log-ratio analysis, to facilitate information borrowing between genes, and to obtain an exact distributional theory for the resulting test statistics. The mixed model is transformed to operate on the M-values and A-values (average log-expression for each spot) instead of on the log-expression values. The log-ratio analysis is shown to ignore information contained in the A-values. The relative efficiency of the log-ratio analysis is shown to depend on the size of the intraspot correlation. A new separate channel analysis method is proposed that assumes a constant intra-spot correlation coefficient across all genes. This approach permits the mixed model to be transformed into an ordinary linear model, allowing the data analysis to use a well-understood empirical Bayes analysis pipeline for linear modeling of microarray data. This yields statistically powerful test statistics that have an exact distributional theory. The log-ratio, mixed model and common correlation methods are compared using three case studies. The results show that separate channel analyses that borrow strength between genes are more powerful than log-ratio analyses. The common correlation analysis is the most powerful of all. The common correlation method proposed in this article for separate-channel analysis of two-channel microarray data is no more difficult to apply in practice than the traditional log-ratio analysis. It provides an intuitive and powerful means to conduct analyses and make comparisons that might otherwise not be possible.

Identification of new autoantigens for primary biliary cirrhosis using human proteome microarrays.

PubMed

Hu, Chao-Jun; Song, Guang; Huang, Wei; Liu, Guo-Zhen; Deng, Chui-Wen; Zeng, Hai-Pan; Wang, Li; Zhang, Feng-Chun; Zhang, Xuan; Jeong, Jun Seop; Blackshaw, Seth; Jiang, Li-Zhi; Zhu, Heng; Wu, Lin; Li, Yong-Zhe

2012-09-01

Primary biliary cirrhosis (PBC) is a chronic cholestatic liver disease of unknown etiology and is considered to be an autoimmune disease. Autoantibodies are important tools for accurate diagnosis of PBC. Here, we employed serum profiling analysis using a human proteome microarray composed of about 17,000 full-length unique proteins and identified 23 proteins that correlated with PBC. To validate these results, we fabricated a PBC-focused microarray with 21 of these newly identified candidates and nine additional known PBC antigens. By screening the PBC microarrays with additional cohorts of 191 PBC patients and 321 controls (43 autoimmune hepatitis, 55 hepatitis B virus, 31 hepatitis C virus, 48 rheumatoid arthritis, 45 systematic lupus erythematosus, 49 systemic sclerosis, and 50 healthy), six proteins were confirmed as novel PBC autoantigens with high sensitivities and specificities, including hexokinase-1 (isoforms I and II), Kelch-like protein 7, Kelch-like protein 12, zinc finger and BTB domain-containing protein 2, and eukaryotic translation initiation factor 2C, subunit 1. To facilitate clinical diagnosis, we developed ELISA for Kelch-like protein 12 and zinc finger and BTB domain-containing protein 2 and tested large cohorts (297 PBC and 637 control sera) to confirm the sensitivities and specificities observed in the microarray-based assays. In conclusion, our research showed that a strategy using high content protein microarray combined with a smaller but more focused protein microarray can effectively identify and validate novel PBC-specific autoantigens and has the capacity to be translated to clinical diagnosis by means of an ELISA-based method.

Ultrafiltration and Microarray for Detection of Microbial Source Tracking Marker and Pathogen Genes in Riverine and Marine Systems

PubMed Central

Li, Xiang; Harwood, Valerie J.; Nayak, Bina

2016-01-01

Pathogen identification and microbial source tracking (MST) to identify sources of fecal pollution improve evaluation of water quality. They contribute to improved assessment of human health risks and remediation of pollution sources. An MST microarray was used to simultaneously detect genes for multiple pathogens and indicators of fecal pollution in freshwater, marine water, sewage-contaminated freshwater and marine water, and treated wastewater. Dead-end ultrafiltration (DEUF) was used to concentrate organisms from water samples, yielding a recovery efficiency of >95% for Escherichia coli and human polyomavirus. Whole-genome amplification (WGA) increased gene copies from ultrafiltered samples and increased the sensitivity of the microarray. Viruses (adenovirus, bocavirus, hepatitis A virus, and human polyomaviruses) were detected in sewage-contaminated samples. Pathogens such as Legionella pneumophila, Shigella flexneri, and Campylobacter fetus were detected along with genes conferring resistance to aminoglycosides, beta-lactams, and tetracycline. Nonmetric dimensional analysis of MST marker genes grouped sewage-spiked freshwater and marine samples with sewage and apart from other fecal sources. The sensitivity (percent true positives) of the microarray probes for gene targets anticipated in sewage was 51 to 57% and was lower than the specificity (percent true negatives; 79 to 81%). A linear relationship between gene copies determined by quantitative PCR and microarray fluorescence was found, indicating the semiquantitative nature of the MST microarray. These results indicate that ultrafiltration coupled with WGA provides sufficient nucleic acids for detection of viruses, bacteria, protozoa, and antibiotic resistance genes by the microarray in applications ranging from beach monitoring to risk assessment. PMID:26729716

An international standardization programme towards the application of gene expression profiling in routine leukaemia diagnostics: the Microarray Innovations in LEukemia study prephase

PubMed Central

Kohlmann, Alexander; Kipps, Thomas J; Rassenti, Laura Z; Downing, James R; Shurtleff, Sheila A; Mills, Ken I; Gilkes, Amanda F; Hofmann, Wolf-Karsten; Basso, Giuseppe; Dell’Orto, Marta Campo; Foà, Robin; Chiaretti, Sabina; De Vos, John; Rauhut, Sonja; Papenhausen, Peter R; Hernández, Jesus M; Lumbreras, Eva; Yeoh, Allen E; Koay, Evelyn S; Li, Rachel; Liu, Wei-min; Williams, Paul M; Wieczorek, Lothar; Haferlach, Torsten

2008-01-01

Gene expression profiling has the potential to enhance current methods for the diagnosis of haematological malignancies. Here, we present data on 204 analyses from an international standardization programme that was conducted in 11 laboratories as a prephase to the Microarray Innovations in LEukemia (MILE) study. Each laboratory prepared two cell line samples, together with three replicate leukaemia patient lysates in two distinct stages: (i) a 5-d course of protocol training, and (ii) independent proficiency testing. Unsupervised, supervised, and r2 correlation analyses demonstrated that microarray analysis can be performed with remarkably high intra-laboratory reproducibility and with comparable quality and reliability. PMID:18573112

Mining Microarray Data at NCBI’s Gene Expression Omnibus (GEO)*

PubMed Central

Barrett, Tanya; Edgar, Ron

2006-01-01

Summary The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo. PMID:16888359

Integrative missing value estimation for microarray data.

PubMed

Hu, Jianjun; Li, Haifeng; Waterman, Michael S; Zhou, Xianghong Jasmine

2006-10-12

Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples. We present the integrative Missing Value Estimation method (iMISS) by incorporating information from multiple reference microarray datasets to improve missing value estimation. For each gene with missing data, we derive a consistent neighbor-gene list by taking reference data sets into consideration. To determine whether the given reference data sets are sufficiently informative for integration, we use a submatrix imputation approach. Our experiments showed that iMISS can significantly and consistently improve the accuracy of the state-of-the-art Local Least Square (LLS) imputation algorithm by up to 15% improvement in our benchmark tests. We demonstrated that the order-statistics-based integrative imputation algorithms can achieve significant improvements over the state-of-the-art missing value estimation approaches such as LLS and is especially good for imputing microarray datasets with a limited number of samples, high rates of missing data, or very noisy measurements. With the rapid accumulation of microarray datasets, the performance of our approach can be further improved by incorporating larger and more appropriate reference datasets.

A proposed metric for assessing the measurement quality of individual microarrays

PubMed Central

Kim, Kyoungmi; Page, Grier P; Beasley, T Mark; Barnes, Stephen; Scheirer, Katherine E; Allison, David B

2006-01-01

Background High-density microarray technology is increasingly applied to study gene expression levels on a large scale. Microarray experiments rely on several critical steps that may introduce error and uncertainty in analyses. These steps include mRNA sample extraction, amplification and labeling, hybridization, and scanning. In some cases this may be manifested as systematic spatial variation on the surface of microarray in which expression measurements within an individual array may vary as a function of geographic position on the array surface. Results We hypothesized that an index of the degree of spatiality of gene expression measurements associated with their physical geographic locations on an array could indicate the summary of the physical reliability of the microarray. We introduced a novel way to formulate this index using a statistical analysis tool. Our approach regressed gene expression intensity measurements on a polynomial response surface of the microarray's Cartesian coordinates. We demonstrated this method using a fixed model and presented results from real and simulated datasets. Conclusion We demonstrated the potential of such a quantitative metric for assessing the reliability of individual arrays. Moreover, we showed that this procedure can be incorporated into laboratory practice as a means to set quality control specifications and as a tool to determine whether an array has sufficient quality to be retained in terms of spatial correlation of gene expression measurements. PMID:16430768

Variation of gene expression in Bacillus subtilis samples of fermentation replicates.

PubMed

Zhou, Ying; Yu, Wen-Bang; Ye, Bang-Ce

2011-06-01

The application of comprehensive gene expression profiling technologies to compare wild and mutated microorganism samples or to assess molecular differences between various treatments has been widely used. However, little is known about the normal variation of gene expression in microorganisms. In this study, an Agilent customized microarray representing 4,106 genes was used to quantify transcript levels of five-repeated flasks to assess normal variation in Bacillus subtilis gene expression. CV analysis and analysis of variance were employed to investigate the normal variance of genes and the components of variance, respectively. The results showed that above 80% of the total variation was caused by biological variance. For the 12 replicates, 451 of 4,106 genes exhibited variance with CV values over 10%. The functional category enrichment analysis demonstrated that these variable genes were mainly involved in cell type differentiation, cell type localization, cell cycle and DNA processing, and spore or cyst coat. Using power analysis, the minimal biological replicate number for a B. subtilis microarray experiment was determined to be six. The results contribute to the definition of the baseline level of variability in B. subtilis gene expression and emphasize the importance of replicate microarray experiments.

Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages.

PubMed

Taminau, Jonatan; Meganck, Stijn; Lazar, Cosmin; Steenhoff, David; Coletta, Alain; Molter, Colin; Duque, Robin; de Schaetzen, Virginie; Weiss Solís, David Y; Bersini, Hugues; Nowé, Ann

2012-12-24

With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck. We present the newly released inSilicoMerging R/Bioconductor package which, together with the earlier released inSilicoDb R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the inSilicoMerging package a set of five visual and six quantitative validation measures are available as well. By providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [https://insilicodb.org/app/].

Preliminary report for analysis of genome wide mutations from four ciprofloxacin resistant B. anthracis Sterne isolates generated by Illumina, 454 sequencing and microarrays for DHS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jaing, Crystal; Vergez, Lisa; Hinckley, Aubree

2011-06-21

The objective of this project is to provide DHS a comprehensive evaluation of the current genomic technologies including genotyping, Taqman PCR, multiple locus variable tandem repeat analysis (MLVA), microarray and high-throughput DNA sequencing in the analysis of biothreat agents from complex environmental samples. As the result of a different DHS project, we have selected for and isolated a large number of ciprofloxacin resistant B. anthracis Sterne isolates. These isolates vary in the concentrations of ciprofloxacin that they can tolerate, suggesting multiple mutations in the samples. In collaboration with University of Houston, Eureka Genomics and Oak Ridge National Laboratory, we analyzedmore » the ciprofloxacin resistant B. anthracis Sterne isolates by microarray hybridization, Illumina and Roche 454 sequencing to understand the error rates and sensitivity of the different methods. The report provides an assessment of the results and a complete set of all protocols used and all data generated along with information to interpret the protocols and data sets.« less

Characterization of regulatory pathways in Xylella fastidiosa: genes and phenotypes controlled by algU.

PubMed

Shi, Xiang Yang; Dumenyo, C Korsi; Hernandez-Martinez, Rufina; Azad, Hamid; Cooksey, Donald A

2007-11-01

Many virulence genes in plant bacterial pathogens are coordinately regulated by "global" regulatory genes. Conducting DNA microarray analysis of bacterial mutants of such genes, compared with the wild type, can help to refine the list of genes that may contribute to virulence in bacterial pathogens. The regulatory gene algU, with roles in stress response and regulation of the biosynthesis of the exopolysaccharide alginate in Pseudomonas aeruginosa and many other bacteria, has been extensively studied. The role of algU in Xylella fastidiosa, the cause of Pierce's disease of grapevines, was analyzed by mutation and whole-genome microarray analysis to define its involvement in aggregation, biofilm formation, and virulence. In this study, an algU::nptII mutant had reduced cell-cell aggregation, attachment, and biofilm formation and lower virulence in grapevines. Microarray analysis showed that 42 genes had significantly lower expression in the algU::nptII mutant than in the wild type. Among these are several genes that could contribute to cell aggregation and biofilm formation, as well as other physiological processes such as virulence, competition, and survival.

DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

NASA Astrophysics Data System (ADS)

Tchagang, Alain B.; Tewfik, Ahmed H.

2006-12-01

Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNA microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this study, we develop novel biclustering algorithms using basic linear algebra and arithmetic tools. The proposed biclustering algorithms can be used to search for all biclusters with constant values, biclusters with constant values on rows, biclusters with constant values on columns, and biclusters with coherent values from a set of data in a timely manner and without solving any optimization problem. We also show how one of the proposed biclustering algorithms can be adapted to identify biclusters with coherent evolution. The algorithms developed in this study discover all valid biclusters of each type, while almost all previous biclustering approaches will miss some.

Identification of rat lung-specific microRNAs by micoRNA microarray: valuable discoveries for the facilitation of lung research.

PubMed

Wang, Yang; Weng, Tingting; Gou, Deming; Chen, Zhongming; Chintagari, Narendranath Reddy; Liu, Lin

2007-01-24

An important mechanism for gene regulation utilizes small non-coding RNAs called microRNAs (miRNAs). These small RNAs play important roles in tissue development, cell differentiation and proliferation, lipid and fat metabolism, stem cells, exocytosis, diseases and cancers. To date, relatively little is known about functions of miRNAs in the lung except lung cancer. In this study, we utilized a rat miRNA microarray containing 216 miRNA probes, printed in-house, to detect the expression of miRNAs in the rat lung compared to the rat heart, brain, liver, kidney and spleen. Statistical analysis using Significant Analysis of Microarray (SAM) and Tukey Honestly Significant Difference (HSD) revealed 2 miRNAs (miR-195 and miR-200c) expressed specifically in the lung and 9 miRNAs co-expressed in the lung and another organ. 12 selected miRNAs were verified by Northern blot analysis. The identified lung-specific miRNAs from this work will facilitate functional studies of miRNAs during normal physiological and pathophysiological processes of the lung.

A Fast, Automatic Segmentation Algorithm for Locating and Delineating Touching Cell Boundaries in Imaged Histopathology

PubMed Central

Qi, Xin; Xing, Fuyong; Foran, David J.; Yang, Lin

2013-01-01

Summary Background Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms. Objectives In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging. Methods It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts' annotation and those generated using the automatic segmentation algorithm. Results The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation. Conclusion The proposed overlapping cell segmentation algorithm can accurately detect the center of each overlapping cell and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation. PMID:22526139

The Diagnostic Yield of Array Comparative Genomic Hybridization Is High Regardless of Severity of Intellectual Disability/Developmental Delay in Children.

PubMed

D'Arrigo, Stefano; Gavazzi, Francesco; Alfei, Enrico; Zuffardi, Orsetta; Montomoli, Cristina; Corso, Barbara; Buzzi, Erika; Sciacca, Francesca L; Bulgheroni, Sara; Riva, Daria; Pantaleoni, Chiara

2016-05-01

Microarray-based comparative genomic hybridization is a method of molecular analysis that identifies chromosomal anomalies (or copy number variants) that correlate with clinical phenotypes. The aim of the present study was to apply a clinical score previously designated by de Vries to 329 patients with intellectual disability/developmental disorder (intellectual disability/developmental delay) referred to our tertiary center and to see whether the clinical factors are associated with a positive outcome of aCGH analyses. Another goal was to test the association between a positive microarray-based comparative genomic hybridization result and the severity of intellectual disability/developmental delay. Microarray-based comparative genomic hybridization identified structural chromosomal alterations responsible for the intellectual disability/developmental delay phenotype in 16% of our sample. Our study showed that causative copy number variants are frequently found even in cases of mild intellectual disability (30.77%). We want to emphasize the need to conduct microarray-based comparative genomic hybridization on all individuals with intellectual disability/developmental delay, regardless of the severity, because the degree of intellectual disability/developmental delay does not predict the diagnostic yield of microarray-based comparative genomic hybridization. © The Author(s) 2015.

Genome Consortium for Active Teaching: Meeting the Goals of BIO2010

PubMed Central

Ledbetter, Mary Lee S.; Hoopes, Laura L.M.; Eckdahl, Todd T.; Heyer, Laurie J.; Rosenwald, Anne; Fowlks, Edison; Tonidandel, Scott; Bucholtz, Brooke; Gottfried, Gail

2007-01-01

The Genome Consortium for Active Teaching (GCAT) facilitates the use of modern genomics methods in undergraduate education. Initially focused on microarray technology, but with an eye toward diversification, GCAT is a community working to improve the education of tomorrow's life science professionals. GCAT participants have access to affordable microarrays, microarray scanners, free software for data analysis, and faculty workshops. Microarrays provided by GCAT have been used by 141 faculty on 134 campuses, including 21 faculty that serve large numbers of underrepresented minority students. An estimated 9480 undergraduates a year will have access to microarrays by 2009 as a direct result of GCAT faculty workshops. Gains for students include significantly improved comprehension of topics in functional genomics and increased interest in research. Faculty reported improved access to new technology and gains in understanding thanks to their involvement with GCAT. GCAT's network of supportive colleagues encourages faculty to explore genomics through student research and to learn a new and complex method with their undergraduates. GCAT is meeting important goals of BIO2010 by making research methods accessible to undergraduates, training faculty in genomics and bioinformatics, integrating mathematics into the biology curriculum, and increasing participation by underrepresented minority students. PMID:17548873

Genome Consortium for Active Teaching: meeting the goals of BIO2010.

PubMed

Campbell, A Malcolm; Ledbetter, Mary Lee S; Hoopes, Laura L M; Eckdahl, Todd T; Heyer, Laurie J; Rosenwald, Anne; Fowlks, Edison; Tonidandel, Scott; Bucholtz, Brooke; Gottfried, Gail

2007-01-01

The Genome Consortium for Active Teaching (GCAT) facilitates the use of modern genomics methods in undergraduate education. Initially focused on microarray technology, but with an eye toward diversification, GCAT is a community working to improve the education of tomorrow's life science professionals. GCAT participants have access to affordable microarrays, microarray scanners, free software for data analysis, and faculty workshops. Microarrays provided by GCAT have been used by 141 faculty on 134 campuses, including 21 faculty that serve large numbers of underrepresented minority students. An estimated 9480 undergraduates a year will have access to microarrays by 2009 as a direct result of GCAT faculty workshops. Gains for students include significantly improved comprehension of topics in functional genomics and increased interest in research. Faculty reported improved access to new technology and gains in understanding thanks to their involvement with GCAT. GCAT's network of supportive colleagues encourages faculty to explore genomics through student research and to learn a new and complex method with their undergraduates. GCAT is meeting important goals of BIO2010 by making research methods accessible to undergraduates, training faculty in genomics and bioinformatics, integrating mathematics into the biology curriculum, and increasing participation by underrepresented minority students.

BATS: a Bayesian user-friendly software for analyzing time series microarray experiments.

PubMed

Angelini, Claudia; Cutillo, Luisa; De Canditiis, Daniela; Mutarelli, Margherita; Pensky, Marianna

2008-10-06

Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest. From methodological and computational point of view, analyzing high-dimensional time course microarray data requires very specific set of tools which are usually not included in standard software packages. Recently, the authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a 'one-sample' time-course microarray experiment, to rank them and to estimate their expression profiles. The method is based on explicit expressions for calculations and, hence, very computationally efficient. The software package BATS (Bayesian Analysis of Time Series) presented here implements the methodology described above. It allows an user to automatically identify and rank differentially expressed genes and to estimate their expression profiles when at least 5-6 time points are available. The package has a user-friendly interface. BATS successfully manages various technical difficulties which arise in time-course microarray experiments, such as a small number of observations, non-uniform sampling intervals and replicated or missing data. BATS is a free user-friendly software for the analysis of both simulated and real microarray time course experiments. The software, the user manual and a brief illustrative example are freely available online at the BATS website: http://www.na.iac.cnr.it/bats.

Ischemic and Nephrotoxic Acute Renal Failure are Distinguished by their Broad Transcriptomic Responses (102/160 char)

PubMed Central

Yuen, Peter S.T.; Jo, Sang-Kyung; Holly, Mikaela K.; Hu, Xuzhen; Star, Robert A.

2006-01-01

Acute renal failure (ARF) has a high morbidity and mortality. In animal ARF models, effective treatments must be administered before or shortly after the insult, limiting their clinical potential. We used microarrays to identify early biomarkers that distinguish ischemic from nephrotoxic ARF, or biomarkers that detect both injury types. We compared rat kidney transcriptomes 2 and 8 hours after ischemia/reperfusion and after mercuric chloride. Quality control and statistical analyses were necessary to normalize microarrays from different lots, eliminate outliers, and exclude unaltered genes. Principal component analysis revealed distinct ischemic and nephrotoxic trajectories, and clear array groupings. Therefore, we used supervised analysis, t-tests and fold changes, to compile gene lists for each group, exclusive or non-exclusive, alone or in combination. There was little network connectivity, even in the largest group. Some microarray-identified genes were validated by TaqMan assay, ruling out artifacts. Western blotting confirmed that HO-1 and ATF3 proteins were upregulated; however, unexpectedly, their localization changed within the kidney. HO-1 staining shifted from cortical (early) to outer stripe of the outer medulla (late), primarily in detaching cells, after mercuric chloride, but not ischemia/reperfusion. ATF3 staining was similar, but with additional early transient expression in the outer stripe after ischemia/reperfusion. We conclude that microarray-identified genes must be evaluated not only for protein levels, but also for anatomical distribution among different zones, nephron segments, or cell types. Although protein detection reagents are limited, microarray data lay a rich foundation to explore biomarkers, therapeutics, and pathophysiology of ARF. PMID:16507785

Systematic Omics Analysis Review (SOAR) Tool to Support Risk Assessment

PubMed Central

McConnell, Emma R.; Bell, Shannon M.; Cote, Ila; Wang, Rong-Lin; Perkins, Edward J.; Garcia-Reyero, Natàlia; Gong, Ping; Burgoon, Lyle D.

2014-01-01

Environmental health risk assessors are challenged to understand and incorporate new data streams as the field of toxicology continues to adopt new molecular and systems biology technologies. Systematic screening reviews can help risk assessors and assessment teams determine which studies to consider for inclusion in a human health assessment. A tool for systematic reviews should be standardized and transparent in order to consistently determine which studies meet minimum quality criteria prior to performing in-depth analyses of the data. The Systematic Omics Analysis Review (SOAR) tool is focused on assisting risk assessment support teams in performing systematic reviews of transcriptomic studies. SOAR is a spreadsheet tool of 35 objective questions developed by domain experts, focused on transcriptomic microarray studies, and including four main topics: test system, test substance, experimental design, and microarray data. The tool will be used as a guide to identify studies that meet basic published quality criteria, such as those defined by the Minimum Information About a Microarray Experiment standard and the Toxicological Data Reliability Assessment Tool. Seven scientists were recruited to test the tool by using it to independently rate 15 published manuscripts that study chemical exposures with microarrays. Using their feedback, questions were weighted based on importance of the information and a suitability cutoff was set for each of the four topic sections. The final validation resulted in 100% agreement between the users on four separate manuscripts, showing that the SOAR tool may be used to facilitate the standardized and transparent screening of microarray literature for environmental human health risk assessment. PMID:25531884

Molecular biological identification of Babesia, Theileria, and Anaplasma species in cattle in Egypt using PCR assays, gene sequence analysis and a novel DNA microarray.

PubMed

El-Ashker, Maged; Hotzel, Helmut; Gwida, Mayada; El-Beskawy, Mohamed; Silaghi, Cornelia; Tomaso, Herbert

2015-01-30

In this preliminary study, a novel DNA microarray system was tested for the diagnosis of bovine piroplasmosis and anaplasmosis in comparison with microscopy and PCR assay results. In the Dakahlia Governorate, Egypt, 164 cattle were investigated for the presence of piroplasms and Anaplasma species. All investigated cattle were clinically examined. Blood samples were screened for the presence of blood parasites using microscopy and PCR assays. Seventy-one animals were acutely ill, whereas 93 were apparently healthy. In acutely ill cattle, Babesia/Theileria species (n=11) and Anaplasma marginale (n=10) were detected. Mixed infections with Babesia/Theileria spp. and A. marginale were present in two further cases. A. marginale infections were also detected in apparently healthy subjects (n=23). The results of PCR assays were confirmed by DNA sequencing. All samples that were positive by PCR for Babesia/Theileria spp. gave also positive results in the microarray analysis. The microarray chips identified Babesia bovis (n=12) and Babesia bigemina (n=2). Cattle with babesiosis were likely to have hemoglobinuria and nervous signs when compared to those with anaplasmosis that frequently had bloody feces. We conclude that clinical examination in combination with microscopy are still very useful in diagnosing acute cases of babesiosis and anaplasmosis, but a combination of molecular biological diagnostic assays will detect even asymptomatic carriers. In perspective, parallel detection of Babesia/Theileria spp. and A. marginale infections using a single microarray system will be a valuable improvement. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Microarray Meta-Analysis of RNA-Binding Protein Functions in Alternative Polyadenylation

PubMed Central

Hu, Wenchao; Liu, Yuting; Yan, Jun

2014-01-01

Alternative polyadenylation (APA) is a post-transcriptional mechanism to generate diverse mRNA transcripts with different 3′UTRs from the same gene. In this study, we systematically searched for the APA events with differential expression in public mouse microarray data. Hundreds of genes with over-represented differential APA events and the corresponding experiments were identified. We further revealed that global APA differential expression occurred prevalently in tissues such as brain comparing to peripheral tissues, and biological processes such as development, differentiation and immune responses. Interestingly, we also observed widespread differential APA events in RNA-binding protein (RBP) genes such as Rbm3, Eif4e2 and Elavl1. Given the fact that RBPs are considered as the main regulators of differential APA expression, we constructed a co-expression network between APAs and RBPs using the microarray data. Further incorporation of CLIP-seq data of selected RBPs showed that Nova2 represses and Mbnl1 promotes the polyadenylation of closest poly(A) sites respectively. Altogether, our study is the first microarray meta-analysis in a mammal on the regulation of APA by RBPs that integrated massive mRNA expression data under a wide-range of biological conditions. Finally, we present our results as a comprehensive resource in an online website for the research community. PMID:24622240

In vitro study of the effects of ELF electric fields on gene expression in human epidermal cells.

PubMed

Collard, Jean-Francois; Mertens, Benjamin; Hinsenkamp, Maurice

2011-01-01

An acceleration of differentiation, at the expense of proliferation, is observed after exposure of various biological models to low frequency and low amplitude electric and electromagnetic fields. Following these results showing significant modifications, we try to identify the biological mechanism involved at the cell level through microarray screening. For this study, we use epidermis cultures harvested from human abdominoplasty. Two platinum electrodes are used to apply the electric signal. The gene expressions of 38,500 well-characterized human genes are analyzed using Affymetrix(®) microarray U133 Plus 2.0 chips. The protocol is repeated on three different patients. After three periods of exposure, a total of 24 chips have been processed. After the application of ELF electric fields, the microarray analysis confirms a modification of the gene expression of epidermis cells. Particularly, four up-regulated genes (DKK1, TXNRD1, ATF3, and MME) and one down-regulated gene (MACF1) are involved in the regulation of proliferation and differentiation. Expression of these five genes was also confirmed by real-time rtPCR in all samples used for microarray analysis. These results corroborate an acceleration of cell differentiation at the expense of cell proliferation. © 2010 Wiley-Liss, Inc.

Searching for links in the biotic characteristics and abiotic parameters of nine different biogas plants

PubMed Central

Walter, Andreas; Knapp, Brigitte A.; Farbmacher, Theresa; Ebner, Christian; Insam, Heribert; Franke‐Whittle, Ingrid H.

2012-01-01

Summary To find links between the biotic characteristics and abiotic process parameters in anaerobic digestion systems, the microbial communities of nine full‐scale biogas plants in South Tyrol (Italy) and Vorarlberg (Austria) were investigated using molecular techniques and the physical and chemical properties were monitored. DNA from sludge samples was subjected to microarray hybridization with the ANAEROCHIP microarray and results indicated that sludge samples grouped into two main clusters, dominated either by Methanosarcina or by Methanosaeta, both aceticlastic methanogens. Hydrogenotrophic methanogens were hardly detected or if detected, gave low hybridization signals. Results obtained using denaturing gradient gel electrophoresis (DGGE) supported the findings of microarray hybridization. Real‐time PCR targeting Methanosarcina and Methanosaeta was conducted to provide quantitative data on the dominating methanogens. Correlation analysis to determine any links between the microbial communities found by microarray analysis, and the physicochemical parameters investigated was conducted. It was shown that the sludge samples dominated by the genus Methanosarcina were positively correlated with higher concentrations of acetate, whereas sludge samples dominated by representatives of the genus Methanosaeta had lower acetate concentrations. No other correlations between biotic characteristics and abiotic parameters were found. Methanogenic communities in each reactor were highly stable and resilient over the whole year. PMID:22950603

BμG@Sbase—a microbial gene expression and comparative genomic database

PubMed Central

Witney, Adam A.; Waldron, Denise E.; Brooks, Lucy A.; Tyler, Richard H.; Withers, Michael; Stoker, Neil G.; Wren, Brendan W.; Butcher, Philip D.; Hinds, Jason

2012-01-01

The reducing cost of high-throughput functional genomic technologies is creating a deluge of high volume, complex data, placing the burden on bioinformatics resources and tool development. The Bacterial Microarray Group at St George's (BμG@S) has been at the forefront of bacterial microarray design and analysis for over a decade and while serving as a hub of a global network of microbial research groups has developed BμG@Sbase, a microbial gene expression and comparative genomic database. BμG@Sbase (http://bugs.sgul.ac.uk/bugsbase/) is a web-browsable, expertly curated, MIAME-compliant database that stores comprehensive experimental annotation and multiple raw and analysed data formats. Consistent annotation is enabled through a structured set of web forms, which guide the user through the process following a set of best practices and controlled vocabulary. The database currently contains 86 expertly curated publicly available data sets (with a further 124 not yet published) and full annotation information for 59 bacterial microarray designs. The data can be browsed and queried using an explorer-like interface; integrating intuitive tree diagrams to present complex experimental details clearly and concisely. Furthermore the modular design of the database will provide a robust platform for integrating other data types beyond microarrays into a more Systems analysis based future. PMID:21948792

BμG@Sbase--a microbial gene expression and comparative genomic database.

PubMed

Witney, Adam A; Waldron, Denise E; Brooks, Lucy A; Tyler, Richard H; Withers, Michael; Stoker, Neil G; Wren, Brendan W; Butcher, Philip D; Hinds, Jason

2012-01-01

The reducing cost of high-throughput functional genomic technologies is creating a deluge of high volume, complex data, placing the burden on bioinformatics resources and tool development. The Bacterial Microarray Group at St George's (BμG@S) has been at the forefront of bacterial microarray design and analysis for over a decade and while serving as a hub of a global network of microbial research groups has developed BμG@Sbase, a microbial gene expression and comparative genomic database. BμG@Sbase (http://bugs.sgul.ac.uk/bugsbase/) is a web-browsable, expertly curated, MIAME-compliant database that stores comprehensive experimental annotation and multiple raw and analysed data formats. Consistent annotation is enabled through a structured set of web forms, which guide the user through the process following a set of best practices and controlled vocabulary. The database currently contains 86 expertly curated publicly available data sets (with a further 124 not yet published) and full annotation information for 59 bacterial microarray designs. The data can be browsed and queried using an explorer-like interface; integrating intuitive tree diagrams to present complex experimental details clearly and concisely. Furthermore the modular design of the database will provide a robust platform for integrating other data types beyond microarrays into a more Systems analysis based future.

Development and Validation of Sandwich ELISA Microarrays with Minimal Assay Interference

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gonzalez, Rachel M.; Servoss, Shannon; Crowley, Sheila A.

Sandwich enzyme-linked immunosorbent assay (ELISA) microarrays are emerging as a strong candidate platform for multiplex biomarker analysis because of the ELISA’s ability to quantitatively measure rare proteins in complex biological fluids. Advantages of this platform are high-throughput potential, assay sensitivity and stringency, and the similarity to the standard ELISA test, which facilitates assay transfer from a research setting to a clinical laboratory. However, a major concern with the multiplexing of ELISAs is maintaining high assay specificity. In this study, we systematically determine the amount of assay interference and noise contributed by individual components of the multiplexed 24-assay system. We findmore » that non-specific reagent cross-reactivity problems are relatively rare. We did identify the presence of contaminant antigens in a “purified antigen”. We tested the validated ELISA microarray chip using paired serum samples that had been collected from four women at a 6-month interval. This analysis demonstrated that protein levels typically vary much more between individuals then within an individual over time, a result which suggests that longitudinal studies may be useful in controlling for biomarker variability across a population. Overall, this research demonstrates the importance of a stringent screening protocol and the value of optimizing the antibody and antigen concentrations when designing chips for ELISA microarrays.« less

Use of principal components analysis and protein microarray to explore the association of HIV-1-specific IgG responses with disease progression.

PubMed

Gerns Storey, Helen L; Richardson, Barbra A; Singa, Benson; Naulikha, Jackie; Prindle, Vivian C; Diaz-Ochoa, Vladimir E; Felgner, Phil L; Camerini, David; Horton, Helen; John-Stewart, Grace; Walson, Judd L

2014-01-01

The role of HIV-1-specific antibody responses in HIV disease progression is complex and would benefit from analysis techniques that examine clusterings of responses. Protein microarray platforms facilitate the simultaneous evaluation of numerous protein-specific antibody responses, though excessive data are cumbersome in analyses. Principal components analysis (PCA) reduces data dimensionality by generating fewer composite variables that maximally account for variance in a dataset. To identify clusters of antibody responses involved in disease control, we investigated the association of HIV-1-specific antibody responses by protein microarray, and assessed their association with disease progression using PCA in a nested cohort design. Associations observed among collections of antibody responses paralleled protein-specific responses. At baseline, greater antibody responses to the transmembrane glycoprotein (TM) and reverse transcriptase (RT) were associated with higher viral loads, while responses to the surface glycoprotein (SU), capsid (CA), matrix (MA), and integrase (IN) proteins were associated with lower viral loads. Over 12 months greater antibody responses were associated with smaller decreases in CD4 count (CA, MA, IN), and reduced likelihood of disease progression (CA, IN). PCA and protein microarray analyses highlighted a collection of HIV-specific antibody responses that together were associated with reduced disease progression, and may not have been identified by examining individual antibody responses. This technique may be useful to explore multifaceted host-disease interactions, such as HIV coinfections.

Vaginal microbial flora analysis by next generation sequencing and microarrays; can microbes indicate vaginal origin in a forensic context?

PubMed

Benschop, Corina C G; Quaak, Frederike C A; Boon, Mathilde E; Sijen, Titia; Kuiper, Irene

2012-03-01

Forensic analysis of biological traces generally encompasses the investigation of both the person who contributed to the trace and the body site(s) from which the trace originates. For instance, for sexual assault cases, it can be beneficial to distinguish vaginal samples from skin or saliva samples. In this study, we explored the use of microbial flora to indicate vaginal origin. First, we explored the vaginal microbiome for a large set of clinical vaginal samples (n = 240) by next generation sequencing (n = 338,184 sequence reads) and found 1,619 different sequences. Next, we selected 389 candidate probes targeting genera or species and designed a microarray, with which we analysed a diverse set of samples; 43 DNA extracts from vaginal samples and 25 DNA extracts from samples from other body sites, including sites in close proximity of or in contact with the vagina. Finally, we used the microarray results and next generation sequencing dataset to assess the potential for a future approach that uses microbial markers to indicate vaginal origin. Since no candidate genera/species were found to positively identify all vaginal DNA extracts on their own, while excluding all non-vaginal DNA extracts, we deduce that a reliable statement about the cellular origin of a biological trace should be based on the detection of multiple species within various genera. Microarray analysis of a sample will then render a microbial flora pattern that is probably best analysed in a probabilistic approach.

Microarray RNA expression analysis of cerebral white matter lesions reveals changes in multiple functional pathways.

PubMed

Simpson, Julie E; Hosny, Ola; Wharton, Stephen B; Heath, Paul R; Holden, Hazel; Fernando, Malee S; Matthews, Fiona; Forster, Gill; O'Brien, John T; Barber, Robert; Kalaria, Raj N; Brayne, Carol; Shaw, Pamela J; Lewis, Claire E; Ince, Paul G

2009-02-01

White matter lesions (WML) in brain aging are linked to dementia and depression. Ischemia contributes to their pathogenesis but other mechanisms may contribute. We used RNA microarray analysis with functional pathway grouping as an unbiased approach to investigate evidence for additional pathogenetic mechanisms. WML were identified by MRI and pathology in brains donated to the Medical Research Council Cognitive Function and Ageing Study Cognitive Function and Aging Study. RNA was extracted to compare WML with nonlesional white matter samples from cases with lesions (WM[L]), and from cases with no lesions (WM[C]) using RNA microarray and pathway analysis. Functional pathways were validated for selected genes by quantitative real-time polymerase chain reaction and immunocytochemistry. We identified 8 major pathways in which multiple genes showed altered RNA transcription (immune regulation, cell cycle, apoptosis, proteolysis, ion transport, cell structure, electron transport, metabolism) among 502 genes that were differentially expressed in WML compared to WM[C]. In WM[L], 409 genes were altered involving the same pathways. Genes selected to validate this microarray data all showed the expected changes in RNA levels and immunohistochemical expression of protein. WML represent areas with a complex molecular phenotype. From this and previous evidence, WML may arise through tissue ischemia but may also reflect the contribution of additional factors like blood-brain barrier dysfunction. Differential expression of genes in WM[L] compared to WM[C] indicate a "field effect" in the seemingly normal surrounding white matter.

Microarray analysis of differentially expressed genes engaged in fruit development between Prunus mume and Prunus armeniaca.

PubMed

Li, Xiaoying; Korir, Nicholas Kibet; Liu, Lili; Shangguan, Lingfei; Wang, Yuzhu; Han, Jian; Chen, Ming; Fang, Jinggui

2012-11-15

Microarray analysis is a technique that can be employed to provide expression profiles of single genes and new insights to elucidate the biological mechanisms responsible for fruit development. To evaluate expression of genes mostly engaged in fruit development between Prunus mume and Prunus armeniaca, we first identified differentially expressed transcripts along the entire fruit life cycle by using microarrays spotted with 10,641 ESTs collected from P. mume and other Prunus EST sequences. A total of 1418 ESTs were selected after quality control of microarray spots and analysis for differential gene expression patterns during fruit development of P. mume and P. Armeniaca. From these, 707 up-regulated and 711 down-regulated genes showing more than two-fold differences in expression level were annotated by GO based on biological processes, molecular functions and cellular components. These differentially expressed genes were found to be involved in several important pathways of carbohydrate, galactose, and starch and sucrose metabolism as well as in biosynthesis of other secondary metabolites via KEGG. This could provide detailed information on the fruit quality differences during development and ripening of these two species. With the results obtained, we provide a practical database for comprehensive understanding of molecular events during fruit development and also lay a theoretical foundation for the cloning of genes regulating in a series of important rate-limiting enzymes involved in vital metabolic pathways during fruit development. Copyright © 2012 Elsevier GmbH. All rights reserved.

16S rRNA based microarray analysis of ten periodontal bacteria in patients with different forms of periodontitis.

PubMed

Topcuoglu, Nursen; Kulekci, Guven

2015-10-01

DNA microarray analysis is a computer based technology, that a reverse capture, which targets 10 periodontal bacteria (ParoCheck) is available for rapid semi-quantitative determination. The aim of this three-year retrospective study was to display the microarray analysis results for the subgingival biofilm samples taken from patient cases diagnosed with different forms of periodontitis. A total of 84 patients with generalized aggressive periodontitis (GAP,n:29), generalized chronic periodontitis (GCP, n:25), peri-implantitis (PI,n:14), localized aggressive periodontitis (LAP,n:8) and refractory chronic periodontitis (RP,n:8) were consecutively selected from the archives of the Oral Microbiological Diagnostic Laboratory. The subgingival biofilm samples were analyzed by the microarray-based identification of 10 selected species. All the tested species were detected in the samples. The red complex bacteria were the most prevalent with very high levels in all groups. Fusobacterium nucleatum was detected in all samples at high levels. The green and blue complex bacteria were less prevalent compared with red and orange complex, except Aggregatibacter actinomycetemcomitas was detected in all LAP group. Positive correlations were found within all the red complex bacteria and between red and orange complex bacteria especially in GCP and GAP groups. Parocheck enables to monitoring of periodontal pathogens in all forms of periodontal disease and can be alternative to other guiding and reliable microbiologic tests. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quantification of Estrogen Receptor Expression in Normal Breast Tissue in Postmenopausal Women With Breast Cancer and Association With Tumor Subtypes.

PubMed

Gulbahce, H Evin; Blair, Cindy K; Sweeney, Carol; Salama, Mohamed E

2017-09-01

Estrogen exposure is important in the pathogenesis of breast cancer and is a contributing risk factor. In this study we quantified estrogen receptor (ER) alpha expression in normal breast epithelium (NBR) in women with breast cancer and correlated it with breast cancer subtypes. Tissue microarrays were constructed from 204 breast cancer patients for whom normal breast tissue away from tumor was available. Slides stained with ER were scanned and expression in normal terminal duct lobular epithelium was quantitated using computer-assisted image analysis. ER expression in normal terminal duct lobular epithelium of postmenopausal women with breast cancer was significantly associated with estrogen and triple (estrogen, progesterone receptors, and HER2) negative phenotypes. Also increased age at diagnosis was significantly associated with ER expression in NBR. ER positivity in normal epithelium did not vary by tumor size, lymph node status, tumor grade, or stage. On the basis of quantitative image analysis, we confirm that ER expression in NBR increases with age in women with breast cancer, and report for the first time, a significant association between ER expression in NBR with ER-negative and triple-negative cancers in postmenopausal women.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

PubMed

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando; Treviño, Victor

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Fully Automated Fluorescent in situ Hybridization (FISH) Staining and Digital Analysis of HER2 in Breast Cancer: A Validation Study

PubMed Central

van der Logt, Elise M. J.; Kuperus, Deborah A. J.; van Setten, Jan W.; van den Heuvel, Marius C.; Boers, James. E.; Schuuring, Ed; Kibbelaar, Robby E.

2015-01-01

HER2 assessment is routinely used to select patients with invasive breast cancer that might benefit from HER2-targeted therapy. The aim of this study was to validate a fully automated in situ hybridization (ISH) procedure that combines the automated Leica HER2 fluorescent ISH system for Bond with supervised automated analysis with the Visia imaging D-Sight digital imaging platform. HER2 assessment was performed on 328 formalin-fixed/paraffin-embedded invasive breast cancer tumors on tissue microarrays (TMA) and 100 (50 selected IHC 2+ and 50 random IHC scores) full-sized slides of resections/biopsies obtained for diagnostic purposes previously. For digital analysis slides were pre-screened at 20x and 100x magnification for all fluorescent signals and supervised-automated scoring was performed on at least two pictures (in total at least 20 nuclei were counted) with the D-Sight HER2 FISH analysis module by two observers independently. Results were compared to data obtained previously with the manual Abbott FISH test. The overall agreement with Abbott FISH data among TMA samples and 50 selected IHC 2+ cases was 98.8% (κ = 0.94) and 93.8% (κ = 0.88), respectively. The results of 50 additionally tested unselected IHC cases were concordant with previously obtained IHC and/or FISH data. The combination of the Leica FISH system with the D-Sight digital imaging platform is a feasible method for HER2 assessment in routine clinical practice for patients with invasive breast cancer. PMID:25844540

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

PubMed Central

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures. PMID:29596496

mRNA-Seq and microarray development for the Grooved carpet shell clam, Ruditapes decussatus: a functional approach to unravel host -parasite interaction

PubMed Central

2013-01-01

Background The Grooved Carpet shell clam Ruditapes decussatus is the autochthonous European clam and the most appreciated from a gastronomic and economic point of view. The production is in decline due to several factors such as Perkinsiosis and habitat invasion and competition by the introduced exotic species, the manila clam Ruditapes philippinarum. After we sequenced R. decussatus transcriptome we have designed an oligo microarray capable of contributing to provide some clues on molecular response of the clam to Perkinsiosis. Results A database consisting of 41,119 unique transcripts was constructed, of which 12,479 (30.3%) were annotated by similarity. An oligo-DNA microarray platform was then designed and applied to profile gene expression in R. decussatus heavily infected by Perkinsus olseni. Functional annotation of differentially expressed genes between those two conditionswas performed by gene set enrichment analysis. As expected, microarrays unveil genes related with stress/infectious agents such as hydrolases, proteases and others. The extensive role of innate immune system was also analyzed and effect of parasitosis upon expression of important molecules such as lectins reviewed. Conclusions This study represents a first attempt to characterize Ruditapes decussatus transcriptome, an important marine resource for the European aquaculture. The trancriptome sequencing and consequent annotation will increase the available tools and resources for this specie, introducing the possibility of high throughput experiments such as microarrays analysis. In this specific case microarray approach was used to unveil some important aspects of host-parasite interaction between the Carpet shell clam and Perkinsus, two non-model species, highlighting some genes associated with this interaction. Ample information was obtained to identify biological processes significantly enriched among differentially expressed genes in Perkinsus infected versus non-infected gills. An overview on the genes related with the immune system on R. decussatus transcriptome is also reported. PMID:24168212

mRNA-Seq and microarray development for the Grooved Carpet shell clam, Ruditapes decussatus: a functional approach to unravel host-parasite interaction.

PubMed

Leite, Ricardo B; Milan, Massimo; Coppe, Alessandro; Bortoluzzi, Stefania; dos Anjos, António; Reinhardt, Richard; Saavedra, Carlos; Patarnello, Tomaso; Cancela, M Leonor; Bargelloni, Luca

2013-10-29

The Grooved Carpet shell clam Ruditapes decussatus is the autochthonous European clam and the most appreciated from a gastronomic and economic point of view. The production is in decline due to several factors such as Perkinsiosis and habitat invasion and competition by the introduced exotic species, the manila clam Ruditapes philippinarum. After we sequenced R. decussatus transcriptome we have designed an oligo microarray capable of contributing to provide some clues on molecular response of the clam to Perkinsiosis. A database consisting of 41,119 unique transcripts was constructed, of which 12,479 (30.3%) were annotated by similarity. An oligo-DNA microarray platform was then designed and applied to profile gene expression in R. decussatus heavily infected by Perkinsus olseni. Functional annotation of differentially expressed genes between those two conditionswas performed by gene set enrichment analysis. As expected, microarrays unveil genes related with stress/infectious agents such as hydrolases, proteases and others. The extensive role of innate immune system was also analyzed and effect of parasitosis upon expression of important molecules such as lectins reviewed. This study represents a first attempt to characterize Ruditapes decussatus transcriptome, an important marine resource for the European aquaculture. The trancriptome sequencing and consequent annotation will increase the available tools and resources for this specie, introducing the possibility of high throughput experiments such as microarrays analysis. In this specific case microarray approach was used to unveil some important aspects of host-parasite interaction between the Carpet shell clam and Perkinsus, two non-model species, highlighting some genes associated with this interaction. Ample information was obtained to identify biological processes significantly enriched among differentially expressed genes in Perkinsus infected versus non-infected gills. An overview on the genes related with the immune system on R. decussatus transcriptome is also reported.

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

PubMed Central

Kilicoglu, Halil; Shin, Dongwook; Rindflesch, Thomas C.

2014-01-01

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology. PMID:24921649

Augmenting microarray data with literature-based knowledge to enhance gene regulatory network inference.

PubMed

Chen, Guocai; Cairelli, Michael J; Kilicoglu, Halil; Shin, Dongwook; Rindflesch, Thomas C

2014-06-01

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method.

PubMed

Bengtsson, Henrik; Hössjer, Ola

2006-03-01

Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple methods have been suggested to date, but it is not clear which is the best. It is therefore important to further study the different normalization methods in detail and the nature of microarray data in general. A methodological study of affine models for gene expression data is carried out. Focus is on two-channel comparative studies, but the findings generalize also to single- and multi-channel data. The discussion applies to spotted as well as in-situ synthesized microarray data. Existing normalization methods such as curve-fit ("lowess") normalization, parallel and perpendicular translation normalization, and quantile normalization, but also dye-swap normalization are revisited in the light of the affine model and their strengths and weaknesses are investigated in this context. As a direct result from this study, we propose a robust non-parametric multi-dimensional affine normalization method, which can be applied to any number of microarrays with any number of channels either individually or all at once. A high-quality cDNA microarray data set with spike-in controls is used to demonstrate the power of the affine model and the proposed normalization method. We find that an affine model can explain non-linear intensity-dependent systematic effects in observed log-ratios. Affine normalization removes such artifacts for non-differentially expressed genes and assures that symmetry between negative and positive log-ratios is obtained, which is fundamental when identifying differentially expressed genes. In addition, affine normalization makes the empirical distributions in different channels more equal, which is the purpose of quantile normalization, and may also explain why dye-swap normalization works or fails. All methods are made available in the aroma package, which is a platform-independent package for R.

Waveguide-excited fluorescence microarray

NASA Astrophysics Data System (ADS)

Sagarzazu, Gabriel; Bedu, Mélanie; Martinelli, Lucio; Ha, Khoi-Nguyen; Pelletier, Nicolas; Safarov, Viatcheslav I.; Weisbuch, Claude; Gacoin, Thierry; Benisty, Henri

2008-04-01

Signal-to-noise ratio is a crucial issue in microarray fluorescence read-out. Several strategies are proposed for its improvement. First, light collection in conventional microarrays scanners is quite limited. It was recently shown that almost full collection can be achieved in an integrated lens-free biosensor, with labelled species hybridizing practically on the surface of a sensitive silicon detector [L. Martinelli et al. Appl. Phys. Lett. 91, 083901 (2007)]. However, even with such an improvement, the ultimate goal of real-time measurements during hybridization is challenging: the detector is dazzled by the large fluorescence of labelled species in the solution. In the present paper we show that this unwanted signal can effectively be reduced if the excitation light is confined in a waveguide. Moreover, the concentration of excitation light in a waveguide results in a huge signal gain. In our experiment we realized a structure consisting of a high index sol-gel waveguide deposited on a low-index substrate. The fluorescent molecules deposited on the surface of the waveguide were excited by the evanescent part of a wave travelling in the guide. The comparison with free-space excitation schemes confirms a huge gain (by several orders of magnitude) in favour of waveguide-based excitation. An optical guide deposited onto an integrated biosensor thus combines both advantages of ideal light collection and enhanced surface localized excitation without compromising the imaging properties. Modelling predicts a negligible penalty from spatial cross-talk in practical applications. We believe that such a system would bring microarrays to hitherto unattained sensitivities.

Data-adaptive test statistics for microarray data.

PubMed

Mukherjee, Sach; Roberts, Stephen J; van der Laan, Mark J

2005-09-01

An important task in microarray data analysis is the selection of genes that are differentially expressed between different tissue samples, such as healthy and diseased. However, microarray data contain an enormous number of dimensions (genes) and very few samples (arrays), a mismatch which poses fundamental statistical problems for the selection process that have defied easy resolution. In this paper, we present a novel approach to the selection of differentially expressed genes in which test statistics are learned from data using a simple notion of reproducibility in selection results as the learning criterion. Reproducibility, as we define it, can be computed without any knowledge of the 'ground-truth', but takes advantage of certain properties of microarray data to provide an asymptotically valid guide to expected loss under the true data-generating distribution. We are therefore able to indirectly minimize expected loss, and obtain results substantially more robust than conventional methods. We apply our method to simulated and oligonucleotide array data. By request to the corresponding author.

Analysis of mutations in oral poliovirus vaccine by hybridization with generic oligonucleotide microchips.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Proudnikov, D.; Kirillov, E.; Chumakov, K.

2000-01-01

This paper describes use of a new technology of hybridization with a micro-array of immobilized oligonucleotides for detection and quantification of neurovirulent mutants in Oral Poliovirus Vaccine (OPV). We used a micro-array consisting of three-dimensional gel-elements containing all possible hexamers (total of 4096 probes). Hybridization of fluorescently labelled viral cDNA samples with such microchips resulted in a pattern of spots that was registered and quantified by a computer-linked CCD camera, so that the sequence of the original cDNA could be deduced. The method could reliably identify single point mutations, since each of them affected fluorescence intensity of 12 micro-array elements.more » Micro-array hybridization of DNA mixtures with varying contents of point mutants demonstrated that the method can detect as little as 10% of revertants in a population of vaccine virus. This new technology should be useful for quality control of live viral vaccines, as well as for other applications requiring identification and quantification of point mutations.« less

Microscopy Images as Interactive Tools in Cell Modeling and Cell Biology Education

ERIC Educational Resources Information Center

Araujo-Jorge, Tania C.; Cardona, Tania S.; Mendes, Claudia L. S.; Henriques-Pons, Andrea; Meirelles, Rosane M. S.; Coutinho, Claudia M. L. M.; Aguiar, Luiz Edmundo V.; Meirelles, Maria de Nazareth L.; de Castro, Solange L.; Barbosa, Helene S.; Luz, Mauricio R. M. P.

2004-01-01

The advent of genomics, proteomics, and microarray technology has brought much excitement to science, both in teaching and in learning. The public is eager to know about the processes of life. In the present context of the explosive growth of scientific information, a major challenge of modern cell biology is to popularize basic concepts of…

Wide-field lensless fluorescent microscopy using a tapered fiber-optic faceplate on a chip.

PubMed

Coskun, Ahmet F; Sencan, Ikbal; Su, Ting-Wei; Ozcan, Aydogan

2011-09-07

We demonstrate lensless fluorescent microscopy over a large field-of-view of ~60 mm(2) with a spatial resolution of <4 µm. In this on-chip fluorescent imaging modality, the samples are placed on a fiber-optic faceplate that is tapered such that the density of the fiber-optic waveguides on the top facet is >5 fold larger than the bottom one. Placed on this tapered faceplate, the fluorescent samples are pumped from the side through a glass hemisphere interface. After excitation of the samples, the pump light is rejected through total internal reflection that occurs at the bottom facet of the sample substrate. The fluorescent emission from the sample is then collected by the smaller end of the tapered faceplate and is delivered to an opto-electronic sensor-array to be digitally sampled. Using a compressive sampling algorithm, we decode these raw lensfree images to validate the resolution (<4 µm) of this on-chip fluorescent imaging platform using microparticles as well as labeled Giardia muris cysts. This wide-field lensfree fluorescent microscopy platform, being compact and high-throughput, might provide a valuable tool especially for cytometry, rare cell analysis (involving large area microfluidic systems) as well as for microarray imaging applications.

Comparative transcriptional and translational analysis of leptospiral outer membrane protein expression in response to temperature.

PubMed

Lo, Miranda; Cordwell, Stuart J; Bulach, Dieter M; Adler, Ben

2009-12-08

Leptospirosis is a global zoonosis affecting millions of people annually. Transcriptional changes in response to temperature were previously investigated using microarrays to identify genes potentially expressed upon host entry. Past studies found that various leptospiral outer membrane proteins are differentially expressed at different temperatures. However, our microarray studies highlighted a divergence between protein abundance and transcript levels for some proteins. Given the abundance of post-transcriptional expression control mechanisms, this finding highlighted the importance of global protein analysis systems. To complement our previous transcription study, we evaluated differences in the proteins of the leptospiral outer membrane fraction in response to temperature upshift. Outer membrane protein-enriched fractions from Leptospira interrogans grown at 30 degrees C or overnight upshift to 37 degrees C were isolated and the relative abundance of each protein was determined by iTRAQ analysis coupled with two-dimensional liquid chromatography and tandem mass spectrometry (2-DLC/MS-MS). We identified 1026 proteins with 99% confidence; 27 and 66 were present at elevated and reduced abundance respectively. Protein abundance changes were compared with transcriptional differences determined from the microarray studies. While there was some correlation between the microarray and iTRAQ data, a subset of genes that showed no differential expression by microarray was found to encode temperature-regulated proteins. This set of genes is of particular interest as it is likely that regulation of their expression occurs post-transcriptionally, providing an opportunity to develop hypotheses about the molecular dynamics of the outer membrane of Leptospira in response to changing environments. This is the first study to compare transcriptional and translational responses to temperature shift in L. interrogans. The results thus provide an insight into the mechanisms used by L. interrogans to adapt to conditions encountered in the host and to cause disease. Our results suggest down-regulation of protein expression in response to temperature, and decreased expression of outer membrane proteins may facilitate minimal interaction with host immune mechanisms.

Is this the real time for genomics?

PubMed

Guarnaccia, Maria; Gentile, Giulia; Alessi, Enrico; Schneider, Claudio; Petralia, Salvatore; Cavallaro, Sebastiano

2014-01-01

In the last decades, molecular biology has moved from gene-by-gene analysis to more complex studies using a genome-wide scale. Thanks to high-throughput genomic technologies, such as microarrays and next-generation sequencing, a huge amount of information has been generated, expanding our knowledge on the genetic basis of various diseases. Although some of this information could be transferred to clinical diagnostics, the technologies available are not suitable for this purpose. In this review, we will discuss the drawbacks associated with the use of traditional DNA microarrays in diagnostics, pointing out emerging platforms that could overcome these obstacles and offer a more reproducible, qualitative and quantitative multigenic analysis. New miniaturized and automated devices, called Lab-on-Chip, begin to integrate PCR and microarray on the same platform, offering integrated sample-to-result systems. The introduction of this kind of innovative devices may facilitate the transition of genome-based tests into clinical routine. Copyright © 2014. Published by Elsevier Inc.

Derivation of an artificial gene to improve classification accuracy upon gene selection.

PubMed

Seo, Minseok; Oh, Sejong

2012-02-01

Classification analysis has been developed continuously since 1936. This research field has advanced as a result of development of classifiers such as KNN, ANN, and SVM, as well as through data preprocessing areas. Feature (gene) selection is required for very high dimensional data such as microarray before classification work. The goal of feature selection is to choose a subset of informative features that reduces processing time and provides higher classification accuracy. In this study, we devised a method of artificial gene making (AGM) for microarray data to improve classification accuracy. Our artificial gene was derived from a whole microarray dataset, and combined with a result of gene selection for classification analysis. We experimentally confirmed a clear improvement of classification accuracy after inserting artificial gene. Our artificial gene worked well for popular feature (gene) selection algorithms and classifiers. The proposed approach can be applied to any type of high dimensional dataset. Copyright © 2011 Elsevier Ltd. All rights reserved.

High-Throughput Lectin Microarray-Based Analysis of Live Cell Surface Glycosylation

PubMed Central

Li, Yu; Tao, Sheng-ce; Zhu, Heng; Schneck, Jonathan P.

2011-01-01

Lectins, plant-derived glycan-binding proteins, have long been used to detect glycans on cell surfaces. However, the techniques used to characterize serum or cells have largely been limited to mass spectrometry, blots, flow cytometry, and immunohistochemistry. While these lectin-based approaches are well established and they can discriminate a limited number of sugar isomers by concurrently using a limited number of lectins, they are not amenable for adaptation to a high-throughput platform. Fortunately, given the commercial availability of lectins with a variety of glycan specificities, lectins can be printed on a glass substrate in a microarray format to profile accessible cell-surface glycans. This method is an inviting alternative for analysis of a broad range of glycans in a high-throughput fashion and has been demonstrated to be a feasible method of identifying binding-accessible cell surface glycosylation on living cells. The current unit presents a lectin-based microarray approach for analyzing cell surface glycosylation in a high-throughput fashion. PMID:21400689

Customizing chemotherapy for colon cancer: the potential of gene expression profiling.

PubMed

Mariadason, John M; Arango, Diego; Augenlicht, Leonard H

2004-06-01

The value of gene expression profiling, or microarray analysis, for the classification and prognosis of multiple forms of cancer is now clearly established. For colon cancer, expression profiling can readily discriminate between normal and tumor tissue, and to some extent between tumors of different histopathological stage and prognosis. While a definitive in vivo study demonstrating the potential of this methodology for predicting response to chemotherapy is presently lacking, the ability of microarrays to distinguish other subtleties of colon cancer phenotype, as well as recent in vitro proof-of-principle experiments utilizing colon cancer cell lines, illustrate the potential of this methodology for predicting the probability of response to specific chemotherapeutic agents. This review discusses some of the recent advances in the use of microarray analysis for understanding and distinguishing colon cancer subtypes, and attempts to identify challenges that need to be overcome in order to achieve the goal of using gene expression profiling for customizing chemotherapy in colon cancer.

Alteration of gene expression in the colon of colorectal cancer model rat by dietary sodium gluconate.

PubMed

Kameue, Chiyoko; Tsukahara, Takamitsu; Ushida, Kazunari

2006-03-01

Butyrate induces apoptosis of various cancer cell lines in a p53-independent manner and inhibits the proliferation of cancer cells. In a previous report, we reported a significant reduction in tumor incidence in rat colon as a result of dietary sodium gluconate (GNA). The stimulation of apoptosis through enhanced butyrate production in the large intestine was involved in the antitumorigenic effect of GNA. In the present study, a cDNA microarray analysis was performed to investigate the particular mechanism involved in the antitumorigenic effect of GNA. Some up-regulated genes suggested by microarray analysis were further evaluated using real-time PCR. A microarray revealed that GNA regulates the expression of retinoic acid receptor (RAR) and retinoid X receptor (RXR), and several genes known as the target of retinoids in cancer cells. In other words, the antitumorigenic effect of GNA may involve the regulation of the retinoid signaling pathway by butyrate in a retinoid-independent manner.

A Sol-gel Integrated Dual-readout Microarray Platform for Quantification and Identification of Prostate-specific Antigen.

PubMed

Lee, SangWook; Lee, Jong Hyun; Kwon, Hyuck Gi; Laurell, Thomas; Jeong, Ok Chan; Kim, Soyoun

2018-01-01

Here, we report a sol-gel integrated affinity microarray for on-chip matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) that enables capture and identification of prostate?specific antigen (PSA) in samples. An anti-PSA antibody (H117) was mixed with a sol?gel, and the mixture was spotted onto a porous silicon (pSi) surface without additional surface modifications. The antibody easily penetrates the sol-gel macropore fluidic network structure, making possible high affinities. To assess the capture affinity of the platform, we performed a direct assay using fluorescein isothiocyanate-labeled PSA. Pure PSA was subjected to on-chip MALDI-TOF-MS analysis, yielding three clear mass peptide peaks (m/z = 1272, 1407, and 1872). The sol-gel microarray platform enables dual readout of PSA both fluorometric and MALDI-TOF MS analysis in biological samples. Here we report a useful method for a means for discovery of biomarkers in complex body fluids.

Statistical Analysis of Microarray Data with Replicated Spots: A Case Study with Synechococcus WH8102

PubMed Central

Thomas, E. V.; Phillippy, K. H.; Brahamsha, B.; Haaland, D. M.; Timlin, J. A.; Elbourne, L. D. H.; Palenik, B.; Paulsen, I. T.

2009-01-01

Until recently microarray experiments often involved relatively few arrays with only a single representation of each gene on each array. A complete genome microarray with multiple spots per gene (spread out spatially across the array) was developed in order to compare the gene expression of a marine cyanobacterium and a knockout mutant strain in a defined artificial seawater medium. Statistical methods were developed for analysis in the special situation of this case study where there is gene replication within an array and where relatively few arrays are used, which can be the case with current array technology. Due in part to the replication within an array, it was possible to detect very small changes in the levels of expression between the wild type and mutant strains. One interesting biological outcome of this experiment is the indication of the extent to which the phosphorus regulatory system of this cyanobacterium affects the expression of multiple genes beyond those strictly involved in phosphorus acquisition. PMID:19404483

Statistical Analysis of Microarray Data with Replicated Spots: A Case Study with Synechococcus WH8102

DOE PAGES

Thomas, E. V.; Phillippy, K. H.; Brahamsha, B.; ...

2009-01-01

Until recently microarray experiments often involved relatively few arrays with only a single representation of each gene on each array. A complete genome microarray with multiple spots per gene (spread out spatially across the array) was developed in order to compare the gene expression of a marine cyanobacterium and a knockout mutant strain in a defined artificial seawater medium. Statistical methods were developed for analysis in the special situation of this case study where there is gene replication within an array and where relatively few arrays are used, which can be the case with current array technology. Due in partmore » to the replication within an array, it was possible to detect very small changes in the levels of expression between the wild type and mutant strains. One interesting biological outcome of this experiment is the indication of the extent to which the phosphorus regulatory system of this cyanobacterium affects the expression of multiple genes beyond those strictly involved in phosphorus acquisition.« less

Computerized system for recognition of autism on the basis of gene expression microarray data.

PubMed

Latkowski, Tomasz; Osowski, Stanislaw

2015-01-01

The aim of this paper is to provide a means to recognize a case of autism using gene expression microarrays. The crucial task is to discover the most important genes which are strictly associated with autism. The paper presents an application of different methods of gene selection, to select the most representative input attributes for an ensemble of classifiers. The set of classifiers is responsible for distinguishing autism data from the reference class. Simultaneous application of a few gene selection methods enables analysis of the ill-conditioned gene expression matrix from different points of view. The results of selection combined with a genetic algorithm and SVM classifier have shown increased accuracy of autism recognition. Early recognition of autism is extremely important for treatment of children and increases the probability of their recovery and return to normal social communication. The results of this research can find practical application in early recognition of autism on the basis of gene expression microarray analysis. Copyright © 2014 Elsevier Ltd. All rights reserved.

Computational synchronization of microarray data with application to Plasmodium falciparum.

PubMed

Zhao, Wei; Dauwels, Justin; Niles, Jacquin C; Cao, Jianshu

2012-06-21

Microarrays are widely used to investigate the blood stage of Plasmodium falciparum infection. Starting with synchronized cells, gene expression levels are continually measured over the 48-hour intra-erythrocytic cycle (IDC). However, the cell population gradually loses synchrony during the experiment. As a result, the microarray measurements are blurred. In this paper, we propose a generalized deconvolution approach to reconstruct the intrinsic expression pattern, and apply it to P. falciparum IDC microarray data. We develop a statistical model for the decay of synchrony among cells, and reconstruct the expression pattern through statistical inference. The proposed method can handle microarray measurements with noise and missing data. The original gene expression patterns become more apparent in the reconstructed profiles, making it easier to analyze and interpret the data. We hypothesize that reconstructed gene expression patterns represent better temporally resolved expression profiles that can be probabilistically modeled to match changes in expression level to IDC transitions. In particular, we identify transcriptionally regulated protein kinases putatively involved in regulating the P. falciparum IDC. By analyzing publicly available microarray data sets for the P. falciparum IDC, protein kinases are ranked in terms of their likelihood to be involved in regulating transitions between the ring, trophozoite and schizont developmental stages of the P. falciparum IDC. In our theoretical framework, a few protein kinases have high probability rankings, and could potentially be involved in regulating these developmental transitions. This study proposes a new methodology for extracting intrinsic expression patterns from microarray data. By applying this method to P. falciparum microarray data, several protein kinases are predicted to play a significant role in the P. falciparum IDC. Earlier experiments have indeed confirmed that several of these kinases are involved in this process. Overall, these results indicate that further functional analysis of these additional putative protein kinases may reveal new insights into how the P. falciparum IDC is regulated.

Microarray-Based Gene Expression Analysis for Veterinary Pathologists: A Review.

PubMed

Raddatz, Barbara B; Spitzbarth, Ingo; Matheis, Katja A; Kalkuhl, Arno; Deschl, Ulrich; Baumgärtner, Wolfgang; Ulrich, Reiner

2017-09-01

High-throughput, genome-wide transcriptome analysis is now commonly used in all fields of life science research and is on the cusp of medical and veterinary diagnostic application. Transcriptomic methods such as microarrays and next-generation sequencing generate enormous amounts of data. The pathogenetic expertise acquired from understanding of general pathology provides veterinary pathologists with a profound background, which is essential in translating transcriptomic data into meaningful biological knowledge, thereby leading to a better understanding of underlying disease mechanisms. The scientific literature concerning high-throughput data-mining techniques usually addresses mathematicians or computer scientists as the target audience. In contrast, the present review provides the reader with a clear and systematic basis from a veterinary pathologist's perspective. Therefore, the aims are (1) to introduce the reader to the necessary methodological background; (2) to introduce the sequential steps commonly performed in a microarray analysis including quality control, annotation, normalization, selection of differentially expressed genes, clustering, gene ontology and pathway analysis, analysis of manually selected genes, and biomarker discovery; and (3) to provide references to publically available and user-friendly software suites. In summary, the data analysis methods presented within this review will enable veterinary pathologists to analyze high-throughput transcriptome data obtained from their own experiments, supplemental data that accompany scientific publications, or public repositories in order to obtain a more in-depth insight into underlying disease mechanisms.

Comparing microarrays and next-generation sequencing technologies for microbial ecology research.

PubMed

Roh, Seong Woon; Abell, Guy C J; Kim, Kyoung-Ho; Nam, Young-Do; Bae, Jin-Woo

2010-06-01

Recent advances in molecular biology have resulted in the application of DNA microarrays and next-generation sequencing (NGS) technologies to the field of microbial ecology. This review aims to examine the strengths and weaknesses of each of the methodologies, including depth and ease of analysis, throughput and cost-effectiveness. It also intends to highlight the optimal application of each of the individual technologies toward the study of a particular environment and identify potential synergies between the two main technologies, whereby both sample number and coverage can be maximized. We suggest that the efficient use of microarray and NGS technologies will allow researchers to advance the field of microbial ecology, and importantly, improve our understanding of the role of microorganisms in their various environments.

Volcano plots in analyzing differential expressions with mRNA microarrays.

PubMed

Li, Wentian

2012-12-01

A volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log(10)(p-value) from the t-test). We review the basic and interactive use of the volcano plot and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide a unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility of applying volcano plots to other fields beyond microarray.

Expression analysis of URI/RMP gene in endometrioid adenocarcinoma by tissue microarray immunohistochemistry.

PubMed

Gu, Junxia; Liang, Yuting; Qiao, Longwei; Li, Xiaoyun; Li, Xingang; Lu, Yaojuan; Zheng, Qiping

2013-01-01

Multiple studies have recently demonstrated the oncogenic property of URI (or RMP, a member of the prefoldin family of molecular chaperones) during progression of hepatocellular carcinoma, ovarian cancer, and possibly prostate cancer. Most recently, we have shown that URI/RMP is up-regulated in cervical cancer, another reproductive system tumor beside ovarian and prostate cancers. To investigate if URI/RMP also plays a role in other reproductive system tumors, especially in endometrioid adenocarcinoma, we analyzed URI/RMP expression in a TMA (tissue microarray) containing tissues from 30 cases of endometrioid adenocarcinoma (which covers tumor tissues from Grade I through Grade III) and adjacent endometrium by immunohistochemistry (IHC) and densitometry analysis using image-pro plus 6.0 software. Our results showed that the mean density of URI/RMP expression in cancerous tissue is slightly higher than that of the adjacent endometrial tissue, though not statistically significant (p>0.05). There is no significant difference either between the mean density of Grade III cancerous tissue and that of Grade I and II cancers. Notably, we detected significantly higher signal intensity in cancerous tissue of all 7 Grade III cases than that of their adjacent endometrial tissue (p<0.05), suggesting a correlation of URI/RMP expression with the differentiation and pathological classification of endometrioid adenocarcinoma. Together, our results demonstrate the heterogeneous expression of URI/RMP in endometrioid adenocarcinoma. The higher level of URI/RMP expression in high-grade endometrioid adenocarcinomas compared to tissues of adjacent endometrium or gland suggests a diagnostic and possibly, a prognostic value of URI/RMP in endometrioid adenocarcinoma.

Expression analysis of URI/RMP gene in endometrioid adenocarcinoma by tissue microarray immunohistochemistry

PubMed Central

Gu, Junxia; Liang, Yuting; Qiao, Longwei; Li, Xiaoyun; Li, Xingang; Lu, Yaojuan; Zheng, Qiping

2013-01-01

Multiple studies have recently demonstrated the oncogenic property of URI (or RMP, a member of the prefoldin family of molecular chaperones) during progression of hepatocellular carcinoma, ovarian cancer, and possibly prostate cancer. Most recently, we have shown that URI/RMP is up-regulated in cervical cancer, another reproductive system tumor beside ovarian and prostate cancers. To investigate if URI/RMP also plays a role in other reproductive system tumors, especially in endometrioid adenocarcinoma, we analyzed URI/RMP expression in a TMA (tissue microarray) containing tissues from 30 cases of endometrioid adenocarcinoma (which covers tumor tissues from Grade I through Grade III) and adjacent endometrium by immunohistochemistry (IHC) and densitometry analysis using image-pro plus 6.0 software. Our results showed that the mean density of URI/RMP expression in cancerous tissue is slightly higher than that of the adjacent endometrial tissue, though not statistically significant (p>0.05). There is no significant difference either between the mean density of Grade III cancerous tissue and that of Grade I and II cancers. Notably, we detected significantly higher signal intensity in cancerous tissue of all 7 Grade III cases than that of their adjacent endometrial tissue (p<0.05), suggesting a correlation of URI/RMP expression with the differentiation and pathological classification of endometrioid adenocarcinoma. Together, our results demonstrate the heterogeneous expression of URI/RMP in endometrioid adenocarcinoma. The higher level of URI/RMP expression in high-grade endometrioid adenocarcinomas compared to tissues of adjacent endometrium or gland suggests a diagnostic and possibly, a prognostic value of URI/RMP in endometrioid adenocarcinoma. PMID:24228101

Significance of estrogen receptor 1 (ESR-1) gene imbalances in colon and hepatocellular carcinomas based on tissue microarrays analysis.

PubMed

Tsiambas, Evangelos; Georgiannos, Stavros N; Salemis, Nikolaos; Alexopoulou, Despoina; Lambropoulou, Sofia; Dimo, Blerta; Ioannidis, Ioannis; Kravvaritis, Christos; Karameris, Andreas; Patsouris, Efstratios; Dourakis, Spyridon

2011-12-01

Estrogen receptor alpha-encoded by ESR1 gene-overexpression correlates with prognosis and response to specific chemotherapy in breast adenocarcinoma cases. Mechanisms of ESR-1 deregulation in carcinomas remain under investigation. To analyze ESR1 in carcinomas of different histogenesis. Using tissue microarray technology, 172 primary carcinomas including breast ductal adenocarcinomas (n=60), hepatocellular carcinomas (n=52), and colon adenocarcinomas (n=60) were cored and re-embedded in three paraffin blocks. Initial diagnosis was based on liquid based cytology (LiquiPrep/ThinPrep). Immunohistochemistry and fluorescence in situ hybridization were performed. Quantitative evaluation of ER-a protein levels was assessed by applying digital image analysis. ER-a overexpression was observed in 41/60 (68.3%), 23/52 (44.2%) and 4/60 (6.6%) cases, respectively. ESR1 gene multiple copies were confirmed in 13/60 (21.6%) breast adenocarcinomas, but high amplification only in 8/13 (62.8%). Allelic absence was identified in 3/52 (5.7%) hepatocellular carcinomas, whereas colon adenocarcinomas demonstrated gene gains in 5/60 (8.3%) cases referred to chr 6 aneuploidy and not to amplification. ER-a overall expression was associated strongly to ESR1 gene copies only in breast carcinoma (P=0.036). ESR-1 gene overexpression happens frequently in breast cancer, but only a subset of them are high amplified cases correlated to increased response rates in hormonal therapy (tamoxifen). Absence of this mechanism in hepatocellular and colon carcinomas maybe is a negative factor for applying this therapy. This is a pattern of histo-genetic depended targeted therapeutic strategy.

A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.

PubMed

Astola, Laura; Molenaar, Jaap

2014-07-01

Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

Role of PELP1 in EGFR-ER Signaling Crosstalk in Ovarian Cancer Cells

DTIC Science & Technology

2009-04-01

expression of genes involved in metastasis using a focused microarray approach. We have used Human Tumor Metastasis Microarray (Oligo GE array from...ovarian cancer progression. Analysis of human genome databases and SAGE data suggested deregulation of PELP1 expression in ovarian cancer cells...PI3K, and STAT3 in the cytosol. PELP1/MNAR regulates meiosis via its interactions with heterotimeric Gbc protein, androgen receptor (AR), and by

Unique Proteins Expressed by Blood Vessels in Skeletal Sites Colonized by Breast Cancer Cells

DTIC Science & Technology

2006-08-01

fluorescent labeled acetylated LDL at an accelerated rate (3). After one week in culture BVECs and MVECs were harvested. Total RNA was extracted from...both cell types using the Qiagen RNeasy kit (Valencia, CA). Microarray labeling, hybridization and analysis was conducted on the RNA by the Penn...State University DNA Microarray Facility under the direction of Dr. Craig Praul. Briefly, RNA obtained from three separate isolations of BVECs and

Gene expression analysis of rheumatoid arthritis synovial lining regions by cDNA microarray combined with laser microdissection: up-regulation of inflammation-associated STAT1, IRF1, CXCL9, CXCL10, and CCL5

PubMed Central

Yoshida, S; Arakawa, F; Higuchi, F; Ishibashi, Y; Goto, M; Sugita, Y; Nomura, Y; Niino, D; Shimizu, K; Aoki, R; Hashikawa, K; Kimura, Y; Yasuda, K; Tashiro, K; Kuhara, S; Nagata, K; Ohshima, K

2012-01-01

Objectives The main histological change in rheumatoid arthritis (RA) is the villous proliferation of synovial lining cells, an important source of cytokines and chemokines, which are associated with inflammation. The aim of this study was to evaluate gene expression in the microdissected synovial lining cells of RA patients, using those of osteoarthritis (OA) patients as the control. Methods Samples were obtained during total joint replacement from 11 RA and five OA patients. Total RNA from the synovial lining cells was derived from selected specimens by laser microdissection (LMD) for subsequent cDNA microarray analysis. In addition, the expression of significant genes was confirmed immunohistochemically. Results The 14 519 genes detected by cDNA microarray were used to compare gene expression levels in synovial lining cells from RA with those from OA patients. Cluster analysis indicated that RA cells, including low- and high-expression subgroups, and OA cells were stored in two main clusters. The molecular activity of RA was statistically consistent with its clinical and histological activity. Expression levels of signal transducer and activator of transcription 1 (STAT1), interferon regulatory factor 1 (IRF1), and the chemokines CXCL9, CXCL10, and CCL5 were statistically significantly higher in the synovium of RA than in that of OA. Immunohistochemically, the lining synovium of RA, but not that of OA, clearly expressed STAT1, IRF1, and chemokines, as was seen in microarray analysis combined with LMD. Conclusions Our findings indicate an important role for lining synovial cells in the inflammatory and proliferative processes of RA. Further understanding of the local signalling in structural components is important in rheumatology. PMID:22401175

Microarray-based analysis of gene expression in lycopersicon esculentum seedling roots in response to cadmium, chromium, mercury, and lead.

PubMed

Hou, Jing; Liu, Xinhui; Wang, Juan; Zhao, Shengnan; Cui, Baoshan

2015-02-03

The effects of heavy metals in agricultural soils have received special attention due to their potential for accumulation in crops, which can affect species at all trophic levels. Therefore, there is a critical need for reliable bioassays for assessing risk levels due to heavy metals in agricultural soil. In the present study, we used microarrays to investigate changes in gene expression of Lycopersicon esculentum in response to Cd-, Cr-, Hg-, or Pb-spiked soil. Exposure to (1)/10 median lethal concentrations (LC50) of Cd, Cr, Hg, or Pb for 7 days resulted in expression changes in 29 Cd-specific, 58 Cr-specific, 192 Hg-specific and 864 Pb-specific genes as determined by microarray analysis, whereas conventional morphological and physiological bioassays did not reveal any toxicant stresses. Hierarchical clustering analysis showed that the characteristic gene expression profiles induced by Cd, Cr, Hg, and Pb were distinct from not only the control but also one another. Furthermore, a total of three genes related to "ion transport" for Cd, 14 genes related to "external encapsulating structure organization", "reproductive developmental process", "lipid metabolic process" and "response to stimulus" for Cr, 11 genes related to "cellular metabolic process" and "cellular response to stimulus" for Hg, 78 genes related to 20 biological processes (e.g., DNA metabolic process, monosaccharide catabolic process, cell division) for Pb were identified and selected as their potential biomarkers. These findings demonstrated that microarray-based analysis of Lycopersicon esculentum was a sensitive tool for the early detection of potential toxicity of heavy metals in agricultural soil, as well as an effective tool for identifying the heavy metal-specific genes, which should be useful for assessing risk levels due to heavy metals in agricultural soil.