Morphological observation and analysis using automated image cytometry for the comparison of trypan blue and fluorescence-based viability detection method.

PubMed

Chan, Leo Li-Ying; Kuksin, Dmitry; Laverty, Daniel J; Saldi, Stephanie; Qiu, Jean

2015-05-01

The ability to accurately determine cell viability is essential to performing a well-controlled biological experiment. Typical experiments range from standard cell culturing to advanced cell-based assays that may require cell viability measurement for downstream experiments. The traditional cell viability measurement method has been the trypan blue (TB) exclusion assay. However, since the introduction of fluorescence-based dyes for cell viability measurement using flow or image-based cytometry systems, there have been numerous publications comparing the two detection methods. Although previous studies have shown discrepancies between TB exclusion and fluorescence-based viability measurements, image-based morphological analysis was not performed in order to examine the viability discrepancies. In this work, we compared TB exclusion and fluorescence-based viability detection methods using image cytometry to observe morphological changes due to the effect of TB on dead cells. Imaging results showed that as the viability of a naturally-dying Jurkat cell sample decreased below 70 %, many TB-stained cells began to exhibit non-uniform morphological characteristics. Dead cells with these characteristics may be difficult to count under light microscopy, thus generating an artificially higher viability measurement compared to fluorescence-based method. These morphological observations can potentially explain the differences in viability measurement between the two methods.

An Improved Algorithm of Congruent Matching Cells (CMC) Method for Firearm Evidence Identifications

PubMed Central

Tong, Mingsi; Song, John; Chu, Wei

2015-01-01

The Congruent Matching Cells (CMC) method was invented at the National Institute of Standards and Technology (NIST) for firearm evidence identifications. The CMC method divides the measured image of a surface area, such as a breech face impression from a fired cartridge case, into small correlation cells and uses four identification parameters to identify correlated cell pairs originating from the same firearm. The CMC method was validated by identification tests using both 3D topography images and optical images captured from breech face impressions of 40 cartridge cases fired from a pistol with 10 consecutively manufactured slides. In this paper, we discuss the processing of the cell correlations and propose an improved algorithm of the CMC method which takes advantage of the cell correlations at a common initial phase angle and combines the forward and backward correlations to improve the identification capability. The improved algorithm is tested by 780 pairwise correlations using the same optical images and 3D topography images as the initial validation. PMID:26958441

An Improved Algorithm of Congruent Matching Cells (CMC) Method for Firearm Evidence Identifications.

PubMed

Tong, Mingsi; Song, John; Chu, Wei

2015-01-01

The Congruent Matching Cells (CMC) method was invented at the National Institute of Standards and Technology (NIST) for firearm evidence identifications. The CMC method divides the measured image of a surface area, such as a breech face impression from a fired cartridge case, into small correlation cells and uses four identification parameters to identify correlated cell pairs originating from the same firearm. The CMC method was validated by identification tests using both 3D topography images and optical images captured from breech face impressions of 40 cartridge cases fired from a pistol with 10 consecutively manufactured slides. In this paper, we discuss the processing of the cell correlations and propose an improved algorithm of the CMC method which takes advantage of the cell correlations at a common initial phase angle and combines the forward and backward correlations to improve the identification capability. The improved algorithm is tested by 780 pairwise correlations using the same optical images and 3D topography images as the initial validation.

Quantifying cell mono-layer cultures by video imaging.

PubMed

Miller, K S; Hook, L A

1996-04-01

A method is described in which the relative number of adherent cells in multi-well tissue-culture plates is assayed by staining the cells with Giemsa and capturing the image of the stained cells with a video camera and charged-coupled device. The resultant image is quantified using the associated video imaging software. The method is shown to be sensitive and reproducible and should be useful for studies where quantifying relative cell numbers and/or proliferation in vitro is required.

Analysis of gene expression levels in individual bacterial cells without image segmentation.

PubMed

Kwak, In Hae; Son, Minjun; Hagen, Stephen J

2012-05-11

Studies of stochasticity in gene expression typically make use of fluorescent protein reporters, which permit the measurement of expression levels within individual cells by fluorescence microscopy. Analysis of such microscopy images is almost invariably based on a segmentation algorithm, where the image of a cell or cluster is analyzed mathematically to delineate individual cell boundaries. However segmentation can be ineffective for studying bacterial cells or clusters, especially at lower magnification, where outlines of individual cells are poorly resolved. Here we demonstrate an alternative method for analyzing such images without segmentation. The method employs a comparison between the pixel brightness in phase contrast vs fluorescence microscopy images. By fitting the correlation between phase contrast and fluorescence intensity to a physical model, we obtain well-defined estimates for the different levels of gene expression that are present in the cell or cluster. The method reveals the boundaries of the individual cells, even if the source images lack the resolution to show these boundaries clearly. Copyright © 2012 Elsevier Inc. All rights reserved.

Quantitative assessment of image motion blur in diffraction images of moving biological cells

NASA Astrophysics Data System (ADS)

Wang, He; Jin, Changrong; Feng, Yuanming; Qi, Dandan; Sa, Yu; Hu, Xin-Hua

2016-02-01

Motion blur (MB) presents a significant challenge for obtaining high-contrast image data from biological cells with a polarization diffraction imaging flow cytometry (p-DIFC) method. A new p-DIFC experimental system has been developed to evaluate the MB and its effect on image analysis using a time-delay-integration (TDI) CCD camera. Diffraction images of MCF-7 and K562 cells have been acquired with different speed-mismatch ratios and compared to characterize MB quantitatively. Frequency analysis of the diffraction images shows that the degree of MB can be quantified by bandwidth variations of the diffraction images along the motion direction. The analytical results were confirmed by the p-DIFC image data acquired at different speed-mismatch ratios and used to validate a method of numerical simulation of MB on blur-free diffraction images, which provides a useful tool to examine the blurring effect on diffraction images acquired from the same cell. These results provide insights on the dependence of diffraction image on MB and allow significant improvement on rapid biological cell assay with the p-DIFC method.

Quantitative analysis of phosphoinositide 3-kinase (PI3K) signaling using live-cell total internal reflection fluorescence (TIRF) microscopy.

PubMed

Johnson, Heath E; Haugh, Jason M

2013-12-02

This unit focuses on the use of total internal reflection fluorescence (TIRF) microscopy and image analysis methods to study the dynamics of signal transduction mediated by class I phosphoinositide 3-kinases (PI3Ks) in mammalian cells. The first four protocols cover live-cell imaging experiments, image acquisition parameters, and basic image processing and segmentation. These methods are generally applicable to live-cell TIRF experiments. The remaining protocols outline more advanced image analysis methods, which were developed in our laboratory for the purpose of characterizing the spatiotemporal dynamics of PI3K signaling. These methods may be extended to analyze other cellular processes monitored using fluorescent biosensors. Copyright © 2013 John Wiley & Sons, Inc.

Spatial-spectral blood cell classification with microscopic hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ran, Qiong; Chang, Lan; Li, Wei; Xu, Xiaofeng

2017-10-01

Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.

AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques.

PubMed

Khan, Arif Ul Maula; Torelli, Angelo; Wolf, Ivo; Gretz, Norbert

2018-05-08

In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

NASA Astrophysics Data System (ADS)

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution.

PubMed

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-06

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

PubMed Central

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining. PMID:28059147

Automated segmentation of retinal pigment epithelium cells in fluorescence adaptive optics images.

PubMed

Rangel-Fonseca, Piero; Gómez-Vieyra, Armando; Malacara-Hernández, Daniel; Wilson, Mario C; Williams, David R; Rossi, Ethan A

2013-12-01

Adaptive optics (AO) imaging methods allow the histological characteristics of retinal cell mosaics, such as photoreceptors and retinal pigment epithelium (RPE) cells, to be studied in vivo. The high-resolution images obtained with ophthalmic AO imaging devices are rich with information that is difficult and/or tedious to quantify using manual methods. Thus, robust, automated analysis tools that can provide reproducible quantitative information about the cellular mosaics under examination are required. Automated algorithms have been developed to detect the position of individual photoreceptor cells; however, most of these methods are not well suited for characterizing the RPE mosaic. We have developed an algorithm for RPE cell segmentation and show its performance here on simulated and real fluorescence AO images of the RPE mosaic. Algorithm performance was compared to manual cell identification and yielded better than 91% correspondence. This method can be used to segment RPE cells for morphometric analysis of the RPE mosaic and speed the analysis of both healthy and diseased RPE mosaics.

Cell tracking for cell image analysis

NASA Astrophysics Data System (ADS)

Bise, Ryoma; Sato, Yoichi

2017-04-01

Cell image analysis is important for research and discovery in biology and medicine. In this paper, we present our cell tracking methods, which is capable of obtaining fine-grain cell behavior metrics. In order to address difficulties under dense culture conditions, where cell detection cannot be done reliably since cell often touch with blurry intercellular boundaries, we proposed two methods which are global data association and jointly solving cell detection and association. We also show the effectiveness of the proposed methods by applying the method to the biological researches.

High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection

PubMed Central

Choudhry, Priya

2016-01-01

Counting cells and colonies is an integral part of high-throughput screens and quantitative cellular assays. Due to its subjective and time-intensive nature, manual counting has hindered the adoption of cellular assays such as tumor spheroid formation in high-throughput screens. The objective of this study was to develop an automated method for quick and reliable counting of cells and colonies from digital images. For this purpose, I developed an ImageJ macro Cell Colony Edge and a CellProfiler Pipeline Cell Colony Counting, and compared them to other open-source digital methods and manual counts. The ImageJ macro Cell Colony Edge is valuable in counting cells and colonies, and measuring their area, volume, morphology, and intensity. In this study, I demonstrate that Cell Colony Edge is superior to other open-source methods, in speed, accuracy and applicability to diverse cellular assays. It can fulfill the need to automate colony/cell counting in high-throughput screens, colony forming assays, and cellular assays. PMID:26848849

Fired Cartridge Case Identification Using Optical Images and the Congruent Matching Cells (CMC) Method

PubMed Central

Tong, Mingsi; Song, John; Chu, Wei; Thompson, Robert M

2014-01-01

The Congruent Matching Cells (CMC) method for ballistics identification was invented at the National Institute of Standards and Technology (NIST). The CMC method is based on the correlation of pairs of small correlation cells instead of the correlation of entire images. Four identification parameters – TCCF, Tθ, Tx and Ty are proposed for identifying correlated cell pairs originating from the same firearm. The correlation conclusion (matching or non-matching) is determined by whether the number of CMC is ≥ 6. This method has been previously validated using a set of 780 pair-wise 3D topography images. However, most ballistic images stored in current local and national databases are in an optical intensity (grayscale) format. As a result, the reliability of applying the CMC method on optical intensity images is an important issue. In this paper, optical intensity images of breech face impressions captured on the same set of 40 cartridge cases are correlated and analyzed for the validation test of CMC method using optical images. This includes correlations of 63 pairs of matching images and 717 pairs of non-matching images under top ring lighting. Tests of the method do not produce any false identification (false positive) or false exclusion (false negative) results, which support the CMC method and the proposed identification criterion, C = 6, for firearm breech face identifications using optical intensity images. PMID:26601045

Fired Cartridge Case Identification Using Optical Images and the Congruent Matching Cells (CMC) Method.

PubMed

Tong, Mingsi; Song, John; Chu, Wei; Thompson, Robert M

2014-01-01

The Congruent Matching Cells (CMC) method for ballistics identification was invented at the National Institute of Standards and Technology (NIST). The CMC method is based on the correlation of pairs of small correlation cells instead of the correlation of entire images. Four identification parameters - T CCF, T θ, T x and T y are proposed for identifying correlated cell pairs originating from the same firearm. The correlation conclusion (matching or non-matching) is determined by whether the number of CMC is ≥ 6. This method has been previously validated using a set of 780 pair-wise 3D topography images. However, most ballistic images stored in current local and national databases are in an optical intensity (grayscale) format. As a result, the reliability of applying the CMC method on optical intensity images is an important issue. In this paper, optical intensity images of breech face impressions captured on the same set of 40 cartridge cases are correlated and analyzed for the validation test of CMC method using optical images. This includes correlations of 63 pairs of matching images and 717 pairs of non-matching images under top ring lighting. Tests of the method do not produce any false identification (false positive) or false exclusion (false negative) results, which support the CMC method and the proposed identification criterion, C = 6, for firearm breech face identifications using optical intensity images.

In vivo stem cell tracking with imageable nanoparticles that bind bioorthogonal chemical receptors on the stem cell surface.

PubMed

Lee, Sangmin; Yoon, Hwa In; Na, Jin Hee; Jeon, Sangmin; Lim, Seungho; Koo, Heebeom; Han, Sang-Soo; Kang, Sun-Woong; Park, Soon-Jung; Moon, Sung-Hwan; Park, Jae Hyung; Cho, Yong Woo; Kim, Byung-Soo; Kim, Sang Kyoon; Lee, Taekwan; Kim, Dongkyu; Lee, Seulki; Pomper, Martin G; Kwon, Ick Chan; Kim, Kwangmeyung

2017-09-01

It is urgently necessary to develop reliable non-invasive stem cell imaging technology for tracking the in vivo fate of transplanted stem cells in living subjects. Herein, we developed a simple and well controlled stem cell imaging method through a combination of metabolic glycoengineering and bioorthogonal copper-free click chemistry. Firstly, the exogenous chemical receptors containing azide (-N 3 ) groups were generated on the surfaces of stem cells through metabolic glycoengineering using metabolic precursor, tetra-acetylated N-azidoacetyl-d-mannosamine(Ac 4 ManNAz). Next, bicyclo[6.1.0]nonyne-modified glycol chitosan nanoparticles (BCN-CNPs) were prepared as imageable nanoparticles to deliver different imaging agents. Cy5.5, iron oxide nanoparticles and gold nanoparticles were conjugated or encapsulated to BCN-CNPs for optical, MR and CT imaging, respectively. These imageable nanoparticles bound chemical receptors on the Ac 4 ManNAz-treated stem cell surface specifically via bioorthogonal copper-free click chemistry. Then they were rapidly taken up by the cell membrane turn-over mechanism resulting in higher endocytic capacity compared non-specific uptake of nanoparticles. During in vivo animal test, BCN-CNP-Cy5.5-labeled stem cells could be continuously tracked by non-invasive optical imaging over 15 days. Furthermore, BCN-CNP-IRON- and BCN-CNP-GOLD-labeled stem cells could be efficiently visualized using in vivo MR and CT imaging demonstrating utility of our stem cell labeling method using chemical receptors. These results conclude that our method based on metabolic glycoengineering and bioorthogonal copper-free click chemistry can stably label stem cells with diverse imageable nanoparticles representing great potential as new stem cell imaging technology. Copyright © 2017 Elsevier Ltd. All rights reserved.

Imaging and reconstruction of cell cortex structures near the cell surface

NASA Astrophysics Data System (ADS)

Jin, Luhong; Zhou, Xiaoxu; Xiu, Peng; Luo, Wei; Huang, Yujia; Yu, Feng; Kuang, Cuifang; Sun, Yonghong; Liu, Xu; Xu, Yingke

2017-11-01

Total internal reflection fluorescence microscopy (TIRFM) provides high optical sectioning capability and superb signal-to-noise ratio for imaging of cell cortex structures. The development of multi-angle (MA)-TIRFM permits high axial resolution imaging and reconstruction of cellular structures near the cell surface. Cytoskeleton is composed of a network of filaments, which are important for maintenance of cell function. The high-resolution imaging and quantitative analysis of filament organization would contribute to our understanding of cytoskeleton regulation in cell. Here, we used a custom-developed MA-TIRFM setup, together with stochastic photobleaching and single molecule localization method, to enhance the lateral resolution of TIRFM imaging to about 100 nm. In addition, we proposed novel methods to perform filament segmentation and 3D reconstruction from MA-TIRFM images. Furthermore, we applied these methods to study the 3D localization of cortical actin and microtubule structures in U373 cancer cells. Our results showed that cortical actins localize ∼ 27 nm closer to the plasma membrane when compared with microtubules. We found that treatment of cells with chemotherapy drugs nocodazole and cytochalasin B disassembles cytoskeletal network and induces the reorganization of filaments towards the cell periphery. In summary, this study provides feasible approaches for 3D imaging and analyzing cell surface distribution of cytoskeletal network. Our established microscopy platform and image analysis toolkits would facilitate the study of cytoskeletal network in cells.

Semi-automated discrimination of retinal pigmented epithelial cells in two-photon fluorescence images of mouse retinas.

PubMed

Alexander, Nathan S; Palczewska, Grazyna; Palczewski, Krzysztof

2015-08-01

Automated image segmentation is a critical step toward achieving a quantitative evaluation of disease states with imaging techniques. Two-photon fluorescence microscopy (TPM) has been employed to visualize the retinal pigmented epithelium (RPE) and provide images indicating the health of the retina. However, segmentation of RPE cells within TPM images is difficult due to small differences in fluorescence intensity between cell borders and cell bodies. Here we present a semi-automated method for segmenting RPE cells that relies upon multiple weak features that differentiate cell borders from the remaining image. These features were scored by a search optimization procedure that built up the cell border in segments around a nucleus of interest. With six images used as a test, our method correctly identified cell borders for 69% of nuclei on average. Performance was strongly dependent upon increasing retinosome content in the RPE. TPM image analysis has the potential of providing improved early quantitative assessments of diseases affecting the RPE.

Analysis of x-ray tomography data of an extruded low density styrenic foam: an image analysis study

NASA Astrophysics Data System (ADS)

Lin, Jui-Ching; Heeschen, William

2016-10-01

Extruded styrenic foams are low density foams that are widely used for thermal insulation. It is difficult to precisely characterize the structure of the cells in low density foams by traditional cross-section viewing due to the frailty of the walls of the cells. X-ray computed tomography (CT) is a non-destructive, three dimensional structure characterization technique that has great potential for structure characterization of styrenic foams. Unfortunately the intrinsic artifacts of the data and the artifacts generated during image reconstruction are often comparable in size and shape to the thin walls of the foam, making robust and reliable analysis of cell sizes challenging. We explored three different image processing methods to clean up artifacts in the reconstructed images, thus allowing quantitative three dimensional determination of cell size in a low density styrenic foam. Three image processing approaches - an intensity based approach, an intensity variance based approach, and a machine learning based approach - are explored in this study, and the machine learning image feature classification method was shown to be the best. Individual cells are segmented within the images after the images were cleaned up using the three different methods and the cell sizes are measured and compared in the study. Although the collected data with the image analysis methods together did not yield enough measurements for a good statistic of the measurement of cell sizes, the problem can be resolved by measuring multiple samples or increasing imaging field of view.

Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model.

PubMed

Yang, Zhen; Bogovic, John A; Carass, Aaron; Ye, Mao; Searson, Peter C; Prince, Jerry L

2013-03-13

With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in fluorescence images of confluent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2) To deal with different defects in the fluorescence images, the cell junctions are enhanced by applying an order-statistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0.88.

Automated 3-D cell counting method for grading uveitis of rodent eye in vivo with optical coherence tomograph.

PubMed

Choi, Woo June; Pepple, Kathryn L; Wang, Ruikang K

2018-05-24

In preclinical vision research, cell grading in small animal models is essential for the quantitative evaluation of intraocular inflammation. Here, we present a new and practical optical coherence tomography (OCT) image analysis method for the automated detection and counting of aqueous cells in the anterior chamber (AC) of a rodent model of uveitis. Anterior segment OCT (AS-OCT) images are acquired with a 100kHz swept-source OCT (SS-OCT) system. The proposed method consists of two steps. In the first step, we first despeckle and binarize each OCT image. After removing AS structures in the binary image, we then apply area thresholding to isolate cell-like objects. Potential cell candidates are selected based on their best fit to roundness. The second step performs the cell counting within the whole AC, in which additional cell tracking analysis is conducted on the successive OCT images to eliminate redundancy in cell counting. Finally, 3-D cell grading using the proposed method is demonstrated in longitudinal OCT imaging of a mouse model of anterior uveitis in vivo. Rendering of anterior segment (orange) of mouse eye and automatically counted anterior chamber cells (green). Inset is a top view of the rendering, showing the cell distribution across the anterior chamber. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Univariate and multivariate methods for chemical mapping of cervical cancer cells

NASA Astrophysics Data System (ADS)

Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2012-01-01

Visualization of cells and subcellular organelles are currently carried out using available microscopy methods such as cryoelectron microscopy, and fluorescence microscopy. These methods require external labeling using fluorescent dyes and extensive sample preparations to access the subcellular structures. However, Raman micro-spectroscopy provides a non-invasive, label-free method for imaging the cells with chemical specificity at sub-micrometer spatial resolutions. The scope of this paper is to image the biochemical/molecular distributions in cells associated with cancerous changes. Raman map data sets were acquired from the human cervical carcinoma cell lines (HeLa) after fixation under 785 nm excitation wavelength. The individual spectrum was recorded by raster-scanning the laser beam over the sample with 1μm step size and 10s exposure time. Images revealing nucleic acids, lipids and proteins (phenylalanine, amide I) were reconstructed using univariate methods. In near future, the small pixel to pixel variations will also be imaged using different multivariate methods (PCA, clustering (HCA, K-means, FCM)) to determine the main cellular constitutions. The hyper-spectral image of cell was reconstructed utilizing the spectral contrast at different pixels of the cell (due to the variation in the biochemical distribution) without using fluorescent dyes. Normal cervical squamous cells will also be imaged in order to differentiate normal and cancer cells of cervix using the biochemical changes in different grades of cancer. Based on the information obtained from the pseudo-color maps, constructed from the hyper-spectral cubes, the primary cellular constituents of normal and cervical cancer cells were identified.

Cell surface and cell outline imaging in plant tissues using the backscattered electron detector in a variable pressure scanning electron microscope

PubMed Central

2013-01-01

Background Scanning electron microscopy (SEM) has been used for high-resolution imaging of plant cell surfaces for many decades. Most SEM imaging employs the secondary electron detector under high vacuum to provide pseudo-3D images of plant organs and especially of surface structures such as trichomes and stomatal guard cells; these samples generally have to be metal-coated to avoid charging artefacts. Variable pressure-SEM allows examination of uncoated tissues, and provides a flexible range of options for imaging, either with a secondary electron detector or backscattered electron detector. In one application, we used the backscattered electron detector under low vacuum conditions to collect images of uncoated barley leaf tissue followed by simple quantification of cell areas. Results Here, we outline methods for backscattered electron imaging of a variety of plant tissues with particular focus on collecting images for quantification of cell size and shape. We demonstrate the advantages of this technique over other methods to obtain high contrast cell outlines, and define a set of parameters for imaging Arabidopsis thaliana leaf epidermal cells together with a simple image analysis protocol. We also show how to vary parameters such as accelerating voltage and chamber pressure to optimise imaging in a range of other plant tissues. Conclusions Backscattered electron imaging of uncoated plant tissue allows acquisition of images showing details of plant morphology together with images of high contrast cell outlines suitable for semi-automated image analysis. The method is easily adaptable to many types of tissue and suitable for any laboratory with standard SEM preparation equipment and a variable-pressure-SEM or tabletop SEM. PMID:24135233

Comparison of parameter-adapted segmentation methods for fluorescence micrographs.

PubMed

Held, Christian; Palmisano, Ralf; Häberle, Lothar; Hensel, Michael; Wittenberg, Thomas

2011-11-01

Interpreting images from fluorescence microscopy is often a time-consuming task with poor reproducibility. Various image processing routines that can help investigators evaluate the images are therefore useful. The critical aspect for a reliable automatic image analysis system is a robust segmentation algorithm that can perform accurate segmentation for different cell types. In this study, several image segmentation methods were therefore compared and evaluated in order to identify the most appropriate segmentation schemes that are usable with little new parameterization and robustly with different types of fluorescence-stained cells for various biological and biomedical tasks. The study investigated, compared, and enhanced four different methods for segmentation of cultured epithelial cells. The maximum-intensity linking (MIL) method, an improved MIL, a watershed method, and an improved watershed method based on morphological reconstruction were used. Three manually annotated datasets consisting of 261, 817, and 1,333 HeLa or L929 cells were used to compare the different algorithms. The comparisons and evaluations showed that the segmentation performance of methods based on the watershed transform was significantly superior to the performance of the MIL method. The results also indicate that using morphological opening by reconstruction can improve the segmentation of cells stained with a marker that exhibits the dotted surface of cells. Copyright © 2011 International Society for Advancement of Cytometry.

Spatial Statistics for Tumor Cell Counting and Classification

NASA Astrophysics Data System (ADS)

Wirjadi, Oliver; Kim, Yoo-Jin; Breuel, Thomas

To count and classify cells in histological sections is a standard task in histology. One example is the grading of meningiomas, benign tumors of the meninges, which requires to assess the fraction of proliferating cells in an image. As this process is very time consuming when performed manually, automation is required. To address such problems, we propose a novel application of Markov point process methods in computer vision, leading to algorithms for computing the locations of circular objects in images. In contrast to previous algorithms using such spatial statistics methods in image analysis, the present one is fully trainable. This is achieved by combining point process methods with statistical classifiers. Using simulated data, the method proposed in this paper will be shown to be more accurate and more robust to noise than standard image processing methods. On the publicly available SIMCEP benchmark for cell image analysis algorithms, the cell count performance of the present paper is significantly more accurate than results published elsewhere, especially when cells form dense clusters. Furthermore, the proposed system performs as well as a state-of-the-art algorithm for the computer-aided histological grading of meningiomas when combined with a simple k-nearest neighbor classifier for identifying proliferating cells.

Long-term Live-cell Imaging to Assess Cell Fate in Response to Paclitaxel.

PubMed

Bolgioni, Amanda F; Vittoria, Marc A; Ganem, Neil J

2018-05-14

Live-cell imaging is a powerful technique that can be used to directly visualize biological phenomena in single cells over extended periods of time. Over the past decade, new and innovative technologies have greatly enhanced the practicality of live-cell imaging. Cells can now be kept in focus and continuously imaged over several days while maintained under 37 °C and 5% CO2 cell culture conditions. Moreover, multiple fields of view representing different experimental conditions can be acquired simultaneously, thus providing high-throughput experimental data. Live-cell imaging provides a significant advantage over fixed-cell imaging by allowing for the direct visualization and temporal quantitation of dynamic cellular events. Live-cell imaging can also identify variation in the behavior of single cells that would otherwise have been missed using population-based assays. Here, we describe live-cell imaging protocols to assess cell fate decisions following treatment with the anti-mitotic drug paclitaxel. We demonstrate methods to visualize whether mitotically arrested cells die directly from mitosis or slip back into interphase. We also describe how the fluorescent ubiquitination-based cell cycle indicator (FUCCI) system can be used to assess the fraction of interphase cells born from mitotic slippage that are capable of re-entering the cell cycle. Finally, we describe a live-cell imaging method to identify nuclear envelope rupture events.

Comparison of corneal endothelial image analysis by Konan SP8000 noncontact and Bio-Optics Bambi systems.

PubMed

Benetz, B A; Diaconu, E; Bowlin, S J; Oak, S S; Laing, R A; Lass, J H

1999-01-01

Compare corneal endothelial image analysis by Konan SP8000 and Bio-Optics Bambi image-analysis systems. Corneal endothelial images from 98 individuals (191 eyes), ranging in age from 4 to 87 years, with a normal slit-lamp examination and no history of ocular trauma, intraocular surgery, or intraocular inflammation were obtained by the Konan SP8000 noncontact specular microscope. One observer analyzed these images by using the Konan system and a second observer by using the Bio-Optics Bambi system. Three methods of analyses were used: a fixed-frame method to obtain cell density (for both Konan and Bio-Optics Bambi) and a "dot" (Konan) or "corners" (Bio-Optics Bambi) method to determine morphometric parameters. The cell density determined by the Konan fixed-frame method was significantly higher (157 cells/mm2) than the Bio-Optics Bambi fixed-frame method determination (p<0.0001). However, the difference in cell density, although still statistically significant, was smaller and reversed comparing the Konan fixed-frame method with both Konan dot and Bio-Optics Bambi comers method (-74 cells/mm2, p<0.0001; -55 cells/mm2, p<0.0001, respectively). Small but statistically significant morphometric analyses differences between Konan and Bio-Optics Bambi were seen: cell density, +19 cells/mm2 (p = 0.03); cell area, -3.0 microm2 (p = 0.008); and coefficient of variation, +1.0 (p = 0.003). There was no statistically significant difference between these two methods in the percentage of six-sided cells detected (p = 0.55). Cell densities measured by the Konan fixed-frame method were comparable with Konan and Bio-Optics Bambi's morphometric analysis, but not with the Bio-Optics Bambi fixed-frame method. The two morphometric analyses were comparable with minimal or no differences for the parameters that were studied. The Konan SP8000 endothelial image-analysis system may be useful for large-scale clinical trials determining cell loss; its noncontact system has many clinical benefits (including patient comfort, safety, ease of use, and short procedure time) and provides reliable cell-density calculations.

Cell nuclei segmentation in fluorescence microscopy images using inter- and intra-region discriminative information.

PubMed

Song, Yang; Cai, Weidong; Feng, David Dagan; Chen, Mei

2013-01-01

Automated segmentation of cell nuclei in microscopic images is critical to high throughput analysis of the ever increasing amount of data. Although cell nuclei are generally visually distinguishable for human, automated segmentation faces challenges when there is significant intensity inhomogeneity among cell nuclei or in the background. In this paper, we propose an effective method for automated cell nucleus segmentation using a three-step approach. It first obtains an initial segmentation by extracting salient regions in the image, then reduces false positives using inter-region feature discrimination, and finally refines the boundary of the cell nuclei using intra-region contrast information. This method has been evaluated on two publicly available datasets of fluorescence microscopic images with 4009 cells, and has achieved superior performance compared to popular state of the art methods using established metrics.

Neutron imaging integrated circuit and method for detecting neutrons

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

Nagarkar, Vivek V.; More, Mitali J.

The present disclosure provides a neutron imaging detector and a method for detecting neutrons. In one example, a method includes providing a neutron imaging detector including plurality of memory cells and a conversion layer on the memory cells, setting one or more of the memory cells to a first charge state, positioning the neutron imaging detector in a neutron environment for a predetermined time period, and reading a state change at one of the memory cells, and measuring a charge state change at one of the plurality of memory cells from the first charge state to a second charge statemore » less than the first charge state, where the charge state change indicates detection of neutrons at said one of the memory cells.« less

Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-imaging

PubMed Central

Wuttisarnwattana, Patiwet; Gargesha, Madhusudhana; Hof, Wouter van’t; Cooke, Kenneth R.

2016-01-01

With its single cell sensitivity over volumes as large as or larger than a mouse, cryo-imaging enables imaging of stem cell biodistribution, homing, engraftment, and molecular mechanisms. We developed and evaluated a highly automated software tool to detect fluorescently labeled stem cells within very large (~200GB) cryo-imaging datasets. Cell detection steps are: preprocess, remove immaterial regions, spatially filter to create features, identify candidate pixels, classify pixels using bagging decision trees, segment cell patches, and perform 3D labeling. There are options for analysis and visualization. To train the classifier, we created synthetic images by placing realistic digital cell models onto cryo-images of control mice devoid of cells. Very good cell detection results were (precision=98.49%, recall=99.97%) for synthetic cryo-images, (precision=97.81%, recall=97.71%) for manually evaluated, actual cryo-images, and <1% false positives in control mice. An α-multiplier applied to features allows one to correct for experimental variations in cell brightness due to labeling. On dim cells (37% of standard brightness), with correction, we improved recall (49.26%→99.36%) without a significant drop in precision (99.99%→99.75%). With tail vein injection, multipotent adult progenitor cells in a graft-versus-host-disease model in the first days post injection were predominantly found in lung, liver, spleen, and bone marrow. Distribution was not simply related to blood flow. The lung contained clusters of cells while other tissues contained single cells. Our methods provided stem cell distribution anywhere in mouse with single cell sensitivity. Methods should provide a rational means of evaluating dosing, delivery methods, cell enhancements, and mechanisms for therapeutic cells. PMID:26552080

Contrast enhancing solution for use in confocal microscopy

DOEpatents

Tannous, Zeina; Torres, Abel; Gonzalez, Salvador

2006-10-31

A method of optically detecting a tumor during surgery. The method includes imaging at least one test point defined on the tumor using a first optical imaging system to provide a first tumor image. The method further includes excising a first predetermined layer of the tumor for forming an in-vivo defect area. A predetermined contrast enhancing solution is disposed on the in-vivo defect area, which is adapted to interact with at least one cell anomaly, such as basal cell carcinoma, located on the in-vivo defect area for optically enhancing the cell anomaly. Thereafter the defect area can be optically imaged to provide a clear and bright representation of the cell anomaly to aid a surgeon while surgically removing the cell anomaly.

High-Throughput Histopathological Image Analysis via Robust Cell Segmentation and Hashing

PubMed Central

Zhang, Xiaofan; Xing, Fuyong; Su, Hai; Yang, Lin; Zhang, Shaoting

2015-01-01

Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells. PMID:26599156

Empirical gradient threshold technique for automated segmentation across image modalities and cell lines.

PubMed

Chalfoun, J; Majurski, M; Peskin, A; Breen, C; Bajcsy, P; Brady, M

2015-10-01

New microscopy technologies are enabling image acquisition of terabyte-sized data sets consisting of hundreds of thousands of images. In order to retrieve and analyze the biological information in these large data sets, segmentation is needed to detect the regions containing cells or cell colonies. Our work with hundreds of large images (each 21,000×21,000 pixels) requires a segmentation method that: (1) yields high segmentation accuracy, (2) is applicable to multiple cell lines with various densities of cells and cell colonies, and several imaging modalities, (3) can process large data sets in a timely manner, (4) has a low memory footprint and (5) has a small number of user-set parameters that do not require adjustment during the segmentation of large image sets. None of the currently available segmentation methods meet all these requirements. Segmentation based on image gradient thresholding is fast and has a low memory footprint. However, existing techniques that automate the selection of the gradient image threshold do not work across image modalities, multiple cell lines, and a wide range of foreground/background densities (requirement 2) and all failed the requirement for robust parameters that do not require re-adjustment with time (requirement 5). We present a novel and empirically derived image gradient threshold selection method for separating foreground and background pixels in an image that meets all the requirements listed above. We quantify the difference between our approach and existing ones in terms of accuracy, execution speed, memory usage and number of adjustable parameters on a reference data set. This reference data set consists of 501 validation images with manually determined segmentations and image sizes ranging from 0.36 Megapixels to 850 Megapixels. It includes four different cell lines and two image modalities: phase contrast and fluorescent. Our new technique, called Empirical Gradient Threshold (EGT), is derived from this reference data set with a 10-fold cross-validation method. EGT segments cells or colonies with resulting Dice accuracy index measurements above 0.92 for all cross-validation data sets. EGT results has also been visually verified on a much larger data set that includes bright field and Differential Interference Contrast (DIC) images, 16 cell lines and 61 time-sequence data sets, for a total of 17,479 images. This method is implemented as an open-source plugin to ImageJ as well as a standalone executable that can be downloaded from the following link: https://isg.nist.gov/. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy.

PubMed

Grah, Joana Sarah; Harrington, Jennifer Alison; Koh, Siang Boon; Pike, Jeremy Andrew; Schreiner, Alexander; Burger, Martin; Schönlieb, Carola-Bibiane; Reichelt, Stefanie

2017-02-15

In this paper we propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB®Graphical User Interface MitosisAnalyser. Copyright © 2017. Published by Elsevier Inc.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

PubMed

Van Valen, David A; Kudo, Takamasa; Lane, Keara M; Macklin, Derek N; Quach, Nicolas T; DeFelice, Mialy M; Maayan, Inbal; Tanouchi, Yu; Ashley, Euan A; Covert, Markus W

2016-11-01

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

DOE PAGES

Van Valen, David A.; Kudo, Takamasa; Lane, Keara M.; ...

2016-11-04

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domainsmore » of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.« less

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

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

Van Valen, David A.; Kudo, Takamasa; Lane, Keara M.

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domainsmore » of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.« less

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

PubMed Central

Van Valen, David A.; Lane, Keara M.; Quach, Nicolas T.; Maayan, Inbal

2016-01-01

Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems. PMID:27814364

Rapid enumeration of viable bacteria by image analysis

NASA Technical Reports Server (NTRS)

Singh, A.; Pyle, B. H.; McFeters, G. A.

1989-01-01

A direct viable counting method for enumerating viable bacteria was modified and made compatible with image analysis. A comparison was made between viable cell counts determined by the spread plate method and direct viable counts obtained using epifluorescence microscopy either manually or by automatic image analysis. Cultures of Escherichia coli, Salmonella typhimurium, Vibrio cholerae, Yersinia enterocolitica and Pseudomonas aeruginosa were incubated at 35 degrees C in a dilute nutrient medium containing nalidixic acid. Filtered samples were stained for epifluorescence microscopy and analysed manually as well as by image analysis. Cells enlarged after incubation were considered viable. The viable cell counts determined using image analysis were higher than those obtained by either the direct manual count of viable cells or spread plate methods. The volume of sample filtered or the number of cells in the original sample did not influence the efficiency of the method. However, the optimal concentration of nalidixic acid (2.5-20 micrograms ml-1) and length of incubation (4-8 h) varied with the culture tested. The results of this study showed that under optimal conditions, the modification of the direct viable count method in combination with image analysis microscopy provided an efficient and quantitative technique for counting viable bacteria in a short time.

A fiber-optic-based imaging system for nondestructive assessment of cell-seeded tissue-engineered scaffolds.

PubMed

Hofmann, Matthias C; Whited, Bryce M; Criswell, Tracy; Rylander, Marissa Nichole; Rylander, Christopher G; Soker, Shay; Wang, Ge; Xu, Yong

2012-09-01

A major limitation in tissue engineering is the lack of nondestructive methods that assess the development of tissue scaffolds undergoing preconditioning in bioreactors. Due to significant optical scattering in most scaffolding materials, current microscope-based imaging methods cannot "see" through thick and optically opaque tissue constructs. To address this deficiency, we developed a fiber-optic-based imaging method that is capable of nondestructive imaging of fluorescently labeled cells through a thick and optically opaque scaffold, contained in a bioreactor. This imaging modality is based on the local excitation of fluorescent cells, the acquisition of fluorescence through the scaffold, and fluorescence mapping based on the position of the excitation light. To evaluate the capability and accuracy of the imaging system, human endothelial cells (ECs), stably expressing green fluorescent protein (GFP), were imaged through a fibrous scaffold. Without sacrificing the scaffolds, we nondestructively visualized the distribution of GFP-labeled cells through a ~500 μm thick scaffold with cell-level resolution and distinct localization. These results were similar to control images obtained using an optical microscope with direct line-of-sight access. Through a detailed quantitative analysis, we demonstrated that this method achieved a resolution on the order of 20-30 μm, with 10% or less deviation from standard optical microscopy. Furthermore, we demonstrated that the penetration depth of the imaging method exceeded that of confocal laser scanning microscopy by more than a factor of 2. Our imaging method also possesses a working distance (up to 8 cm) much longer than that of a standard confocal microscopy system, which can significantly facilitate bioreactor integration. This method will enable the nondestructive monitoring of ECs seeded on the lumen of a tissue-engineered vascular graft during preconditioning in vitro, as well as for other tissue-engineered constructs in the future.

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Liu, Xi; Zhou, Mei; Qiu, Song; Sun, Li; Liu, Hongying; Li, Qingli; Wang, Yiting

2017-12-01

Red blood cell counting, as a routine examination, plays an important role in medical diagnoses. Although automated hematology analyzers are widely used, manual microscopic examination by a hematologist or pathologist is still unavoidable, which is time-consuming and error-prone. This paper proposes a full-automatic red blood cell counting method which is based on microscopic hyperspectral imaging of blood smears and combines spatial and spectral information to achieve high precision. The acquired hyperspectral image data of the blood smear in the visible and near-infrared spectral range are firstly preprocessed, and then a quadratic blind linear unmixing algorithm is used to get endmember abundance images. Based on mathematical morphological operation and an adaptive Otsu’s method, a binaryzation process is performed on the abundance images. Finally, the connected component labeling algorithm with magnification-based parameter setting is applied to automatically select the binary images of red blood cell cytoplasm. Experimental results show that the proposed method can perform well and has potential for clinical applications.

Live-cell imaging of endogenous mRNAs with a small molecule.

PubMed

Sato, Shin-ichi; Watanabe, Mizuki; Katsuda, Yousuke; Murata, Asako; Wang, Dan Ohtan; Uesugi, Motonari

2015-02-02

Determination of subcellular localization and dynamics of mRNA is increasingly important to understanding gene expression. A new convenient and versatile method is reported that permits spatiotemporal imaging of specific non-engineered RNAs in living cells. The method uses transfection of a plasmid encoding a gene-specific RNA aptamer, combined with a cell-permeable synthetic small molecule, the fluorescence of which is restored only when the RNA aptamer hybridizes with its cognitive mRNA. The method was validated by live-cell imaging of the endogenous mRNA of β-actin. Application of the technology to mRNAs of a total of 84 human cytoskeletal genes allowed us to observe cellular dynamics of several endogenous mRNAs including arfaptin-2, cortactin, and cytoplasmic FMR1-interacting protein 2. The RNA-imaging technology and its further optimization might permit live-cell imaging of any RNA molecules. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Dual photon excitation microscopy and image threshold segmentation in live cell imaging during compression testing.

PubMed

Moo, Eng Kuan; Abusara, Ziad; Abu Osman, Noor Azuan; Pingguan-Murphy, Belinda; Herzog, Walter

2013-08-09

Morphological studies of live connective tissue cells are imperative to helping understand cellular responses to mechanical stimuli. However, photobleaching is a constant problem to accurate and reliable live cell fluorescent imaging, and various image thresholding methods have been adopted to account for photobleaching effects. Previous studies showed that dual photon excitation (DPE) techniques are superior over conventional one photon excitation (OPE) confocal techniques in minimizing photobleaching. In this study, we investigated the effects of photobleaching resulting from OPE and DPE on morphology of in situ articular cartilage chondrocytes across repeat laser exposures. Additionally, we compared the effectiveness of three commonly-used image thresholding methods in accounting for photobleaching effects, with and without tissue loading through compression. In general, photobleaching leads to an apparent volume reduction for subsequent image scans. Performing seven consecutive scans of chondrocytes in unloaded cartilage, we found that the apparent cell volume loss caused by DPE microscopy is much smaller than that observed using OPE microscopy. Applying scan-specific image thresholds did not prevent the photobleaching-induced volume loss, and volume reductions were non-uniform over the seven repeat scans. During cartilage loading through compression, cell fluorescence increased and, depending on the thresholding method used, led to different volume changes. Therefore, different conclusions on cell volume changes may be drawn during tissue compression, depending on the image thresholding methods used. In conclusion, our findings confirm that photobleaching directly affects cell morphology measurements, and that DPE causes less photobleaching artifacts than OPE for uncompressed cells. When cells are compressed during tissue loading, a complicated interplay between photobleaching effects and compression-induced fluorescence increase may lead to interpretations in cell responses to mechanical stimuli that depend on the microscopic approach and the thresholding methods used and may result in contradictory interpretations. Copyright © 2013 Elsevier Ltd. All rights reserved.

Stem Cell Monitoring with a Direct or Indirect Labeling Method.

PubMed

Kim, Min Hwan; Lee, Yong Jin; Kang, Joo Hyun

2016-12-01

The molecular imaging techniques allow monitoring of the transplanted cells in the same individuals over time, from early localization to the survival, migration, and differentiation. Generally, there are two methods of stem cell labeling: direct and indirect labeling methods. The direct labeling method introduces a labeling agent into the cell, which is stably incorporated or attached to the cells prior to transplantation. Direct labeling of cells with radionuclides is a simple method with relatively fewer adverse events related to genetic responses. However, it can only allow short-term distribution of transplanted cells because of the decreasing imaging signal with radiodecay, according to the physical half-lives, or the signal becomes more diffuse with cell division and dispersion. The indirect labeling method is based on the expression of a reporter gene transduced into the cell before transplantation, which is then visualized upon the injection of an appropriate probe or substrate. In this review, various imaging strategies to monitor the survival and behavior change of transplanted stem cells are covered. Taking these new approaches together, the direct and indirect labeling methods may provide new insights on the roles of in vivo stem cell monitoring, from bench to bedside.

Using Cell-ID 1.4 with R for Microscope-Based Cytometry

PubMed Central

Bush, Alan; Chernomoretz, Ariel; Yu, Richard; Gordon, Andrew

2012-01-01

This unit describes a method for quantifying various cellular features (e.g., volume, total and subcellular fluorescence localization) from sets of microscope images of individual cells. It includes procedures for tracking cells over time. One purposefully defocused transmission image (sometimes referred to as bright-field or BF) is acquired to segment the image and locate each cell. Fluorescent images (one for each of the color channels to be analyzed) are then acquired by conventional wide-field epifluorescence or confocal microscopy. This method uses the image processing capabilities of Cell-ID (Gordon et al., 2007, as updated here) and data analysis by the statistical programming framework R (R-Development-Team, 2008), which we have supplemented with a package of routines for analyzing Cell-ID output. Both Cell-ID and the analysis package are open-source. PMID:23026908

Quantitative Image Restoration in Bright Field Optical Microscopy.

PubMed

Gutiérrez-Medina, Braulio; Sánchez Miranda, Manuel de Jesús

2017-11-07

Bright field (BF) optical microscopy is regarded as a poor method to observe unstained biological samples due to intrinsic low image contrast. We introduce quantitative image restoration in bright field (QRBF), a digital image processing method that restores out-of-focus BF images of unstained cells. Our procedure is based on deconvolution, using a point spread function modeled from theory. By comparing with reference images of bacteria observed in fluorescence, we show that QRBF faithfully recovers shape and enables quantify size of individual cells, even from a single input image. We applied QRBF in a high-throughput image cytometer to assess shape changes in Escherichia coli during hyperosmotic shock, finding size heterogeneity. We demonstrate that QRBF is also applicable to eukaryotic cells (yeast). Altogether, digital restoration emerges as a straightforward alternative to methods designed to generate contrast in BF imaging for quantitative analysis. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening.

PubMed

Chen, C; Li, H; Zhou, X; Wong, S T C

2008-05-01

Image-based, high throughput genome-wide RNA interference (RNAi) experiments are increasingly carried out to facilitate the understanding of gene functions in intricate biological processes. Automated screening of such experiments generates a large number of images with great variations in image quality, which makes manual analysis unreasonably time-consuming. Therefore, effective techniques for automatic image analysis are urgently needed, in which segmentation is one of the most important steps. This paper proposes a fully automatic method for cells segmentation in genome-wide RNAi screening images. The method consists of two steps: nuclei and cytoplasm segmentation. Nuclei are extracted and labelled to initialize cytoplasm segmentation. Since the quality of RNAi image is rather poor, a novel scale-adaptive steerable filter is designed to enhance the image in order to extract long and thin protrusions on the spiky cells. Then, constraint factor GCBAC method and morphological algorithms are combined to be an integrated method to segment tight clustered cells. Compared with the results obtained by using seeded watershed and the ground truth, that is, manual labelling results by experts in RNAi screening data, our method achieves higher accuracy. Compared with active contour methods, our method consumes much less time. The positive results indicate that the proposed method can be applied in automatic image analysis of multi-channel image screening data.

HEp-2 cell image classification method based on very deep convolutional networks with small datasets

NASA Astrophysics Data System (ADS)

Lu, Mengchi; Gao, Long; Guo, Xifeng; Liu, Qiang; Yin, Jianping

2017-07-01

Human Epithelial-2 (HEp-2) cell images staining patterns classification have been widely used to identify autoimmune diseases by the anti-Nuclear antibodies (ANA) test in the Indirect Immunofluorescence (IIF) protocol. Because manual test is time consuming, subjective and labor intensive, image-based Computer Aided Diagnosis (CAD) systems for HEp-2 cell classification are developing. However, methods proposed recently are mostly manual features extraction with low accuracy. Besides, the scale of available benchmark datasets is small, which does not exactly suitable for using deep learning methods. This issue will influence the accuracy of cell classification directly even after data augmentation. To address these issues, this paper presents a high accuracy automatic HEp-2 cell classification method with small datasets, by utilizing very deep convolutional networks (VGGNet). Specifically, the proposed method consists of three main phases, namely image preprocessing, feature extraction and classification. Moreover, an improved VGGNet is presented to address the challenges of small-scale datasets. Experimental results over two benchmark datasets demonstrate that the proposed method achieves superior performance in terms of accuracy compared with existing methods.

High resolution resonance ionization imaging detector and method

DOEpatents

Winefordner, James D.; Matveev, Oleg I.; Smith, Benjamin W.

1999-01-01

A resonance ionization imaging device (RIID) and method for imaging objects using the RIID are provided, the RIID system including a RIID cell containing an ionizable vapor including monoisotopic atoms or molecules, the cell being positioned to intercept scattered radiation of a resonance wavelength .lambda..sub.1 from the object which is to be detected or imaged, a laser source disposed to illuminate the RIID cell with laser radiation having a wavelength .lambda..sub.2 or wavelengths .lambda..sub.2, .lambda..sub.3 selected to ionize atoms in the cell that are in an excited state by virtue of having absorbed the scattered resonance laser radiation, and a luminescent screen at the back surface of the RIID cell which presents an image of the number and position of charged particles present in the RIID cell as a result of the ionization of the excited state atoms. The method of the invention further includes the step of initially illuminating the object to be detected or imaged with a laser having a wavelength selected such that the object will scatter laser radiation having the resonance wavelength .lambda..sub.1.

Live CLEM imaging to analyze nuclear structures at high resolution.

PubMed

Haraguchi, Tokuko; Osakada, Hiroko; Koujin, Takako

2015-01-01

Fluorescence microscopy (FM) and electron microscopy (EM) are powerful tools for observing molecular components in cells. FM can provide temporal information about cellular proteins and structures in living cells. EM provides nanometer resolution images of cellular structures in fixed cells. We have combined FM and EM to develop a new method of correlative light and electron microscopy (CLEM), called "Live CLEM." In this method, the dynamic behavior of specific molecules of interest is first observed in living cells using fluorescence microscopy (FM) and then cellular structures in the same cell are observed using electron microscopy (EM). Following image acquisition, FM and EM images are compared to enable the fluorescent images to be correlated with the high-resolution images of cellular structures obtained using EM. As this method enables analysis of dynamic events involving specific molecules of interest in the context of specific cellular structures at high resolution, it is useful for the study of nuclear structures including nuclear bodies. Here we describe Live CLEM that can be applied to the study of nuclear structures in mammalian cells.

Imaging Stem Cells Implanted in Infarcted Myocardium

PubMed Central

Zhou, Rong; Acton, Paul D.; Ferrari, Victor A.

2008-01-01

Stem cell–based cellular cardiomyoplasty represents a promising therapy for myocardial infarction. Noninvasive imaging techniques would allow the evaluation of survival, migration, and differentiation status of implanted stem cells in the same subject over time. This review describes methods for cell visualization using several corresponding noninvasive imaging modalities, including magnetic resonance imaging, positron emission tomography, single-photon emission computed tomography, and bioluminescent imaging. Reporter-based cell visualization is compared with direct cell labeling for short- and long-term cell tracking. PMID:17112999

Development of tyrosinase-based reporter genes for preclinical photoacoustic imaging of mesenchymal stem cells

NASA Astrophysics Data System (ADS)

Märk, Julia; Ruschke, Karen; Dortay, Hakan; Schreiber, Isabelle; Sass, Andrea; Qazi, Taimoor; Pumberger, Matthias; Laufer, Jan

2014-03-01

The capability to image stem cells in vivo in small animal models over extended periods of time is important to furthering our understanding of the processes involved in tissue regeneration. Photoacoustic imaging is suited to this application as it can provide high resolution (tens of microns) absorption-based images of superficial tissues (cm depths). However, stem cells are rare, highly migratory, and can divide into more specialised cells. Genetic labelling strategies are therefore advantageous for their visualisation. In this study, methods for the transfection and viral transduction of mesenchymal stem cells with reporter genes for the co-expression of tyrosinase and a fluorescent protein (mCherry). Initial photoacoustic imaging experiments of tyrosinase expressing cells in small animal models of tissue regeneration were also conducted. Lentiviral transduction methods were shown to result in stable expression of tyrosinase and mCherry in mesenchymal stem cells. The results suggest that photoacoustic imaging using reporter genes is suitable for the study of stem cell driven tissue regeneration in small animals.

Ex vivo Live Imaging of Lung Metastasis and Their Microenvironment

PubMed Central

Maynard, Carrie; Plaks, Vicki

2016-01-01

Metastasis is a major cause for cancer-related morbidity and mortality. Metastasis is a multistep process and due to its complexity, the exact cellular and molecular processes that govern metastatic dissemination and growth are still elusive. Live imaging allows visualization of the dynamic and spatial interactions of cells and their microenvironment. Solid tumors commonly metastasize to the lungs. However, the anatomical location of the lungs poses a challenge to intravital imaging. This protocol provides a relatively simple and quick method for ex vivo live imaging of the dynamic interactions between tumor cells and their surrounding stroma within lung metastasis. Using this method, the motility of cancer cells as well as interactions between cancer cells and stromal cells in their microenvironment can be visualized in real time for several hours. By using transgenic fluorescent reporter mice, a fluorescent cell line, injectable fluorescently labeled molecules and/or antibodies, multiple components of the lung microenvironment can be visualized, such as blood vessels and immune cells. To image the different cell types, a spinning disk confocal microscope that allows long-term continuous imaging with rapid, four-color image acquisition has been used. Time-lapse movies compiled from images collected over multiple positions and focal planes show interactions between live metastatic and immune cells for at least 4 hr. This technique can be further used to test chemotherapy or targeted therapy. Moreover, this method could be adapted for the study of other lung-related pathologies that may affect the lung microenvironment. PMID:26862704

A convergence algorithm for correlation of breech face images based on the congruent matching cells (CMC) method.

PubMed

Chen, Zhe; Song, John; Chu, Wei; Soons, Johannes A; Zhao, Xuezeng

2017-11-01

The Congruent Matching Cells (CMC) method was invented at the National Institute of Standards and Technology (NIST) for accurate firearm evidence identification and error rate estimation. The CMC method is based on the principle of discretization. The toolmark image of the reference sample is divided into correlation cells. Each cell is registered to the cell-sized area of the compared image that has maximum surface topography similarity. For each resulting cell pair, one parameter quantifies the similarity of the cell surface topography and three parameters quantify the pattern congruency of the registration position and orientation. An identification (declared match) requires a significant number of CMCs, that is, cell pairs that meet both similarity and pattern congruency requirements. The use of cell correlations reduces the effects of "invalid regions" in the compared image pairs and increases the correlation accuracy. The identification accuracy of the CMC method can be further improved by considering a feature named "convergence," that is, the tendency of the x-y registration positions of the correlated cell pairs to converge at the correct registration angle when comparing same-source samples at different relative orientations. In this paper, the difference of the convergence feature between known matching (KM) and known non-matching (KNM) image pairs is characterized, based on which an improved algorithm is developed for breech face image correlations using the CMC method. Its advantage is demonstrated by comparison with three existing CMC algorithms using four datasets. The datasets address three different brands of consecutively manufactured pistol slides, with significant differences in the distribution overlap of cell pair topography similarity for KM and KNM image pairs. For the same CMC threshold values, the convergence algorithm demonstrates noticeably improved results by reducing the number of false-positive or false-negative CMCs in a comparison. Published by Elsevier B.V.

Content-based cell pathology image retrieval by combining different features

NASA Astrophysics Data System (ADS)

Zhou, Guangquan; Jiang, Lu; Luo, Limin; Bao, Xudong; Shu, Huazhong

2004-04-01

Content Based Color Cell Pathology Image Retrieval is one of the newest computer image processing applications in medicine. Recently, some algorithms have been developed to achieve this goal. Because of the particularity of cell pathology images, the result of the image retrieval based on single characteristic is not satisfactory. A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed. Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation and mathematics morphology. The features that include color, texture and morphologic features are extracted from single leukocyte to represent main attribute in the search query. The features are then normalized because the numerical value range and physical meaning of extracted features are different. Finally, the relevance feedback system is introduced. So that the system can automatically adjust the weights of different features and improve the results of retrieval system according to the feedback information. Retrieval results using the proposed method fit closely with human perception and are better than those obtained with the methods based on single feature.

[The application of stereology in radiology imaging and cell biology fields].

PubMed

Hu, Na; Wang, Yan; Feng, Yuanming; Lin, Wang

2012-08-01

Stereology is an interdisciplinary method for 3D morphological study developed from mathematics and morphology. It is widely used in medical image analysis and cell biology studies. Because of its unbiased, simple, fast, reliable and non-invasive characteristics, stereology has been widely used in biomedical areas for quantitative analysis and statistics, such as histology, pathology and medical imaging. Because the stereological parameters show distinct differences in different pathology, many scholars use stereological methods to do quantitative analysis in their studies in recent years, for example, in the areas of the condition of cancer cells, tumor grade, disease development and the patient's prognosis, etc. This paper describes the stereological concept and estimation methods, also illustrates the applications of stereology in the fields of CT images, MRI images and cell biology, and finally reflects the universality, the superiority and reliability of stereology.

An Automatic Segmentation Method Combining an Active Contour Model and a Classification Technique for Detecting Polycomb-group Proteinsin High-Throughput Microscopy Images.

PubMed

Gregoretti, Francesco; Cesarini, Elisa; Lanzuolo, Chiara; Oliva, Gennaro; Antonelli, Laura

2016-01-01

The large amount of data generated in biological experiments that rely on advanced microscopy can be handled only with automated image analysis. Most analyses require a reliable cell image segmentation eventually capable of detecting subcellular structures.We present an automatic segmentation method to detect Polycomb group (PcG) proteins areas isolated from nuclei regions in high-resolution fluorescent cell image stacks. It combines two segmentation algorithms that use an active contour model and a classification technique serving as a tool to better understand the subcellular three-dimensional distribution of PcG proteins in live cell image sequences. We obtained accurate results throughout several cell image datasets, coming from different cell types and corresponding to different fluorescent labels, without requiring elaborate adjustments to each dataset.

Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques.

PubMed

Feng, Jingwen; Feng, Tong; Yang, Chengwen; Wang, Wei; Sa, Yu; Feng, Yuanming

2018-06-01

This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining. The texture features of the diffraction images were extracted with in-house software based on the Gray-level co-occurrence matrix algorithm to generate datasets for cell classification with supervised machine learning method. Therefore, this new method has been verified in hydrogen peroxide induced apoptosis model of HL-60. Results show that accuracy of higher than 90% was achieved respectively in independent test datasets from each cell type based on logistic regression with ridge estimators, which indicated that p-DIFC system has a great potential in predicting and classifying cells in different stages of apoptosis.

The Use of Multidimensional Image-Based Analysis to Accurately Monitor Cell Growth in 3D Bioreactor Culture

PubMed Central

Baradez, Marc-Olivier; Marshall, Damian

2011-01-01

The transition from traditional culture methods towards bioreactor based bioprocessing to produce cells in commercially viable quantities for cell therapy applications requires the development of robust methods to ensure the quality of the cells produced. Standard methods for measuring cell quality parameters such as viability provide only limited information making process monitoring and optimisation difficult. Here we describe a 3D image-based approach to develop cell distribution maps which can be used to simultaneously measure the number, confluency and morphology of cells attached to microcarriers in a stirred tank bioreactor. The accuracy of the cell distribution measurements is validated using in silico modelling of synthetic image datasets and is shown to have an accuracy >90%. Using the cell distribution mapping process and principal component analysis we show how cell growth can be quantitatively monitored over a 13 day bioreactor culture period and how changes to manufacture processes such as initial cell seeding density can significantly influence cell morphology and the rate at which cells are produced. Taken together, these results demonstrate how image-based analysis can be incorporated in cell quality control processes facilitating the transition towards bioreactor based manufacture for clinical grade cells. PMID:22028809

The use of multidimensional image-based analysis to accurately monitor cell growth in 3D bioreactor culture.

PubMed

Baradez, Marc-Olivier; Marshall, Damian

2011-01-01

The transition from traditional culture methods towards bioreactor based bioprocessing to produce cells in commercially viable quantities for cell therapy applications requires the development of robust methods to ensure the quality of the cells produced. Standard methods for measuring cell quality parameters such as viability provide only limited information making process monitoring and optimisation difficult. Here we describe a 3D image-based approach to develop cell distribution maps which can be used to simultaneously measure the number, confluency and morphology of cells attached to microcarriers in a stirred tank bioreactor. The accuracy of the cell distribution measurements is validated using in silico modelling of synthetic image datasets and is shown to have an accuracy >90%. Using the cell distribution mapping process and principal component analysis we show how cell growth can be quantitatively monitored over a 13 day bioreactor culture period and how changes to manufacture processes such as initial cell seeding density can significantly influence cell morphology and the rate at which cells are produced. Taken together, these results demonstrate how image-based analysis can be incorporated in cell quality control processes facilitating the transition towards bioreactor based manufacture for clinical grade cells.

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

PubMed Central

Caetano dos Santos, Florentino Luciano; Skottman, Heli; Juuti-Uusitalo, Kati; Hyttinen, Jari

2016-01-01

Aims A fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification. Methods For image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level. Dataset and Results The RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%. Conclusions Here we showed that the developed ensembles of texture descriptors are able to classify the RPE cell maturation stage. Moreover, we proved that preprocessing and region-based decomposition improves many descriptors’ accuracy in biological dataset classification. Finally, we built the first public dataset of stem cell-derived RPE cells, which is publicly available to the scientific community for classification studies. The proposed tool is available at https://www.dei.unipd.it/node/2357 and the RPE dataset at http://www.biomeditech.fi/data/RPE_dataset/. Both are available at https://figshare.com/s/d6fb591f1beb4f8efa6f. PMID:26895509

Preparing Fresh Retinal Slices from Adult Zebrafish for Ex Vivo Imaging Experiments.

PubMed

Giarmarco, Michelle M; Cleghorn, Whitney M; Hurley, James B; Brockerhoff, Susan E

2018-05-09

The retina is a complex tissue that initiates and integrates the first steps of vision. Dysfunction of retinal cells is a hallmark of many blinding diseases, and future therapies hinge on fundamental understandings about how different retinal cells function normally. Gaining such information with biochemical methods has proven difficult because contributions of particular cell types are diminished in the retinal cell milieu. Live retinal imaging can provide a view of numerous biological processes on a subcellular level, thanks to a growing number of genetically encoded fluorescent biosensors. However, this technique has thus far been limited to tadpoles and zebrafish larvae, the outermost retinal layers of isolated retinas, or lower resolution imaging of retinas in live animals. Here we present a method for generating live ex vivo retinal slices from adult zebrafish for live imaging via confocal microscopy. This preparation yields transverse slices with all retinal layers and most cell types visible for performing confocal imaging experiments using perfusion. Transgenic zebrafish expressing fluorescent proteins or biosensors in specific retinal cell types or organelles are used to extract single-cell information from an intact retina. Additionally, retinal slices can be loaded with fluorescent indicator dyes, adding to the method's versatility. This protocol was developed for imaging Ca 2+ within zebrafish cone photoreceptors, but with proper markers it could be adapted to measure Ca 2+ or metabolites in Müller cells, bipolar and horizontal cells, microglia, amacrine cells, or retinal ganglion cells. The retinal pigment epithelium is removed from slices so this method is not suitable for studying that cell type. With practice, it is possible to generate serial slices from one animal for multiple experiments. This adaptable technique provides a powerful tool for answering many questions about retinal cell biology, Ca 2+ , and energy homeostasis.

Correlative Imaging of Fluorescent Proteins in Resin-Embedded Plant Material1

PubMed Central

Bell, Karen; Mitchell, Steve; Paultre, Danae; Posch, Markus; Oparka, Karl

2013-01-01

Fluorescent proteins (FPs) were developed for live-cell imaging and have revolutionized cell biology. However, not all plant tissues are accessible to live imaging using confocal microscopy, necessitating alternative approaches for protein localization. An example is the phloem, a tissue embedded deep within plant organs and sensitive to damage. To facilitate accurate localization of FPs within recalcitrant tissues, we developed a simple method for retaining FPs after resin embedding. This method is based on low-temperature fixation and dehydration, followed by embedding in London Resin White, and avoids the need for cryosections. We show that a palette of FPs can be localized in plant tissues while retaining good structural cell preservation, and that the polymerized block face can be counterstained with cell wall probes. Using this method we have been able to image green fluorescent protein-labeled plasmodesmata to a depth of more than 40 μm beneath the resin surface. Using correlative light and electron microscopy of the phloem, we were able to locate the same FP-labeled sieve elements in semithin and ultrathin sections. Sections were amenable to antibody labeling, and allowed a combination of confocal and superresolution imaging (three-dimensional-structured illumination microscopy) on the same cells. These correlative imaging methods should find several uses in plant cell biology. PMID:23457228

High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles.

PubMed

Li, Fuhai; Zhou, Xiaobo; Zhu, Jinmin; Ma, Jinwen; Huang, Xudong; Wong, Stephen T C

2007-10-09

High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study. The cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system. The proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.

Imaging cell picker: A morphology-based automated cell separation system on a photodegradable hydrogel culture platform.

PubMed

Shibuta, Mayu; Tamura, Masato; Kanie, Kei; Yanagisawa, Masumi; Matsui, Hirofumi; Satoh, Taku; Takagi, Toshiyuki; Kanamori, Toshiyuki; Sugiura, Shinji; Kato, Ryuji

2018-06-09

Cellular morphology on and in a scaffold composed of extracellular matrix generally represents the cellular phenotype. Therefore, morphology-based cell separation should be interesting method that is applicable to cell separation without staining surface markers in contrast to conventional cell separation methods (e.g., fluorescence activated cell sorting and magnetic activated cell sorting). In our previous study, we have proposed a cloning technology using a photodegradable gelatin hydrogel to separate the individual cells on and in hydrogels. To further expand the applicability of this photodegradable hydrogel culture platform, we here report an image-based cell separation system imaging cell picker for the morphology-based cell separation on a photodegradable hydrogel. We have developed the platform which enables the automated workflow of image acquisition, image processing and morphology analysis, and collection of a target cells. We have shown the performance of the morphology-based cell separation through the optimization of the critical parameters that determine the system's performance, such as (i) culture conditions, (ii) imaging conditions, and (iii) the image analysis scheme, to actually clone the cells of interest. Furthermore, we demonstrated the morphology-based cloning performance of cancer cells in the mixture of cells by automated hydrogel degradation by light irradiation and pipetting. Copyright © 2018 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Live-cell confocal microscopy and quantitative 4D image analysis of anchor cell invasion through the basement membrane in C. elegans

PubMed Central

Kelley, Laura C.; Wang, Zheng; Hagedorn, Elliott J.; Wang, Lin; Shen, Wanqing; Lei, Shijun; Johnson, Sam A.; Sherwood, David R.

2018-01-01

Cell invasion through basement membrane (BM) barriers is crucial during development, leukocyte trafficking, and for the spread of cancer. Despite its importance in normal and diseased states, the mechanisms that direct invasion are poorly understood, in large part because of the inability to visualize dynamic cell-basement membrane interactions in vivo. This protocol describes multi-channel time-lapse confocal imaging of anchor cell invasion in live C. elegans. Methods presented include outline slide preparation and worm growth synchronization (15 min), mounting (20 min), image acquisition (20-180 min), image processing (20 min), and quantitative analysis (variable timing). Images acquired enable direct measurement of invasive dynamics including invadopodia formation, cell membrane protrusions, and BM removal. This protocol can be combined with genetic analysis, molecular activity probes, and optogenetic approaches to uncover molecular mechanisms underlying cell invasion. These methods can also be readily adapted for real-time analysis of cell migration, basement membrane turnover, and cell membrane dynamics by any worm laboratory. PMID:28880279

Photoluminescent graphene quantum dots for in vivo imaging of apoptotic cells

NASA Astrophysics Data System (ADS)

Roy, Prathik; Periasamy, Arun Prakash; Lin, Chiu-Ya; Her, Guor-Mour; Chiu, Wei-Jane; Li, Chi-Lin; Shu, Chia-Lun; Huang, Chih-Ching; Liang, Chi-Te; Chang, Huan-Tsung

2015-01-01

Apoptosis (programmed cell death) is linked to many incurable neurodegenerative, cardiovascular and cancer causing diseases. Numerous methods have been developed for imaging apoptotic cells in vitro; however, there are few methods available for imaging apoptotic cells in live animals (in vivo). Here we report a novel method utilizing the unique photoluminescence properties of plant leaf-derived graphene quantum dots (GQDs) modified with annexin V antibody (AbA5) to form (AbA5)-modified GQDs (AbA5-GQDs) enabling us to label apoptotic cells in live zebrafish (Danio rerio). The key is that zebrafish shows bright red photoluminescence in the presence of apoptotic cells. The toxicity of the GQDs has also been investigated with the GQDs exhibiting high biocompatibility as they were excreted from the zebrafish's body without affecting its growth significantly at a concentration lower than 2 mg mL-1 over a period of 4 to 72 hour post fertilization. The GQDs have further been used to image human breast adenocarcinoma cell line (MCF-7 cells), human cervical cancer cell line (HeLa cells), and normal human mammary epithelial cell line (MCF-10A). These results are indispensable to further the advance of graphene-based nanomaterials for biomedical applications.Apoptosis (programmed cell death) is linked to many incurable neurodegenerative, cardiovascular and cancer causing diseases. Numerous methods have been developed for imaging apoptotic cells in vitro; however, there are few methods available for imaging apoptotic cells in live animals (in vivo). Here we report a novel method utilizing the unique photoluminescence properties of plant leaf-derived graphene quantum dots (GQDs) modified with annexin V antibody (AbA5) to form (AbA5)-modified GQDs (AbA5-GQDs) enabling us to label apoptotic cells in live zebrafish (Danio rerio). The key is that zebrafish shows bright red photoluminescence in the presence of apoptotic cells. The toxicity of the GQDs has also been investigated with the GQDs exhibiting high biocompatibility as they were excreted from the zebrafish's body without affecting its growth significantly at a concentration lower than 2 mg mL-1 over a period of 4 to 72 hour post fertilization. The GQDs have further been used to image human breast adenocarcinoma cell line (MCF-7 cells), human cervical cancer cell line (HeLa cells), and normal human mammary epithelial cell line (MCF-10A). These results are indispensable to further the advance of graphene-based nanomaterials for biomedical applications. Electronic supplementary information (ESI) available: Experimental discussion on synthesis, characterization, cellular imaging, cytotoxicity of GQDs in addition to its effect on zebrafish embryos, preparation of annexin V (A5)-modified GQDs (AbA5-GQDs), staining procedures and imaging are given. Figures for XRD, UV-vis absorption, photoluminescence of GQDs, mortality of zebrafish, time course recording of morphology of zebrafish embryos and morphology of adult zebrafish exposed to GQDs are illustrated. See DOI: 10.1039/c4nr07005d

Imaging of cellular spread on a three-dimensional scaffold by means of a novel cell-labeling technique for high-resolution computed tomography.

PubMed

Thimm, Benjamin W; Hofmann, Sandra; Schneider, Philipp; Carretta, Roberto; Müller, Ralph

2012-03-01

Computed tomography (CT) represents a truly three-dimensional (3D) imaging technique that can provide high-resolution images on the cellular level. Thus, one approach to detect single cells is X-ray absorption-based CT, where cells are labeled with a dense, opaque material providing the required contrast for CT imaging. Within the present work, a novel cell-labeling method has been developed showing the feasibility of labeling fixed cells with iron oxide (FeO) particles for subsequent CT imaging and quantitative morphometry. A biotin-streptavidin detection system was exploited to bind FeO particles to its target endothelial cells. The binding of the particles was predominantly close to the cell centers on 2D surfaces as shown by light microscopy, scanning electron microscopy, and CT. When cells were cultured on porous, 3D polyurethane surfaces, significantly more FeO particles were detected compared with surfaces without cells and FeO particle labeling using CT. Here, we report on the implementation and evaluation of a novel cell detection method based on high-resolution CT. This system has potential in cell tracking for 3D in vitro imaging in the future.

Phase contrast microscopy of living cells within the whole lens: spatial correlations and morphological dynamics

PubMed Central

Kong, Zhiying; Zhu, Xiangjia; Zhang, Shenghai; Wu, Jihong

2012-01-01

Purpose Images from cultured lens cells do not convey enough spatial information, and imaging of fixed lens specimens cannot reveal dynamic changes in the cells. As such, a real-time, convenient approach for monitoring label-free imaging of dynamic processes of living cells within the whole lens is urgently needed. Methods Female Wistar rat lenses were kept in organ culture. Insulin-like growth factor-I was added to the culture medium to induce cell mitosis. A novel method of ultraviolet (UV) irradiation was used to induce cell apoptosis and fiber damage. The cellular morphological dynamics within the whole lens were monitored by inverted phase contrast microscopy. Apoptosis was assessed using a commercial kit with Hoechst 33342/YO-PRO®-1/propidium iodide (PI). Results The intrinsic transparency and low-light scattering property of the rat lens permitted direct imaging of the lens epithelial cells (LECs) and the superficial fiber cells. We visualized the processes of mitosis and apoptosis of the LECs, and we obtained dynamic images of posterior fiber cells following UVA irradiation. Conclusions This method opens a new window for observing lens cells in their physiologic location, and it can be readily applied in studies on lens physiology and pathology. PMID:22879736

Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images.

PubMed

Boix, Macarena; Cantó, Begoña

2013-04-01

Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows a segmentation with the largest area in the cell. We study different wavelet families and we conclude that the wavelet db1 is the best and it can serve for posterior works on blood pathologies. The proposed method generates goods results when it is applied on several images. Finally, the proposed algorithm made in MatLab environment is verified for a selected blood cells.

Characterization of the Distance Relationship Between Localized Serotonin Receptors and Glia Cells on Fluorescence Microscopy Images of Brain Tissue.

PubMed

Jacak, Jaroslaw; Schaller, Susanne; Borgmann, Daniela; Winkler, Stephan M

2015-08-01

We here present two new methods for the characterization of fluorescent localization microscopy images obtained from immunostained brain tissue sections. Direct stochastic optical reconstruction microscopy images of 5-HT1A serotonin receptors and glial fibrillary acidic proteins in healthy cryopreserved brain tissues are analyzed. In detail, we here present two image processing methods for characterizing differences in receptor distribution on glial cells and their distribution on neural cells: One variant relies on skeleton extraction and adaptive thresholding, the other on k-means based discrete layer segmentation. Experimental results show that both methods can be applied for distinguishing classes of images with respect to serotonin receptor distribution. Quantification of nanoscopic changes in relative protein expression on particular cell types can be used to analyze degeneration in tissues caused by diseases or medical treatment.

Imaging Tumor Cell Movement In Vivo

PubMed Central

Entenberg, David; Kedrin, Dmitriy; Wyckoff, Jeffrey; Sahai, Erik; Condeelis, John; Segall, Jeffrey E.

2013-01-01

This unit describes the methods that we have been developing for analyzing tumor cell motility in mouse and rat models of breast cancer metastasis. Rodents are commonly used both to provide a mammalian system for studying human tumor cells (as xenografts in immunocompromised mice) as well as for following the development of tumors from a specific tissue type in transgenic lines. The Basic Protocol in this unit describes the standard methods used for generation of mammary tumors and imaging them. Additional protocols for labeling macrophages, blood vessel imaging, and image analysis are also included. PMID:23456602

Glioblastoma cells labeled by robust Raman tags for enhancing imaging contrast.

PubMed

Huang, Li-Ching; Chang, Yung-Ching; Wu, Yi-Syuan; Sun, Wei-Lun; Liu, Chan-Chuan; Sze, Chun-I; Chen, Shiuan-Yeh

2018-05-01

Complete removal of a glioblastoma multiforme (GBM), a highly malignant brain tumor, is challenging due to its infiltrative characteristics. Therefore, utilizing imaging agents such as fluorophores to increase the contrast between GBM and normal cells can help neurosurgeons to locate residual cancer cells during image guided surgery. In this work, Raman tag based labeling and imaging for GBM cells in vitro is described and evaluated. The cell membrane of a GBM adsorbs a substantial amount of functionalized Raman tags through overexpression of the epidermal growth factor receptor (EGFR) and "broadcasts" stronger pre-defined Raman signals than normal cells. The average ratio between Raman signals from a GBM cell and autofluorescence from a normal cell can be up to 15. In addition, the intensity of these images is stable under laser illuminations without suffering from the severe photo-bleaching that usually occurs in fluorescent imaging. Our results show that labeling and imaging GBM cells via robust Raman tags is a viable alternative method to distinguish them from normal cells. This Raman tag based method can be used solely or integrated into an existing fluorescence system to improve the identification of infiltrative glial tumor cells around the boundary, which will further reduce GBM recurrence. In addition, it can also be applied/extended to other types of cancer to improve the effectiveness of image guided surgery.

Glioblastoma cells labeled by robust Raman tags for enhancing imaging contrast

PubMed Central

Huang, Li-Ching; Chang, Yung-Ching; Wu, Yi-Syuan; Sun, Wei-Lun; Liu, Chan-Chuan; Sze, Chun-I; Chen, Shiuan-Yeh

2018-01-01

Complete removal of a glioblastoma multiforme (GBM), a highly malignant brain tumor, is challenging due to its infiltrative characteristics. Therefore, utilizing imaging agents such as fluorophores to increase the contrast between GBM and normal cells can help neurosurgeons to locate residual cancer cells during image guided surgery. In this work, Raman tag based labeling and imaging for GBM cells in vitro is described and evaluated. The cell membrane of a GBM adsorbs a substantial amount of functionalized Raman tags through overexpression of the epidermal growth factor receptor (EGFR) and “broadcasts” stronger pre-defined Raman signals than normal cells. The average ratio between Raman signals from a GBM cell and autofluorescence from a normal cell can be up to 15. In addition, the intensity of these images is stable under laser illuminations without suffering from the severe photo-bleaching that usually occurs in fluorescent imaging. Our results show that labeling and imaging GBM cells via robust Raman tags is a viable alternative method to distinguish them from normal cells. This Raman tag based method can be used solely or integrated into an existing fluorescence system to improve the identification of infiltrative glial tumor cells around the boundary, which will further reduce GBM recurrence. In addition, it can also be applied/extended to other types of cancer to improve the effectiveness of image guided surgery. PMID:29760976

In vivo fluorescence imaging of primate retinal ganglion cells and retinal pigment epithelial cells

NASA Astrophysics Data System (ADS)

Gray, Daniel C.; Merigan, William; Wolfing, Jessica I.; Gee, Bernard P.; Porter, Jason; Dubra, Alfredo; Twietmeyer, Ted H.; Ahamd, Kamran; Tumbar, Remy; Reinholz, Fred; Williams, David R.

2006-08-01

The ability to resolve single cells noninvasively in the living retina has important applications for the study of normal retina, diseased retina, and the efficacy of therapies for retinal disease. We describe a new instrument for high-resolution, in vivo imaging of the mammalian retina that combines the benefits of confocal detection, adaptive optics, multispectral, and fluorescence imaging. The instrument is capable of imaging single ganglion cells and their axons through retrograde transport in ganglion cells of fluorescent dyes injected into the monkey lateral geniculate nucleus (LGN). In addition, we demonstrate a method involving simultaneous imaging in two spectral bands that allows the integration of very weak signals across many frames despite inter-frame movement of the eye. With this method, we are also able to resolve the smallest retinal capillaries in fluorescein angiography and the mosaic of retinal pigment epithelium (RPE) cells with lipofuscin autofluorescence.

Comparison of semi-automated center-dot and fully automated endothelial cell analyses from specular microscopy images.

PubMed

Maruoka, Sachiko; Nakakura, Shunsuke; Matsuo, Naoko; Yoshitomi, Kayo; Katakami, Chikako; Tabuchi, Hitoshi; Chikama, Taiichiro; Kiuchi, Yoshiaki

2017-10-30

To evaluate two specular microscopy analysis methods across different endothelial cell densities (ECDs). Endothelial images of one eye from each of 45 patients were taken by using three different specular microscopes (three replicates each). To determine the consistency of the center-dot method, we compared SP-6000 and SP-2000P images. CME-530 and SP-6000 images were compared to assess the consistency of the fully automated method. The SP-6000 images from the two methods were compared. Intraclass correlation coefficients (ICCs) for the three measurements were calculated, and parametric multiple comparisons tests and Bland-Altman analysis were performed. The ECD mean value was 2425 ± 883 (range 516-3707) cells/mm 2 . ICC values were > 0.9 for all three microscopes for ECD, but the coefficients of variation (CVs) were 0.3-0.6. For ECD measurements, Bland-Altman analysis revealed that the mean difference was 42 cells/mm 2 between the SP-2000P and SP-6000 for the center-dot method; 57 cells/mm 2 between the SP-6000 measurements from both methods; and -5 cells/mm 2 between the SP-6000 and CME-530 for the fully automated method (95% limits of agreement: - 201 to 284 cell/mm 2 , - 410 to 522 cells/mm 2 , and - 327 to 318 cells/mm 2 , respectively). For CV measurements, the mean differences were - 3, - 12, and 13% (95% limits of agreement - 18 to 11, - 26 to 2, and - 5 to 32%, respectively). Despite using three replicate measurements, the precision of the center-dot method with the SP-2000P and SP-6000 software was only ± 10% for ECD data and was even worse for the fully automated method. Japan Clinical Trials Register ( http://www.umin.ac.jp/ctr/index/htm9 ) number UMIN 000015236.

Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

PubMed

Hatipoglu, Nuh; Bilgin, Gokhan

2017-10-01

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

Rotation of single live mammalian cells using dynamic holographic optical tweezers

NASA Astrophysics Data System (ADS)

Bin Cao; Kelbauskas, Laimonas; Chan, Samantha; Shetty, Rishabh M.; Smith, Dean; Meldrum, Deirdre R.

2017-05-01

We report on a method for rotating single mammalian cells about an axis perpendicular to the optical system axis through the imaging plane using dynamic holographic optical tweezers (HOTs). Two optical traps are created on the opposite edges of a mammalian cell and are continuously transitioned through the imaging plane along the circumference of the cell in opposite directions, thus providing the torque to rotate the cell in a controlled fashion. The method enables a complete 360° rotation of live single mammalian cells with spherical or near-to spherical shape in 3D space, and represents a useful tool suitable for the single cell analysis field, including tomographic imaging.

Methods, compositions and kits for imaging cells and tissues using nanoparticles and spatial frequency heterodyne imaging

DOEpatents

Rose-Petruck, Christoph; Wands, Jack R.; Rand, Danielle; Derdak, Zoltan; Ortiz, Vivian

2016-04-19

Methods, compositions, systems, devices and kits are provided herein for preparing and using a nanoparticle composition and spatial frequency heterodyne imaging for visualizing cells or tissues. In various embodiments, the nanoparticle composition includes at least one of: a nanoparticle, a polymer layer, and a binding agent, such that the polymer layer coats the nanoparticle and is for example a polyethylene glycol, a polyelectrolyte, an anionic polymer, or a cationic polymer, and such that the binding agent that specifically binds the cells or the tissue. Methods, compositions, systems, devices and kits are provided for identifying potential therapeutic agents in a model using the nanoparticle composition and spatial frequency heterodyne imaging.

Mapping Diffusion in a Living Cell via the Phasor Approach

PubMed Central

Ranjit, Suman; Lanzano, Luca; Gratton, Enrico

2014-01-01

Diffusion of a fluorescent protein within a cell has been measured using either fluctuation-based techniques (fluorescence correlation spectroscopy (FCS) or raster-scan image correlation spectroscopy) or particle tracking. However, none of these methods enables us to measure the diffusion of the fluorescent particle at each pixel of the image. Measurement using conventional single-point FCS at every individual pixel results in continuous long exposure of the cell to the laser and eventual bleaching of the sample. To overcome this limitation, we have developed what we believe to be a new method of scanning with simultaneous construction of a fluorescent image of the cell. In this believed new method of modified raster scanning, as it acquires the image, the laser scans each individual line multiple times before moving to the next line. This continues until the entire area is scanned. This is different from the original raster-scan image correlation spectroscopy approach, where data are acquired by scanning each frame once and then scanning the image multiple times. The total time of data acquisition needed for this method is much shorter than the time required for traditional FCS analysis at each pixel. However, at a single pixel, the acquired intensity time sequence is short; requiring nonconventional analysis of the correlation function to extract information about the diffusion. These correlation data have been analyzed using the phasor approach, a fit-free method that was originally developed for analysis of FLIM images. Analysis using this method results in an estimation of the average diffusion coefficient of the fluorescent species at each pixel of an image, and thus, a detailed diffusion map of the cell can be created. PMID:25517145

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images.

PubMed

Niioka, Hirohiko; Asatani, Satoshi; Yoshimura, Aina; Ohigashi, Hironori; Tagawa, Seiichi; Miyake, Jun

2018-01-01

In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.

Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy.

PubMed

Wang, Quanli; Niemi, Jarad; Tan, Chee-Meng; You, Lingchong; West, Mike

2010-01-01

An increasingly common component of studies in synthetic and systems biology is analysis of dynamics of gene expression at the single-cell level, a context that is heavily dependent on the use of time-lapse movies. Extracting quantitative data on the single-cell temporal dynamics from such movies remains a major challenge. Here, we describe novel methods for automating key steps in the analysis of single-cell, fluorescent images-segmentation and lineage reconstruction-to recognize and track individual cells over time. The automated analysis iteratively combines a set of extended morphological methods for segmentation, and uses a neighborhood-based scoring method for frame-to-frame lineage linking. Our studies with bacteria, budding yeast and human cells, demonstrate the portability and usability of these methods, whether using phase, bright field or fluorescent images. These examples also demonstrate the utility of our integrated approach in facilitating analyses of engineered and natural cellular networks in diverse settings. The automated methods are implemented in freely available, open-source software.

Comparison of pre-processing techniques for fluorescence microscopy images of cells labeled for actin.

PubMed

Muralidhar, Gautam S; Channappayya, Sumohana S; Slater, John H; Blinka, Ellen M; Bovik, Alan C; Frey, Wolfgang; Markey, Mia K

2008-11-06

Automated analysis of fluorescence microscopy images of endothelial cells labeled for actin is important for quantifying changes in the actin cytoskeleton. The current manual approach is laborious and inefficient. The goal of our work is to develop automated image analysis methods, thereby increasing cell analysis throughput. In this study, we present preliminary results on comparing different algorithms for cell segmentation and image denoising.

Gray-level co-occurrence matrix analysis of several cell types in mouse brain using resolution-enhanced photothermal microscopy

NASA Astrophysics Data System (ADS)

Kobayashi, Takayoshi; Sundaram, Durga; Nakata, Kazuaki; Tsurui, Hiromichi

2017-03-01

Qualifications of intracellular structure were performed for the first time using the gray-level co-occurrence matrix (GLCM) method for images of cells obtained by resolution-enhanced photothermal imaging. The GLCM method has been used to extract five parameters of texture features for five different types of cells in mouse brain; pyramidal neurons and glial cells in the basal nucleus (BGl), dentate gyrus granule cells, cerebellar Purkinje cells, and cerebellar granule cells. The parameters are correlation, contrast, angular second moment (ASM), inverse difference moment (IDM), and entropy for the images of cells of interest in a mouse brain. The parameters vary depending on the pixel distance taken in the analysis method. Based on the obtained results, we identified that the most suitable GLCM parameter is IDM for pyramidal neurons and BGI, granule cells in the dentate gyrus, Purkinje cells and granule cells in the cerebellum. It was also found that the ASM is the most appropriate for neurons in the basal nucleus.

Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy

PubMed Central

Young, Jonathan W; Locke, James C W; Altinok, Alphan; Rosenfeld, Nitzan; Bacarian, Tigran; Swain, Peter S; Mjolsness, Eric; Elowitz, Michael B

2014-01-01

Quantitative single-cell time-lapse microscopy is a powerful method for analyzing gene circuit dynamics and heterogeneous cell behavior. We describe the application of this method to imaging bacteria by using an automated microscopy system. This protocol has been used to analyze sporulation and competence differentiation in Bacillus subtilis, and to quantify gene regulation and its fluctuations in individual Escherichia coli cells. The protocol involves seeding and growing bacteria on small agarose pads and imaging the resulting microcolonies. Images are then reviewed and analyzed using our laboratory's custom MATLAB analysis code, which segments and tracks cells in a frame-to-frame method. This process yields quantitative expression data on cell lineages, which can illustrate dynamic expression profiles and facilitate mathematical models of gene circuits. With fast-growing bacteria, such as E. coli or B. subtilis, image acquisition can be completed in 1 d, with an additional 1–2 d for progressing through the analysis procedure. PMID:22179594

A Microfluidic Platform for Correlative Live-Cell and Super-Resolution Microscopy

PubMed Central

Tam, Johnny; Cordier, Guillaume Alan; Bálint, Štefan; Sandoval Álvarez, Ángel; Borbely, Joseph Steven; Lakadamyali, Melike

2014-01-01

Recently, super-resolution microscopy methods such as stochastic optical reconstruction microscopy (STORM) have enabled visualization of subcellular structures below the optical resolution limit. Due to the poor temporal resolution, however, these methods have mostly been used to image fixed cells or dynamic processes that evolve on slow time-scales. In particular, fast dynamic processes and their relationship to the underlying ultrastructure or nanoscale protein organization cannot be discerned. To overcome this limitation, we have recently developed a correlative and sequential imaging method that combines live-cell and super-resolution microscopy. This approach adds dynamic background to ultrastructural images providing a new dimension to the interpretation of super-resolution data. However, currently, it suffers from the need to carry out tedious steps of sample preparation manually. To alleviate this problem, we implemented a simple and versatile microfluidic platform that streamlines the sample preparation steps in between live-cell and super-resolution imaging. The platform is based on a microfluidic chip with parallel, miniaturized imaging chambers and an automated fluid-injection device, which delivers a precise amount of a specified reagent to the selected imaging chamber at a specific time within the experiment. We demonstrate that this system can be used for live-cell imaging, automated fixation, and immunostaining of adherent mammalian cells in situ followed by STORM imaging. We further demonstrate an application by correlating mitochondrial dynamics, morphology, and nanoscale mitochondrial protein distribution in live and super-resolution images. PMID:25545548

Automated Cell Detection and Morphometry on Growth Plate Images of Mouse Bone

PubMed Central

Ascenzi, Maria-Grazia; Du, Xia; Harding, James I; Beylerian, Emily N; de Silva, Brian M; Gross, Ben J; Kastein, Hannah K; Wang, Weiguang; Lyons, Karen M; Schaeffer, Hayden

2014-01-01

Microscopy imaging of mouse growth plates is extensively used in biology to understand the effect of specific molecules on various stages of normal bone development and on bone disease. Until now, such image analysis has been conducted by manual detection. In fact, when existing automated detection techniques were applied, morphological variations across the growth plate and heterogeneity of image background color, including the faint presence of cells (chondrocytes) located deeper in tissue away from the image’s plane of focus, and lack of cell-specific features, interfered with identification of cell. We propose the first method of automated detection and morphometry applicable to images of cells in the growth plate of long bone. Through ad hoc sequential application of the Retinex method, anisotropic diffusion and thresholding, our new cell detection algorithm (CDA) addresses these challenges on bright-field microscopy images of mouse growth plates. Five parameters, chosen by the user in respect of image characteristics, regulate our CDA. Our results demonstrate effectiveness of the proposed numerical method relative to manual methods. Our CDA confirms previously established results regarding chondrocytes’ number, area, orientation, height and shape of normal growth plates. Our CDA also confirms differences previously found between the genetic mutated mouse Smad1/5CKO and its control mouse on fluorescence images. The CDA aims to aid biomedical research by increasing efficiency and consistency of data collection regarding arrangement and characteristics of chondrocytes. Our results suggest that automated extraction of data from microscopy imaging of growth plates can assist in unlocking information on normal and pathological development, key to the underlying biological mechanisms of bone growth. PMID:25525552

Dynamic phase differences based on quantitative phase imaging for the objective evaluation of cell behavior.

PubMed

Krizova, Aneta; Collakova, Jana; Dostal, Zbynek; Kvasnica, Lukas; Uhlirova, Hana; Zikmund, Tomas; Vesely, Pavel; Chmelik, Radim

2015-01-01

Quantitative phase imaging (QPI) brought innovation to noninvasive observation of live cell dynamics seen as cell behavior. Unlike the Zernike phase contrast or differential interference contrast, QPI provides quantitative information about cell dry mass distribution. We used such data for objective evaluation of live cell behavioral dynamics by the advanced method of dynamic phase differences (DPDs). The DPDs method is considered a rational instrument offered by QPI. By subtracting the antecedent from the subsequent image in a time-lapse series, only the changes in mass distribution in the cell are detected. The result is either visualized as a two dimensional color-coded projection of these two states of the cell or as a time dependence of changes quantified in picograms. Then in a series of time-lapse recordings, the chain of cell mass distribution changes that would otherwise escape attention is revealed. Consequently, new salient features of live cell behavior should emerge. Construction of the DPDs method and results exhibiting the approach are presented. Advantage of the DPDs application is demonstrated on cells exposed to an osmotic challenge. For time-lapse acquisition of quantitative phase images, the recently developed coherence-controlled holographic microscope was employed.

Dynamic phase differences based on quantitative phase imaging for the objective evaluation of cell behavior

NASA Astrophysics Data System (ADS)

Krizova, Aneta; Collakova, Jana; Dostal, Zbynek; Kvasnica, Lukas; Uhlirova, Hana; Zikmund, Tomas; Vesely, Pavel; Chmelik, Radim

2015-11-01

Quantitative phase imaging (QPI) brought innovation to noninvasive observation of live cell dynamics seen as cell behavior. Unlike the Zernike phase contrast or differential interference contrast, QPI provides quantitative information about cell dry mass distribution. We used such data for objective evaluation of live cell behavioral dynamics by the advanced method of dynamic phase differences (DPDs). The DPDs method is considered a rational instrument offered by QPI. By subtracting the antecedent from the subsequent image in a time-lapse series, only the changes in mass distribution in the cell are detected. The result is either visualized as a two-dimensional color-coded projection of these two states of the cell or as a time dependence of changes quantified in picograms. Then in a series of time-lapse recordings, the chain of cell mass distribution changes that would otherwise escape attention is revealed. Consequently, new salient features of live cell behavior should emerge. Construction of the DPDs method and results exhibiting the approach are presented. Advantage of the DPDs application is demonstrated on cells exposed to an osmotic challenge. For time-lapse acquisition of quantitative phase images, the recently developed coherence-controlled holographic microscope was employed.

Multimodality molecular imaging and extracellular vesicle release based genetic profiling with porphyrin nanodroplets (Conference Presentation)

NASA Astrophysics Data System (ADS)

Zemp, Roger J.; Paproski, Robert J.

2017-03-01

For emerging tissue-engineering applications, transplants, and cell-based therapies it is important to assess cell viability and function in vivo in deep tissues. Bioluminescence and fluorescence methods are poorly suited to deep monitoring applications with high resolution and require genetically-engineered reporters which are not always feasible. We report on a method for imaging cell viability using deep, high-resolution photoacoustic imaging. We use an exogenous dye, Resazurin, itself weakly fluorescent until it is reduced from blue to a pink color with bright red fluorescence. Upon cell death fluorescence is lost and an absorption shift is observed. The irreversible reaction of resazurin to resorufin is proportional to aerobic respiration. We detect colorimetric absorption shifts using multispectral photoacoustic imaging and quantify the fraction of viable cells. SKOV-3 cells with and without ±80oC heat treatment were imaged after Resazurin treatment. High 575nm:620nm ratiometric absorption and photoacoustic signals in viable cells were observed with a much lower ratio in low-viability populations.

Coherent diffraction imaging of non-isolated object with apodized illumination.

PubMed

Khakurel, Krishna P; Kimura, Takashi; Joti, Yasumasa; Matsuyama, Satoshi; Yamauchi, Kazuto; Nishino, Yoshinori

2015-11-02

Coherent diffraction imaging (CDI) is an established lensless imaging method widely used at the x-ray regime applicable to the imaging of non-periodic materials. Conventional CDI can practically image isolated objects only, which hinders the broader application of the method. We present the imaging of non-isolated objects by employing recently proposed "non-scanning" apodized-illumination CDI at an optical wavelength. We realized isolated apodized illumination with a specially designed optical configuration and succeeded in imaging phase objects as well as amplitude objects. The non-scanning nature of the method is important particularly in imaging live cells and tissues, where fast imaging is required for non-isolated objects, and is an advantage over ptychography. We believe that our result of phase contrast imaging at an optical wavelength can be extended to the quantitative phase imaging of cells and tissues. The method also provides the feasibility of the lensless single-shot imaging of extended objects with x-ray free-electron lasers.

Color edges extraction using statistical features and automatic threshold technique: application to the breast cancer cells.

PubMed

Ben Chaabane, Salim; Fnaiech, Farhat

2014-01-23

Color image segmentation has been so far applied in many areas; hence, recently many different techniques have been developed and proposed. In the medical imaging area, the image segmentation may be helpful to provide assistance to doctor in order to follow-up the disease of a certain patient from the breast cancer processed images. The main objective of this work is to rebuild and also to enhance each cell from the three component images provided by an input image. Indeed, from an initial segmentation obtained using the statistical features and histogram threshold techniques, the resulting segmentation may represent accurately the non complete and pasted cells and enhance them. This allows real help to doctors, and consequently, these cells become clear and easy to be counted. A novel method for color edges extraction based on statistical features and automatic threshold is presented. The traditional edge detector, based on the first and the second order neighborhood, describing the relationship between the current pixel and its neighbors, is extended to the statistical domain. Hence, color edges in an image are obtained by combining the statistical features and the automatic threshold techniques. Finally, on the obtained color edges with specific primitive color, a combination rule is used to integrate the edge results over the three color components. Breast cancer cell images were used to evaluate the performance of the proposed method both quantitatively and qualitatively. Hence, a visual and a numerical assessment based on the probability of correct classification (PC), the false classification (Pf), and the classification accuracy (Sens(%)) are presented and compared with existing techniques. The proposed method shows its superiority in the detection of points which really belong to the cells, and also the facility of counting the number of the processed cells. Computer simulations highlight that the proposed method substantially enhances the segmented image with smaller error rates better than other existing algorithms under the same settings (patterns and parameters). Moreover, it provides high classification accuracy, reaching the rate of 97.94%. Additionally, the segmentation method may be extended to other medical imaging types having similar properties.

Cell-free measurements of brightness of fluorescently labeled antibodies

PubMed Central

Zhou, Haiying; Tourkakis, George; Shi, Dennis; Kim, David M.; Zhang, Hairong; Du, Tommy; Eades, William C.; Berezin, Mikhail Y.

2017-01-01

Validation of imaging contrast agents, such as fluorescently labeled imaging antibodies, has been recognized as a critical challenge in clinical and preclinical studies. As the number of applications for imaging antibodies grows, these materials are increasingly being subjected to careful scrutiny. Antibody fluorescent brightness is one of the key parameters that is of critical importance. Direct measurements of the brightness with common spectroscopy methods are challenging, because the fluorescent properties of the imaging antibodies are highly sensitive to the methods of conjugation, degree of labeling, and contamination with free dyes. Traditional methods rely on cell-based assays that lack reproducibility and accuracy. In this manuscript, we present a novel and general approach for measuring the brightness using antibody-avid polystyrene beads and flow cytometry. As compared to a cell-based method, the described technique is rapid, quantitative, and highly reproducible. The proposed method requires less than ten microgram of sample and is applicable for optimizing synthetic conjugation procedures, testing commercial imaging antibodies, and performing high-throughput validation of conjugation procedures. PMID:28150730

Segmentation and Morphometric Analysis of Cells from Fluorescence Microscopy Images of Cytoskeletons

PubMed Central

Ujihara, Yoshihiro; Nakamura, Masanori; Miyazaki, Hiroshi; Wada, Shigeo

2013-01-01

We developed a method to reconstruct cell geometry from confocal fluorescence microscopy images of the cytoskeleton. In the method, region growing was implemented twice. First, it was applied to the extracellular regions to differentiate them from intracellular noncytoskeletal regions, which both appear black in fluorescence microscopy imagery, and then to cell regions for cell identification. Analysis of morphological parameters revealed significant changes in cell shape associated with cytoskeleton disruption, which offered insight into the mechanical role of the cytoskeleton in maintaining cell shape. The proposed segmentation method is promising for investigations on cell morphological changes with respect to internal cytoskeletal structures. PMID:23762186

Segmentation and morphometric analysis of cells from fluorescence microscopy images of cytoskeletons.

PubMed

Ujihara, Yoshihiro; Nakamura, Masanori; Miyazaki, Hiroshi; Wada, Shigeo

2013-01-01

We developed a method to reconstruct cell geometry from confocal fluorescence microscopy images of the cytoskeleton. In the method, region growing was implemented twice. First, it was applied to the extracellular regions to differentiate them from intracellular noncytoskeletal regions, which both appear black in fluorescence microscopy imagery, and then to cell regions for cell identification. Analysis of morphological parameters revealed significant changes in cell shape associated with cytoskeleton disruption, which offered insight into the mechanical role of the cytoskeleton in maintaining cell shape. The proposed segmentation method is promising for investigations on cell morphological changes with respect to internal cytoskeletal structures.

In situ cell-by-cell imaging and analysis of small cell populations by mass spectrometry

USDA-ARS?s Scientific Manuscript database

Molecular imaging by mass spectrometry (MS) is emerging as a tool to determine the distribution of proteins, lipids and metabolites in tissues. The existing imaging methods, however, rely on predefined typically rectangular grids for sampling that ignore the natural cellular organization of the tiss...

A gradient method for the quantitative analysis of cell movement and tissue flow and its application to the analysis of multicellular Dictyostelium development.

PubMed

Siegert, F; Weijer, C J; Nomura, A; Miike, H

1994-01-01

We describe the application of a novel image processing method, which allows quantitative analysis of cell and tissue movement in a series of digitized video images. The result is a vector velocity field showing average direction and velocity of movement for every pixel in the frame. We apply this method to the analysis of cell movement during different stages of the Dictyostelium developmental cycle. We analysed time-lapse video recordings of cell movement in single cells, mounds and slugs. The program can correctly assess the speed and direction of movement of either unlabelled or labelled cells in a time series of video images depending on the illumination conditions. Our analysis of cell movement during multicellular development shows that the entire morphogenesis of Dictyostelium is characterized by rotational cell movement. The analysis of cell and tissue movement by the velocity field method should be applicable to the analysis of morphogenetic processes in other systems such as gastrulation and neurulation in vertebrate embryos.

89Zr-Oxine Complex for In Vivo PET Imaging of Labelled Cells and Associated Methods | NCI Technology Transfer Center | TTC

Cancer.gov

The National Cancer Institute seek parties interested in in-licensing and/or collaborative research to develop and commercialize cell labeling, cell tracking, cell trafficking, cell-based therapy, and PET imaging for cancer.

A Semi-Automatic Method for Image Analysis of Edge Dynamics in Living Cells

PubMed Central

Huang, Lawrence; Helmke, Brian P.

2011-01-01

Spatial asymmetry of actin edge ruffling contributes to the process of cell polarization and directional migration, but mechanisms by which external cues control actin polymerization near cell edges remain unclear. We designed a quantitative image analysis strategy to measure the spatiotemporal distribution of actin edge ruffling. Time-lapse images of endothelial cells (ECs) expressing mRFP-actin were segmented using an active contour method. In intensity line profiles oriented normal to the cell edge, peak detection identified the angular distribution of polymerized actin within 1 µm of the cell edge, which was localized to lamellipodia and edge ruffles. Edge features associated with filopodia and peripheral stress fibers were removed. Circular statistical analysis enabled detection of cell polarity, indicated by a unimodal distribution of edge ruffles. To demonstrate the approach, we detected a rapid, nondirectional increase in edge ruffling in serum-stimulated ECs and a change in constitutive ruffling orientation in quiescent, nonpolarized ECs. Error analysis using simulated test images demonstrate robustness of the method to variations in image noise levels, edge ruffle arc length, and edge intensity gradient. These quantitative measurements of edge ruffling dynamics enable investigation at the cellular length scale of the underlying molecular mechanisms regulating actin assembly and cell polarization. PMID:21643526

A high-throughput AO/PI-based cell concentration and viability detection method using the Celigo image cytometry.

PubMed

Chan, Leo Li-Ying; Smith, Tim; Kumph, Kendra A; Kuksin, Dmitry; Kessel, Sarah; Déry, Olivier; Cribbes, Scott; Lai, Ning; Qiu, Jean

2016-10-01

To ensure cell-based assays are performed properly, both cell concentration and viability have to be determined so that the data can be normalized to generate meaningful and comparable results. Cell-based assays performed in immuno-oncology, toxicology, or bioprocessing research often require measuring of multiple samples and conditions, thus the current automated cell counter that uses single disposable counting slides is not practical for high-throughput screening assays. In the recent years, a plate-based image cytometry system has been developed for high-throughput biomolecular screening assays. In this work, we demonstrate a high-throughput AO/PI-based cell concentration and viability method using the Celigo image cytometer. First, we validate the method by comparing directly to Cellometer automated cell counter. Next, cell concentration dynamic range, viability dynamic range, and consistency are determined. The high-throughput AO/PI method described here allows for 96-well to 384-well plate samples to be analyzed in less than 7 min, which greatly reduces the time required for the single sample-based automated cell counter. In addition, this method can improve the efficiency for high-throughput screening assays, where multiple cell counts and viability measurements are needed prior to performing assays such as flow cytometry, ELISA, or simply plating cells for cell culture.

Measurement of marine picoplankton cell size by using a cooled, charge-coupled device camera with image-analyzed fluorescence microscopy.

PubMed Central

Viles, C L; Sieracki, M E

1992-01-01

Accurate measurement of the biomass and size distribution of picoplankton cells (0.2 to 2.0 microns) is paramount in characterizing their contribution to the oceanic food web and global biogeochemical cycling. Image-analyzed fluorescence microscopy, usually based on video camera technology, allows detailed measurements of individual cells to be taken. The application of an imaging system employing a cooled, slow-scan charge-coupled device (CCD) camera to automated counting and sizing of individual picoplankton cells from natural marine samples is described. A slow-scan CCD-based camera was compared to a video camera and was superior for detecting and sizing very small, dim particles such as fluorochrome-stained bacteria. Several edge detection methods for accurately measuring picoplankton cells were evaluated. Standard fluorescent microspheres and a Sargasso Sea surface water picoplankton population were used in the evaluation. Global thresholding was inappropriate for these samples. Methods used previously in image analysis of nanoplankton cells (2 to 20 microns) also did not work well with the smaller picoplankton cells. A method combining an edge detector and an adaptive edge strength operator worked best for rapidly generating accurate cell sizes. A complete sample analysis of more than 1,000 cells averages about 50 min and yields size, shape, and fluorescence data for each cell. With this system, the entire size range of picoplankton can be counted and measured. Images PMID:1610183

Introduction to Modern Methods in Light Microscopy.

PubMed

Ryan, Joel; Gerhold, Abby R; Boudreau, Vincent; Smith, Lydia; Maddox, Paul S

2017-01-01

For centuries, light microscopy has been a key method in biological research, from the early work of Robert Hooke describing biological organisms as cells, to the latest in live-cell and single-molecule systems. Here, we introduce some of the key concepts related to the development and implementation of modern microscopy techniques. We briefly discuss the basics of optics in the microscope, super-resolution imaging, quantitative image analysis, live-cell imaging, and provide an outlook on active research areas pertaining to light microscopy.

3D nanoscale imaging of the yeast, Schizosaccharomyces pombe, by full-field transmission X-ray microscopy at 5.4 keV.

PubMed

Chen, Jie; Yang, Yunhao; Zhang, Xiaobo; Andrews, Joy C; Pianetta, Piero; Guan, Yong; Liu, Gang; Xiong, Ying; Wu, Ziyu; Tian, Yangchao

2010-07-01

Three-dimensional (3D) nanoscale structures of the fission yeast, Schizosaccharomyces pombe, can be obtained by full-field transmission hard X-ray microscopy with 30 nm resolution using synchrotron radiation sources. Sample preparation is relatively simple and the samples are portable across various imaging environments, allowing for high-throughput sample screening. The yeast cells were fixed and double-stained with Reynold's lead citrate and uranyl acetate. We performed both absorption contrast and Zernike phase contrast imaging on these cells in order to test this method. The membranes, nucleus, and subcellular organelles of the cells were clearly visualized using absorption contrast mode. The X-ray images of the cells could be used to study the spatial distributions of the organelles in the cells. These results show unique structural information, demonstrating that hard X-ray microscopy is a complementary method for imaging and analyzing biological samples.

Live cell imaging compatible immobilization of Chlamydomonas reinhardtii in microfluidic platform for biodiesel research.

PubMed

Park, Jae Woo; Na, Sang Cheol; Nguyen, Thanh Qua; Paik, Sang-Min; Kang, Myeongwoo; Hong, Daewha; Choi, Insung S; Lee, Jae-Hyeok; Jeon, Noo Li

2015-03-01

This paper describes a novel surface immobilization method for live-cell imaging of Chlamydomonas reinhardtii for continuous monitoring of lipid droplet accumulation. Microfluidics allows high-throughput manipulation and analysis of single cells in precisely controlled microenvironment. Fluorescence imaging based quantitative measurement of lipid droplet accumulation in microalgae had been difficult due to their intrinsic motile behavior. We present a simple surface immobilization method using gelatin coating as the "biological glue." We take advantage of hydroxyproline (Hyp)-based non-covalent interaction between gelatin and the outer cell wall of microalgae to anchor the cells inside the microfluidic device. We have continuously monitored single microalgal cells for up to 6 days. The immobilized microalgae remain viable (viability was comparable to bulk suspension cultured controls). When exposed to wall shear stress, most of the cells remain attached up to 0.1 dyne/cm(2) . Surface immobilization allowed high-resolution, live-cell imaging of mitotic process in real time-which followed previously reported stages in mitosis of suspension cultured cells. Use of gelatin coated microfluidics devices can result in better methods for microalgae strain screening and culture condition optimization that will help microalgal biodiesel become more economically viable. © 2014 Wiley Periodicals, Inc.

In vitro Cell Viability by CellProfiler® Software as Equivalent to MTT Assay.

PubMed

Gasparini, Luciana S; Macedo, Nayana D; Pimentel, Elisângela F; Fronza, Marcio; Junior, Valdemar L; Borges, Warley S; Cole, Eduardo R; Andrade, Tadeu U; Endringer, Denise C; Lenz, Dominik

2017-07-01

This study evaluated in vitro cell viability by the colorimetric MTT stands for 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay compared to image analysis by CellProfiler ® software. Hepatoma (Hepa-1c1c7) and fibroblast (L929) cells were exposed to isolated substances, camptothecin, lycorine, tazettine, albomaculine, 3-epimacronine, trispheridine, galanthine and Padina gymnospora , Sargassum sp. methanolic extract, and Habranthus itaobinus Ravenna ethyl acetate in different concentrations. After MTT assay, cells were stained with Panotic dye kit. Cell images were obtained with an inverted microscope equipped with a digital camera. The images were analyzed by CellProfiler ® . No cytotoxicity at the highest concentration analyzed for 3-epimacronine, albomaculine, galanthine, trispheridine, P. gymnospora extract and Sargassum sp. extract where detected. Tazettine offered cytotoxicity only against the Hepa1c1c7 cell line. Lycorine, camptothecin, and H. itaobinus extract exhibited cytotoxic effects in both cell lines. The viability methods tested were correlated demonstrated by Bland-Atman test with normal distribution with mean difference between the two methods close to zero, bias value 3.0263. The error was within the limits of the confidence intervals and these values had a narrow difference. The correlation between the two methods was demonstrated by the linear regression plotted as R 2 . CellProfiler ® image analysis presented similar results to the MTT assay in the identification of viable cells, and image analysis may assist part of biological analysis procedures. The presented methodology is inexpensive and reproducible. In vitro cell viability assessment with MTT (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) assay may be replaced by image analysis by CellProfiler ® . The viability methods tested were correlated demonstrated by Bland-Atman test with normal distribution with mean difference between the two methods close to zero, bias value 3.0263. The correlation between the two methods was demonstrated by the linear regression plotted as R2. Abbreviations: HPLC: High pressure liquid chromatography MTT: (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide) (3-(4, 5-dimethylthiazolyl-2)-2, 5-diphenyltetrazolium bromide).

Multi-scale Gaussian representation and outline-learning based cell image segmentation

PubMed Central

2013-01-01

Background High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. Methods We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. Results and conclusions We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks. PMID:24267488

Multispectral Live-Cell Imaging.

PubMed

Cohen, Sarah; Valm, Alex M; Lippincott-Schwartz, Jennifer

2018-06-01

Fluorescent proteins and vital dyes are invaluable tools for studying dynamic processes within living cells. However, the ability to distinguish more than a few different fluorescent reporters in a single sample is limited by the spectral overlap of available fluorophores. Here, we present a protocol for imaging live cells labeled with six fluorophores simultaneously. A confocal microscope with a spectral detector is used to acquire images, and linear unmixing algorithms are applied to identify the fluorophores present in each pixel of the image. We describe the application of this method to visualize the dynamics of six different organelles, and to quantify the contacts between organelles. However, this method can be used to image any molecule amenable to tagging with a fluorescent probe. Thus, multispectral live-cell imaging is a powerful tool for systems-level analysis of cellular organization and dynamics. © 2018 by John Wiley & Sons, Inc. Copyright © 2018 John Wiley & Sons, Inc.

Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection--a first study.

PubMed

Hu, D; Sarder, P; Ronhovde, P; Orthaus, S; Achilefu, S; Nussinov, Z

2014-01-01

Inspired by a multiresolution community detection based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Furthermore, using the proposed method, the mean-square error in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The multiresolution community detection method appeared to perform better than a popular spectral clustering-based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in mean-square error with increasing resolution. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection -A First Study

PubMed Central

Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Orthaus, Sandra; Achilefu, Samuel; Nussinov, Zohar

2014-01-01

Inspired by a multi-resolution community detection (MCD) based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Further, using the proposed method, the mean-square error (MSE) in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The MCD method appeared to perform better than a popular spectral clustering based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in MSE with increasing resolution. PMID:24251410

Optical sectioning and 3D reconstructions as an alternative to scanning electron microscopy for analysis of cell shape.

PubMed

Landis, Jacob B; Ventura, Kayla L; Soltis, Douglas E; Soltis, Pamela S; Oppenheimer, David G

2015-04-01

Visualizing flower epidermal cells is often desirable for investigating the interaction between flowers and their pollinators, in addition to the broader range of ecological interactions in which flowers are involved. We developed a protocol for visualizing petal epidermal cells without the limitations of the commonly used method of scanning electron microscopy (SEM). Flower material was collected and fixed in glutaraldehyde, followed by dehydration in an ethanol series. Flowers were dissected to collect petals, and subjected to a Histo-Clear series to remove the cuticle. Material was then stained with aniline blue, mounted on microscope slides, and imaged using a compound fluorescence microscope to obtain optical sections that were reconstructed into a 3D image. This optical sectioning method yielded high-quality images of the petal epidermal cells with virtually no damage to cells. Flowers were processed in larger batches than are possible using common SEM methods. Also, flower size was not a limiting factor as often observed in SEM studies. Flowers up to 5 cm in length were processed and mounted for visualization. This method requires no special equipment for sample preparation prior to imaging and should be seen as an alternative method to SEM.

Comparison of manual & automated analysis methods for corneal endothelial cell density measurements by specular microscopy.

PubMed

Huang, Jianyan; Maram, Jyotsna; Tepelus, Tudor C; Modak, Cristina; Marion, Ken; Sadda, SriniVas R; Chopra, Vikas; Lee, Olivia L

2017-08-07

To determine the reliability of corneal endothelial cell density (ECD) obtained by automated specular microscopy versus that of validated manual methods and factors that predict such reliability. Sharp central images from 94 control and 106 glaucomatous eyes were captured with Konan specular microscope NSP-9900. All images were analyzed by trained graders using Konan CellChek Software, employing the fully- and semi-automated methods as well as Center Method. Images with low cell count (input cells number <100) and/or guttata were compared with the Center and Flex-Center Methods. ECDs were compared and absolute error was used to assess variation. The effect on ECD of age, cell count, cell size, and cell size variation was evaluated. No significant difference was observed between the Center and Flex-Center Methods in corneas with guttata (p=0.48) or low ECD (p=0.11). No difference (p=0.32) was observed in ECD of normal controls <40 yrs old between the fully-automated method and manual Center Method. However, in older controls and glaucomatous eyes, ECD was overestimated by the fully-automated method (p=0.034) and semi-automated method (p=0.025) as compared to manual method. Our findings show that automated analysis significantly overestimates ECD in the eyes with high polymegathism and/or large cell size, compared to the manual method. Therefore, we discourage reliance upon the fully-automated method alone to perform specular microscopy analysis, particularly if an accurate ECD value is imperative. Copyright © 2017. Published by Elsevier España, S.L.U.

Imaging of polysaccharides in the tomato cell wall with Raman microspectroscopy

PubMed Central

2014-01-01

Background The primary cell wall of fruits and vegetables is a structure mainly composed of polysaccharides (pectins, hemicelluloses, cellulose). Polysaccharides are assembled into a network and linked together. It is thought that the percentage of components and of plant cell wall has an important influence on mechanical properties of fruits and vegetables. Results In this study the Raman microspectroscopy technique was introduced to the visualization of the distribution of polysaccharides in cell wall of fruit. The methodology of the sample preparation, the measurement using Raman microscope and multivariate image analysis are discussed. Single band imaging (for preliminary analysis) and multivariate image analysis methods (principal component analysis and multivariate curve resolution) were used for the identification and localization of the components in the primary cell wall. Conclusions Raman microspectroscopy supported by multivariate image analysis methods is useful in distinguishing cellulose and pectins in the cell wall in tomatoes. It presents how the localization of biopolymers was possible with minimally prepared samples. PMID:24917885

Phase imaging microscopy for the diagnostics of plasma-cell interaction

NASA Astrophysics Data System (ADS)

Ohene, Yolanda; Marinov, Ilya; de Laulanié, Lucie; Dupuy, Corinne; Wattelier, Benoit; Starikovskaia, Svetlana

2015-06-01

Phase images of biological specimens were obtained by the method of Quadriwave Lateral Shearing Interferometry (QWLSI). The QWLSI technique produces, at high resolution, phase images of the cells having been exposed to a plasma treatment and enables the quantitative analysis of the changes in the surface area of the cells over time. Morphological changes in the HTori normal thyroid cells were demonstrated using this method. There was a comparison of the cell behaviour between control cells, cells treated by plasma of a nanosecond dielectric barrier discharge, including cells pre-treated by catalase, and cells treated with an equivalent amount of H2O2. The major changes in the cell membrane morphology were observed at only 5 min after the plasma treatment. The primary role of reactive oxygen species (ROS) in this degradation is suggested. Deformation and condensation of the cell nucleus were observed 2-3 h after the treatment and are supposedly related to apoptosis induction. The coupling of the phase QWLSI with immunofluorescence imaging would give a deeper insight into the mechanisms of plasma induced cell death.

Quantitative imaging of glutathione in live cells using a reversible reaction-based ratiometric fluorescent probe

USDA-ARS?s Scientific Manuscript database

Glutathione (GSH) plays an important role in maintaining redox homeostasis inside cells. Currently, there are no methods available to quantitatively assess the GSH concentration in live cells. Live cell fluorescence imaging revolutionized the understanding of cell biology and has become an indispens...

Super-resolution Microscopy in Plant Cell Imaging.

PubMed

Komis, George; Šamajová, Olga; Ovečka, Miroslav; Šamaj, Jozef

2015-12-01

Although the development of super-resolution microscopy methods dates back to 1994, relevant applications in plant cell imaging only started to emerge in 2010. Since then, the principal super-resolution methods, including structured-illumination microscopy (SIM), photoactivation localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), and stimulated emission depletion microscopy (STED), have been implemented in plant cell research. However, progress has been limited due to the challenging properties of plant material. Here we summarize the basic principles of existing super-resolution methods and provide examples of applications in plant science. The limitations imposed by the nature of plant material are reviewed and the potential for future applications in plant cell imaging is highlighted. Copyright © 2015 Elsevier Ltd. All rights reserved.

Diffusion lengths of silicon solar cells from luminescence images

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

Wuerfel, P.; Trupke, T.; Puzzer, T.

A method for spatially resolved measurement of the minority carrier diffusion length in silicon wafers and in silicon solar cells is introduced. The method, which is based on measuring the ratio of two luminescence images taken with two different spectral filters, is applicable, in principle, to both photoluminescence and electroluminescence measurements and is demonstrated experimentally by electroluminescence measurements on a multicrystalline silicon solar cell. Good agreement is observed with the diffusion length distribution obtained from a spectrally resolved light beam induced current map. In contrast to the determination of diffusion lengths from one single luminescence image, the method proposed heremore » gives absolute values of the diffusion length and, in comparison, it is much less sensitive to lateral voltage variations across the cell area as caused by local variations of the series resistance. It is also shown that measuring the ratio of two luminescence images allows distinguishing shunts or surface defects from bulk defects.« less

Comparative assessment of fluorescent transgene methods for quantitative imaging in human cells.

PubMed

Mahen, Robert; Koch, Birgit; Wachsmuth, Malte; Politi, Antonio Z; Perez-Gonzalez, Alexis; Mergenthaler, Julia; Cai, Yin; Ellenberg, Jan

2014-11-05

Fluorescence tagging of proteins is a widely used tool to study protein function and dynamics in live cells. However, the extent to which different mammalian transgene methods faithfully report on the properties of endogenous proteins has not been studied comparatively. Here we use quantitative live-cell imaging and single-molecule spectroscopy to analyze how different transgene systems affect imaging of the functional properties of the mitotic kinase Aurora B. We show that the transgene method fundamentally influences level and variability of expression and can severely compromise the ability to report on endogenous binding and localization parameters, providing a guide for quantitative imaging studies in mammalian cells. © 2014 Mahen et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Evaluation of imaging biomarkers for identification of single cancer cells in blood

NASA Astrophysics Data System (ADS)

Odaka, Masao; Kim, Hyonchol; Girault, Mathias; Hattori, Akihiro; Terazono, Hideyuki; Matsuura, Kenji; Yasuda, Kenji

2015-06-01

A method of discriminating single cancer cells from whole blood cells based on their morphological visual characteristics (i.e., “imaging biomarker”) was examined. Cells in healthy rat blood, a cancer cell line (MAT-LyLu), and cells in cancer-cell-implanted rat blood were chosen as models, and their bright-field (BF, whole-cell morphology) and fluorescence (FL, nucleus morphology) images were taken by an on-chip multi-imaging flow cytometry system and compared. Eight imaging biomarker indices, i.e., cellular area in a BF image, nucleus area in an FL image, area ratio of a whole cell and its nucleus, distance of the mass center between a whole cell and nucleus, cellular and nucleus perimeter, and perimeter ratios were calculated and analyzed using the BF and FL images taken. Results show that cancer cells can be clearly distinguished from healthy blood cells using correlation diagrams for cellular and nucleus areas as two different categories. Moreover, a portion of cancer cells showed a low nucleus perimeter ratio less than 0.9 because of the irregular nucleus morphologies of cancer cells. These results indicate that the measurements of imaging biomarkers are practically applicable to identifying cancer cells in blood.

Investigating the Functional Role of Prostate-Specific Membrane Antigen and its Enzymatic Activity in Prostate Cancer Metastasis

DTIC Science & Technology

2008-02-01

fluorescent probes for live cell imaging . PSMA distribution of cells grown on different extracellular matrices will be characterized to provide guidance...PCa migration, using in vitro cell model systems and live - cell imaging methods, we characterized the role of PSMA in cell motility and adhesion. Using...Generated fluorescently conjugated anti-PSMA antibodies for live cell imaging . 2. Optimized the siRNA-PSMA transfection and achieved an approximately

Classification of human carcinoma cells using multispectral imagery

NASA Astrophysics Data System (ADS)

Ćinar, Umut; Y. Ćetin, Yasemin; Ćetin-Atalay, Rengul; Ćetin, Enis

2016-03-01

In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options.

Improved image decompression for reduced transform coding artifacts

NASA Technical Reports Server (NTRS)

Orourke, Thomas P.; Stevenson, Robert L.

1994-01-01

The perceived quality of images reconstructed from low bit rate compression is severely degraded by the appearance of transform coding artifacts. This paper proposes a method for producing higher quality reconstructed images based on a stochastic model for the image data. Quantization (scalar or vector) partitions the transform coefficient space and maps all points in a partition cell to a representative reconstruction point, usually taken as the centroid of the cell. The proposed image estimation technique selects the reconstruction point within the quantization partition cell which results in a reconstructed image which best fits a non-Gaussian Markov random field (MRF) image model. This approach results in a convex constrained optimization problem which can be solved iteratively. At each iteration, the gradient projection method is used to update the estimate based on the image model. In the transform domain, the resulting coefficient reconstruction points are projected to the particular quantization partition cells defined by the compressed image. Experimental results will be shown for images compressed using scalar quantization of block DCT and using vector quantization of subband wavelet transform. The proposed image decompression provides a reconstructed image with reduced visibility of transform coding artifacts and superior perceived quality.

Automating Cell Detection and Classification in Human Brain Fluorescent Microscopy Images Using Dictionary Learning and Sparse Coding

PubMed Central

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A.; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M.

2017-01-01

Background Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. New method Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Results Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. Comparison with existing methods We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. Conclusion The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. PMID:28267565

Design of a Single-Cell Positioning Controller Using Electroosmotic Flow and Image Processing

PubMed Central

Ay, Chyung; Young, Chao-Wang; Chen, Jhong-Yin

2013-01-01

The objective of the current research was not only to provide a fast and automatic positioning platform for single cells, but also improved biomolecular manipulation techniques. In this study, an automatic platform for cell positioning using electroosmotic flow and image processing technology was designed. The platform was developed using a PCI image acquisition interface card for capturing images from a microscope and then transferring them to a computer using human-machine interface software. This software was designed by the Laboratory Virtual Instrument Engineering Workbench, a graphical language for finding cell positions and viewing the driving trace, and the fuzzy logic method for controlling the voltage or time of an electric field. After experiments on real human leukemic cells (U-937), the success of the cell positioning rate achieved by controlling the voltage factor reaches 100% within 5 s. A greater precision is obtained when controlling the time factor, whereby the success rate reaches 100% within 28 s. Advantages in both high speed and high precision are attained if these two voltage and time control methods are combined. The control speed with the combined method is about 5.18 times greater than that achieved by the time method, and the control precision with the combined method is more than five times greater than that achieved by the voltage method. PMID:23698272

[Automated analyser of organ cultured corneal endothelial mosaic].

PubMed

Gain, P; Thuret, G; Chiquet, C; Gavet, Y; Turc, P H; Théillère, C; Acquart, S; Le Petit, J C; Maugery, J; Campos, L

2002-05-01

Until now, organ-cultured corneal endothelial mosaic has been assessed in France by cell counting using a calibrated graticule, or by drawing cells on a computerized image. The former method is unsatisfactory because it is characterized by a lack of objective evaluation of the cell surface and hexagonality and it requires an experienced technician. The latter method is time-consuming and requires careful attention. We aimed to make an efficient, fast and easy to use, automated digital analyzer of video images of the corneal endothelium. The hardware included a PC Pentium III ((R)) 800 MHz-Ram 256, a Data Translation 3155 acquisition card, a Sony SC 75 CE CCD camera, and a 22-inch screen. Special functions for automated cell boundary determination consisted of Plug-in programs included in the ImageTool software. Calibration was performed using a calibrated micrometer. Cell densities of 40 organ-cultured corneas measured by both manual and automated counting were compared using parametric tests (Student's t test for paired variables and the Pearson correlation coefficient). All steps were considered more ergonomic i.e., endothelial image capture, image selection, thresholding of multiple areas of interest, automated cell count, automated detection of errors in cell boundary drawing, presentation of the results in an HTML file including the number of counted cells, cell density, coefficient of variation of cell area, cell surface histogram and cell hexagonality. The device was efficient because the global process lasted on average 7 minutes and did not require an experienced technician. The correlation between cell densities obtained with both methods was high (r=+0.84, p<0.001). The results showed an under-estimation using manual counting (2191+/-322 vs. 2273+/-457 cell/mm(2), p=0.046), compared with the automated method. Our automated endothelial cell analyzer is efficient and gives reliable results quickly and easily. A multicentric validation would allow us to standardize cell counts among cornea banks in our country.

General Staining and Segmentation Procedures for High Content Imaging and Analysis.

PubMed

Chambers, Kevin M; Mandavilli, Bhaskar S; Dolman, Nick J; Janes, Michael S

2018-01-01

Automated quantitative fluorescence microscopy, also known as high content imaging (HCI), is a rapidly growing analytical approach in cell biology. Because automated image analysis relies heavily on robust demarcation of cells and subcellular regions, reliable methods for labeling cells is a critical component of the HCI workflow. Labeling of cells for image segmentation is typically performed with fluorescent probes that bind DNA for nuclear-based cell demarcation or with those which react with proteins for image analysis based on whole cell staining. These reagents, along with instrument and software settings, play an important role in the successful segmentation of cells in a population for automated and quantitative image analysis. In this chapter, we describe standard procedures for labeling and image segmentation in both live and fixed cell samples. The chapter will also provide troubleshooting guidelines for some of the common problems associated with these aspects of HCI.

DeepPap: Deep Convolutional Networks for Cervical Cell Classification.

PubMed

Zhang, Ling; Le Lu; Nogues, Isabella; Summers, Ronald M; Liu, Shaoxiong; Yao, Jianhua

2017-11-01

Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells-without prior segmentation-based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pretrained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively resampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.

Supervised graph hashing for histopathology image retrieval and classification.

PubMed

Shi, Xiaoshuang; Xing, Fuyong; Xu, KaiDi; Xie, Yuanpu; Su, Hai; Yang, Lin

2017-12-01

In pathology image analysis, morphological characteristics of cells are critical to grade many diseases. With the development of cell detection and segmentation techniques, it is possible to extract cell-level information for further analysis in pathology images. However, it is challenging to conduct efficient analysis of cell-level information on a large-scale image dataset because each image usually contains hundreds or thousands of cells. In this paper, we propose a novel image retrieval based framework for large-scale pathology image analysis. For each image, we encode each cell into binary codes to generate image representation using a novel graph based hashing model and then conduct image retrieval by applying a group-to-group matching method to similarity measurement. In order to improve both computational efficiency and memory requirement, we further introduce matrix factorization into the hashing model for scalable image retrieval. The proposed framework is extensively validated with thousands of lung cancer images, and it achieves 97.98% classification accuracy and 97.50% retrieval precision with all cells of each query image used. Copyright © 2017 Elsevier B.V. All rights reserved.

Multiphoton luminescent graphene quantum dots for in vivo tracking of human adipose-derived stem cells

NASA Astrophysics Data System (ADS)

Kim, Jin; Song, Sung Ho; Jin, Yoonhee; Park, Hyun-Ji; Yoon, Hyewon; Jeon, Seokwoo; Cho, Seung-Woo

2016-04-01

The applicability of graphene quantum dots (GQDs) for the in vitro and in vivo live imaging and tracking of different types of human stem cells is investigated. GQDs synthesized by the modified graphite intercalated compound method show efficient cellular uptake with improved biocompatibility and highly sensitive optical properties, indicating their feasibility as a bio-imaging probe for stem cell therapy.The applicability of graphene quantum dots (GQDs) for the in vitro and in vivo live imaging and tracking of different types of human stem cells is investigated. GQDs synthesized by the modified graphite intercalated compound method show efficient cellular uptake with improved biocompatibility and highly sensitive optical properties, indicating their feasibility as a bio-imaging probe for stem cell therapy. Electronic supplementary information (ESI) available: Additional results. See DOI: 10.1039/c6nr02143c

An improved method for differentiating cell-bound from internalized particles by imaging flow cytometry.

PubMed

Smirnov, Asya; Solga, Michael D; Lannigan, Joanne; Criss, Alison K

2015-08-01

Recognition, binding, internalization, and elimination of pathogens and cell debris are important functions of professional as well as non-professional phagocytes. However, high-throughput methods for quantifying cell-associated particles and discriminating bound from internalized particles have been lacking. Here we describe a protocol for using imaging flow cytometry to quantify the attached and phagocytosed particles that are associated with a population of cells. Cells were exposed to fluorescent particles, fixed, and exposed to an antibody of a different fluorophore that recognizes the particles. The antibody is added without cell permeabilization, such that the antibody only binds extracellular particles. Cells with and without associated particles were identified by imaging flow cytometry. For each cell with associated particles, a spot count algorithm was employed to quantify the number of extracellular (double fluorescent) and intracellular (single fluorescent) particles per cell, from which the percent particle internalization was determined. The spot count algorithm was empirically validated by examining the fluorescence and phase contrast images acquired by the flow cytometer. We used this protocol to measure binding and internalization of the bacterium Neisseria gonorrhoeae by primary human neutrophils, using different bacterial variants and under different cellular conditions. The results acquired using imaging flow cytometry agreed with findings that were previously obtained using conventional immunofluorescence microscopy. This protocol provides a rapid, powerful method for measuring the association and internalization of any particle by any cell type. Copyright © 2015 Elsevier B.V. All rights reserved.

Segmentation and Quantitative Analysis of Apoptosis of Chinese Hamster Ovary Cells from Fluorescence Microscopy Images.

PubMed

Du, Yuncheng; Budman, Hector M; Duever, Thomas A

2017-06-01

Accurate and fast quantitative analysis of living cells from fluorescence microscopy images is useful for evaluating experimental outcomes and cell culture protocols. An algorithm is developed in this work to automatically segment and distinguish apoptotic cells from normal cells. The algorithm involves three steps consisting of two segmentation steps and a classification step. The segmentation steps are: (i) a coarse segmentation, combining a range filter with a marching square method, is used as a prefiltering step to provide the approximate positions of cells within a two-dimensional matrix used to store cells' images and the count of the number of cells for a given image; and (ii) a fine segmentation step using the Active Contours Without Edges method is applied to the boundaries of cells identified in the coarse segmentation step. Although this basic two-step approach provides accurate edges when the cells in a given image are sparsely distributed, the occurrence of clusters of cells in high cell density samples requires further processing. Hence, a novel algorithm for clusters is developed to identify the edges of cells within clusters and to approximate their morphological features. Based on the segmentation results, a support vector machine classifier that uses three morphological features: the mean value of pixel intensities in the cellular regions, the variance of pixel intensities in the vicinity of cell boundaries, and the lengths of the boundaries, is developed for distinguishing apoptotic cells from normal cells. The algorithm is shown to be efficient in terms of computational time, quantitative analysis, and differentiation accuracy, as compared with the use of the active contours method without the proposed preliminary coarse segmentation step.

In vivo imaging of the retinal pigment epithelial cells

NASA Astrophysics Data System (ADS)

Morgan, Jessica Ijams Wolfing

The retinal pigment epithelial (RPE) cells form an important layer of the retina because they are responsible for providing metabolic support to the photoreceptors. Techniques to image the RPE layer include autofluorescence imaging with a scanning laser ophthalmoscope (SLO). However, previous studies were unable to resolve single RPE cells in vivo. This thesis describes the technique of combining autofluorescence, SLO, adaptive optics (AO), and dual-wavelength simultaneous imaging and registration to visualize the individual cells in the RPE mosaic in human and primate retina for the first time in vivo. After imaging the RPE mosaic non-invasively, the cell layer's structure and regularity were characterized using quantitative metrics of cell density, spacing, and nearest neighbor distances. The RPE mosaic was compared to the cone mosaic, and RPE imaging methods were confirmed using histology. The ability to image the RPE mosaic led to the discovery of a novel retinal change following light exposure; 568 nm exposures caused an immediate reduction in autofluorescence followed by either full recovery or permanent damage in the RPE layer. A safety study was conducted to determine the range of exposure irradiances that caused permanent damage or transient autofluorescence reductions. Additionally, the threshold exposure causing autofluorescence reduction was determined and reciprocity of radiant exposure was confirmed. Light exposures delivered by the AOSLO were not significantly different than those delivered by a uniform source. As all exposures tested were near or below the permissible light levels of safety standards, this thesis provides evidence that the current light safety standards need to be revised. Finally, with the retinal damage and autofluorescence reduction thresholds identified, the methods of RPE imaging were modified to allow successful imaging of the individual cells in the RPE mosaic while still ensuring retinal safety. This thesis has provided a highly sensitive method for studying the in vivo morphology of individual RPE cells in normal, diseased, and damaged retinas. The methods presented here also will allow longitudinal studies for tracking disease progression and assessing treatment efficacy in human patients and animal models of retinal diseases affecting the RPE.

Synchrotron X-ray computed laminography of the three-dimensional anatomy of tomato leaves.

PubMed

Verboven, Pieter; Herremans, Els; Helfen, Lukas; Ho, Quang T; Abera, Metadel; Baumbach, Tilo; Wevers, Martine; Nicolaï, Bart M

2015-01-01

Synchrotron radiation computed laminography (SR-CL) is presented as an imaging method for analyzing the three-dimensional (3D) anatomy of leaves. The SR-CL method was used to provide 3D images of 1-mm² samples of intact leaves at a pixel resolution of 750 nm. The method allowed visualization and quantitative analysis of palisade and spongy mesophyll cells, and showed local venation patterns, aspects of xylem vascular structure and stomata. The method failed to image subcellular organelles such as chloroplasts. We constructed 3D computer models of leaves that can provide a basis for calculating gas exchange, light penetration and water and solute transport. The leaf anatomy of two different tomato genotypes grown in saturating light conditions was compared by 3D analysis. Differences were found in calculated values of tissue porosity, cell number density, cell area to volume ratio and cell volume and cell shape distributions of palisade and spongy cell layers. In contrast, the exposed cell area to leaf area ratio in mesophyll, a descriptor that correlates to the maximum rate of photosynthesis in saturated light conditions, was no different between spongy and palisade cells or between genotypes. The use of 3D image processing avoids many of the limitations of anatomical analysis with two-dimensional sections. © 2014 The Authors The Plant Journal © 2014 John Wiley & Sons Ltd.

Reduced background autofluorescence for cell imaging using nanodiamonds and lanthanide chelates.

PubMed

Cordina, Nicole M; Sayyadi, Nima; Parker, Lindsay M; Everest-Dass, Arun; Brown, Louise J; Packer, Nicolle H

2018-03-14

Bio-imaging is a key technique in tracking and monitoring important biological processes and fundamental biomolecular interactions, however the interference of background autofluorescence with targeted fluorophores is problematic for many bio-imaging applications. This study reports on two novel methods for reducing interference with cellular autofluorescence for bio-imaging. The first method uses fluorescent nanodiamonds (FNDs), containing nitrogen vacancy centers. FNDs emit at near-infrared wavelengths typically higher than most cellular autofluorescence; and when appropriately functionalized, can be used for background-free imaging of targeted biomolecules. The second method uses europium-chelating tags with long fluorescence lifetimes. These europium-chelating tags enhance background-free imaging due to the short fluorescent lifetimes of cellular autofluorescence. In this study, we used both methods to target E-selectin, a transmembrane glycoprotein that is activated by inflammation, to demonstrate background-free fluorescent staining in fixed endothelial cells. Our findings indicate that both FND and Europium based staining can improve fluorescent bio-imaging capabilities by reducing competition with cellular autofluorescence. 30 nm nanodiamonds coated with the E-selectin antibody was found to enable the most sensitive detective of E-selectin in inflamed cells, with a 40-fold increase in intensity detected.

Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images

PubMed Central

Yan, Pingkum; Zhou, Xiaobo; Shah, Mubarak; Wong, Stephen T. C.

2010-01-01

High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems. PMID:18270043

Method for observing phase objects without halos and directional shadows

NASA Astrophysics Data System (ADS)

Suzuki, Yoshimasa; Kajitani, Kazuo; Ohde, Hisashi

2015-03-01

A new microscopy method for observing phase objects without halos and directional shadows is proposed. The key optical element is an annular aperture at the front focal plane of a condenser with a larger diameter than those used in standard phase contrast microscopy. The light flux passing through the annular aperture is changed by the specimen's surface profile and then passes through an objective and contributes to image formation. This paper presents essential conditions for realizing the method. In this paper, images of colonies formed by induced pluripotent stem (iPS) cells using this method are compared with the conventional phase contrast method and the bright-field method when the NA of the illumination is small to identify differences among these techniques. The outlines of the iPS cells are clearly visible with this method, whereas they are not clearly visible due to halos when using the phase contrast method or due to weak contrast when using the bright-field method. Other images using this method are also presented to demonstrate a capacity of this method: a mouse ovum and superimposition of several different images of mouse iPS cells.

Cryo-image Analysis of Tumor Cell Migration, Invasion, and Dispersal in a Mouse Xenograft Model of Human Glioblastoma Multiforme

PubMed Central

Qutaish, Mohammed Q.; Sullivant, Kristin E.; Burden-Gulley, Susan M.; Lu, Hong; Roy, Debashish; Wang, Jing; Basilion, James P.; Brady-Kalnay, Susann M.; Wilson, David L.

2012-01-01

Purpose The goals of this study were to create cryo-imaging methods to quantify characteristics (size, dispersal, and blood vessel density) of mouse orthotopic models of glioblastoma multiforme (GBM) and to enable studies of tumor biology, targeted imaging agents, and theranostic nanoparticles. Procedures Green fluorescent protein-labeled, human glioma LN-229 cells were implanted into mouse brain. At 20–38 days, cryo-imaging gave whole brain, 4-GB, 3D microscopic images of bright field anatomy, including vasculature, and fluorescent tumor. Image analysis/visualization methods were developed. Results Vessel visualization and segmentation methods successfully enabled analyses. The main tumor mass volume, the number of dispersed clusters, the number of cells/cluster, and the percent dispersed volume all increase with age of the tumor. Histograms of dispersal distance give a mean and median of 63 and 56 μm, respectively, averaged over all brains. Dispersal distance tends to increase with age of the tumors. Dispersal tends to occur along blood vessels. Blood vessel density did not appear to increase in and around the tumor with this cell line. Conclusion Cryo-imaging and software allow, for the first time, 3D, whole brain, microscopic characterization of a tumor from a particular cell line. LN-229 exhibits considerable dispersal along blood vessels, a characteristic of human tumors that limits treatment success. PMID:22125093

Functionalization of titanium surface with chitosan via silanation: 3D CLSM imaging of cell biocompatibility behaviour.

PubMed

Attik, G N; D'Almeida, M; Toury, B; Grosgogeat, B

2013-09-16

Biocompatibility ranks as one of the most important properties of dental materials. One of the criteria for biocompatibility is the absence of material toxicity to cells, according to the ISO 7405 and 10993 recommendations. Among numerous available methods for toxicity assessment; 3-dimensional Confocal Laser Scanning Microscopy (3D CLSM) imaging was chosen because it provides an accurate and sensitive index of living cell behavior in contact with chitosan coated tested implants. The purpose of this study was to investigate the in vitro biocompatibility of functionalized titanium with chitosan via a silanation using sensitive and innovative 3D CLSM imaging as an investigation method for cytotoxicity assessment. The biocompatibility of four samples (controls cells, TA6V, TA6V-TESBA and TA6V-TESBAChitosan) was compared in vitro after 24h of exposure. Confocal imaging was performed on cultured human gingival fibroblast (HGF1) like cells using Live/Dead® staining. Image series were obtained with a FV10i confocal biological inverted system and analyzed with FV10-ASW 3.1 Software (Olympus France). Image analysis showed no cytotoxicity in the presence of the three tested substrates after 24 h of contact. A slight decrease of cell viability was found in contact with TA6V-TESBA with and without chitosan compared to negative control cells. Our findings highlighted the use of 3D CLSM confocal imaging as a sensitive method to evaluate qualitatively and quantitatively the biocompatibility behavior of functionalized titanium with chitosan via a silanation. The biocompatibility of the new functionalized coating to HGF1 cells is as good as the reference in biomedical device implantation TA6V.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

PubMed

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M; Grinberg, Lea T

2017-04-15

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

Onion cell imaging by using Talbot/self-imaging effect

NASA Astrophysics Data System (ADS)

Agarwal, Shilpi; Kumar, Varun; Shakher, Chandra

2017-08-01

This paper presents the amplitude and phase imaging of onion epidermis cell using the self-imaging capabilities of a grating (Talbot effect) in visible light region. In proposed method, the Fresnel diffraction pattern from the first grating and object is recorded at self-image plane. Fast Fourier Transform (FFT) is used for extracting the 3D amplitude and phase image of onion epidermis cell. The stability of the proposed system, from environmental perturbation as well as its compactness and portability give the proposed system a high potential for several clinical applications.

Non-interferometric quantitative phase imaging of yeast cells

NASA Astrophysics Data System (ADS)

Poola, Praveen K.; Pandiyan, Vimal Prabhu; John, Renu

2015-12-01

Real-time imaging of live cells is quite difficult without the addition of external contrast agents. Various methods for quantitative phase imaging of living cells have been proposed like digital holographic microscopy and diffraction phase microscopy. In this paper, we report theoretical and experimental results of quantitative phase imaging of live yeast cells with nanometric precision using transport of intensity equations (TIE). We demonstrate nanometric depth sensitivity in imaging live yeast cells using this technique. This technique being noninterferometric, does not need any coherent light sources and images can be captured through a regular bright-field microscope. This real-time imaging technique would deliver the depth or 3-D volume information of cells and is highly promising in real-time digital pathology applications, screening of pathogens and staging of diseases like malaria as it does not need any preprocessing of samples.

Platinum blue staining of cells grown in electrospun scaffolds.

PubMed

Yusuf, Mohammed; Millas, Ana Luiza G; Estandarte, Ana Katrina C; Bhella, Gurdeep K; McKean, Robert; Bittencourt, Edison; Robinson, Ian K

2014-01-01

Fibroblast cells grown in electrospun polymer scaffolds were stained with platinum blue, a heavy metal stain, and imaged using scanning electron microscopy. Good contrast on the cells was achieved compared with samples that were gold sputter coated. The cell morphology could be clearly observed, and the cells could be distinguished from the scaffold fibers. Here we optimized the required concentration of platinum blue for imaging cells grown in scaffolds and show that a higher concentration causes platinum aggregation. Overall, platinum blue is a useful stain for imaging cells because of its enhanced contrast using scanning electron microscopy (SEM). In the future it would be useful to investigate cell growth and morphology using three-dimensional imaging methods.

3D nanoscale imaging of the yeast, Schizosaccharomyces pombe, by full-field transmission x-ray microscopy at 5.4 keV

PubMed Central

Chen, Jie; Yang, Yunhao; Zhang, Xiaobo; Andrews, Joy C.; Pianetta, Piero; Guan, Yong; Liu, Gang; Xiong, Ying; Wu, Ziyu; Tian, Yangchao

2010-01-01

Three-dimensional (3D) nanoscale structures of the fission yeast, Schizosaccharomyces pombe, can be obtained by full-field transmission hard x-ray microscopy with 30 nm resolution using synchrotron radiation sources. Sample preparation is relatively simple and the samples are portable across various imaging environments, allowing for high throughput sample screening. The yeast cells were fixed and double stained with Reynold’s lead citrate and uranyl acetate. We performed both absorption contrast and Zernike phase contrast imaging on these cells in order to test this method. The membranes, nucleus and subcellular organelles of the cells were clearly visualized using absorption contrast mode. The x-ray images of the cells could be used to study the spatial distributions of the organelles in the cells. These results show unique structural information, demonstrating that hard x-ray microscopy is a complementary method for imaging and analyzing biological samples. PMID:20349228

Improved Serial Sectioning Techniques for Correlative Light-Electron Microscopy Mapping of Human Langerhans Islets

PubMed Central

Saitoh, Sei; Ohno, Nobuhiko; Saitoh, Yurika; Terada, Nobuo; Shimo, Satoshi; Aida, Kaoru; Fujii, Hideki; Kobayashi, Tetsuro; Ohno, Shinichi

2018-01-01

Combined analysis of immunostaining for various biological molecules coupled with investigations of ultrastructural features of individual cells is a powerful approach for studies of cellular functions in normal and pathological conditions. However, weak antigenicity of tissues fixed by conventional methods poses a problem for immunoassays. This study introduces a method of correlative light and electron microscopy imaging of the same endocrine cells of compact and diffuse islets from human pancreatic tissue specimens. The method utilizes serial sections obtained from Epon-embedded specimens fixed with glutaraldehyde and osmium tetroxide. Double-immunofluorescence staining of thick Epon sections for endocrine hormones (insulin and glucagon) and regenerating islet-derived gene 1 α (REG1α) was performed following the removal of Epoxy resin with sodium ethoxide, antigen retrieval by autoclaving, and de-osmification treatment with hydrogen peroxide. The immunofluorescence images of endocrine cells were superimposed with the electron microscopy images of the same cells obtained from serial ultrathin sections. Immunofluorescence images showed well-preserved secretory granules in endocrine cells, whereas electron microscopy observations demonstrated corresponding secretory granules and intracellular organelles in the same cells. In conclusion, the correlative imaging approach developed by us may be useful for examining ultrastructural features in combination with immunolocalisation of endocrine hormones in the same human pancreatic islets. PMID:29622846

Seeing the forest and trees: whole-body and whole-brain imaging for circadian biology.

PubMed

Ode, K L; Ueda, H R

2015-09-01

Recent advances in methods for making mammalian organs translucent have made possible whole-body fluorescent imaging with single-cell resolution. Because organ-clearing methods can be used to image the heterogeneous nature of cell populations, they are powerful tools to investigate the hierarchical organization of the cellular circadian clock, and how the clock synchronizes a variety of physiological activities. In particular, methods compatible with genetically encoded fluorescent reporters have the potential to detect circadian activity in different brain regions and the circadian-phase distribution across the whole body. In this review, we summarize the current methods and strategy for making organs translucent (removal of lipids, decolourization of haemoglobin and adjusting the refractive index of the specimen). We then discuss possible applications to circadian biology. For example, the coupling of circadian rhythms among different brain regions, brain activity in sleep-wake cycles and the role of migrating cells such as immune cells and cancer cells in chronopharmacology. © 2015 John Wiley & Sons Ltd.

Automatic choroid cells segmentation and counting based on approximate convexity and concavity of chain code in fluorescence microscopic image

NASA Astrophysics Data System (ADS)

Lu, Weihua; Chen, Xinjian; Zhu, Weifang; Yang, Lei; Cao, Zhaoyuan; Chen, Haoyu

2015-03-01

In this paper, we proposed a method based on the Freeman chain code to segment and count rhesus choroid-retinal vascular endothelial cells (RF/6A) automatically for fluorescence microscopy images. The proposed method consists of four main steps. First, a threshold filter and morphological transform were applied to reduce the noise. Second, the boundary information was used to generate the Freeman chain codes. Third, the concave points were found based on the relationship between the difference of the chain code and the curvature. Finally, cells segmentation and counting were completed based on the characteristics of the number of the concave points, the area and shape of the cells. The proposed method was tested on 100 fluorescence microscopic cell images, and the average true positive rate (TPR) is 98.13% and the average false positive rate (FPR) is 4.47%, respectively. The preliminary results showed the feasibility and efficiency of the proposed method.

A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching

PubMed Central

Chen, Cheng; Wang, Wei; Ozolek, John A.; Rohde, Gustavo K.

2013-01-01

We describe a new supervised learning-based template matching approach for segmenting cell nuclei from microscopy images. The method uses examples selected by a user for building a statistical model which captures the texture and shape variations of the nuclear structures from a given dataset to be segmented. Segmentation of subsequent, unlabeled, images is then performed by finding the model instance that best matches (in the normalized cross correlation sense) local neighborhood in the input image. We demonstrate the application of our method to segmenting nuclei from a variety of imaging modalities, and quantitatively compare our results to several other methods. Quantitative results using both simulated and real image data show that, while certain methods may work well for certain imaging modalities, our software is able to obtain high accuracy across several imaging modalities studied. Results also demonstrate that, relative to several existing methods, the template-based method we propose presents increased robustness in the sense of better handling variations in illumination, variations in texture from different imaging modalities, providing more smooth and accurate segmentation borders, as well as handling better cluttered nuclei. PMID:23568787

Non-protein thiol imaging and quantification in live cells with a novel benzofurazan sulfide triphenylphosphonium fluorogenic compound.

PubMed

Yang, Yang; Guan, Xiangming

2017-05-01

Thiols (-SH) play various roles in biological systems. They are divided into protein thiols (PSH) and non-protein thiols (NPSH). Due to the significant roles thiols play in various physiological/pathological functions, numerous analytical methods have been developed for thiol assays. Most of these methods are developed for glutathione, the major form of NPSH. Majority of these methods require tissue/cell homogenization before analysis. Due to a lack of effective thiol-specific fluorescent/fluorogenic reagents, methods for imaging and quantifying thiols in live cells are limited. Determination of an analyte in live cells can reveal information that cannot be revealed by analysis of cell homogenates. Previously, we reported a thiol-specific thiol-sulfide exchange reaction. Based on this reaction, a benzofurazan sulfide thiol-specific fluorogenic reagent was developed. The reagent was able to effectively image and quantify total thiols (PSH+NPSH) in live cells through fluorescence microscopy. The reagent was later named as GUALY's reagent. Here we would like to report an extension of the work by synthesizing a novel benzofurazan sulfide triphenylphosphonium derivative [(((7,7'-thiobis(benzo[c][1,2,5]oxadiazole-4,4'-sulfonyl))bis(methylazanediyl))bis(butane-4,1-diyl))bis(triphenylphosphonium) (TBOP)]. Like GUALY's reagent, TBOP is a thiol-specific fluorogenic agent that is non-fluorescent but forms fluorescent thiol adducts in a thiol-specific fashion. Different than GUALY's reagent, TBOP reacts only with NPSH but not with PSH. TBOP was effectively used to image and quantify NPSH in live cells using fluorescence microscopy. TBOP is a complementary reagent to GUALY's reagent in determining the roles of PSH, NPSH, and total thiols in thiol-related physiological/pathological functions in live cells through fluorescence microscopy. Graphical Abstract Live cell imaging and quantification of non-protein thiols by TBOP.

Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition.

PubMed

Mathew, B; Schmitz, A; Muñoz-Descalzo, S; Ansari, N; Pampaloni, F; Stelzer, E H K; Fischer, S C

2015-06-08

Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset. We present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91%) whereas the other methods each failed for at least one dataset (F-measure≤69%). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values. We developed a novel and fully automated three-dimensional cell nuclei segmentation method incorporating LoS decomposition. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity. Our method showed superior performance compared to state-of-the-art methods, performing accurately for a variety of test images. Hence, our LoS approach can be readily applied to quantitative evaluation in drug testing, developmental and cell biology.

Hyperspectral microscope imaging methods to classify gram-positive and gram-negative foodborne pathogenic bacteria

USDA-ARS?s Scientific Manuscript database

An acousto-optic tunable filter-based hyperspectral microscope imaging method has potential for identification of foodborne pathogenic bacteria from microcolony rapidly with a single cell level. We have successfully developed the method to acquire quality hyperspectral microscopic images from variou...

Holographic intravital microscopy for 2-D and 3-D imaging intact circulating blood cells in microcapillaries of live mice

NASA Astrophysics Data System (ADS)

Kim, Kyoohyun; Choe, Kibaek; Park, Inwon; Kim, Pilhan; Park, Yongkeun

2016-09-01

Intravital microscopy is an essential tool that reveals behaviours of live cells under conditions close to natural physiological states. So far, although various approaches for imaging cells in vivo have been proposed, most require the use of labelling and also provide only qualitative imaging information. Holographic imaging approach based on measuring the refractive index distributions of cells, however, circumvent these problems and offer quantitative and label-free imaging capability. Here, we demonstrate in vivo two- and three-dimensional holographic imaging of circulating blood cells in intact microcapillaries of live mice. The measured refractive index distributions of blood cells provide morphological and biochemical properties including three-dimensional cell shape, haemoglobin concentration, and haemoglobin contents at the individual cell level. With the present method, alterations in blood flow dynamics in live healthy and sepsis-model mice were also investigated.

Single-cell photoacoustic thermometry

PubMed Central

Gao, Liang; Wang, Lidai; Li, Chiye; Liu, Yan; Ke, Haixin; Zhang, Chi

2013-01-01

Abstract. A novel photoacoustic thermometric method is presented for simultaneously imaging cells and sensing their temperature. With three-seconds-per-frame imaging speed, a temperature resolution of 0.2°C was achieved in a photo-thermal cell heating experiment. Compared to other approaches, the photoacoustic thermometric method has the advantage of not requiring custom-developed temperature-sensitive biosensors. This feature should facilitate the conversion of single-cell thermometry into a routine lab tool and make it accessible to a much broader biological research community. PMID:23377004

Note: An automated image analysis method for high-throughput classification of surface-bound bacterial cell motions.

PubMed

Shen, Simon; Syal, Karan; Tao, Nongjian; Wang, Shaopeng

2015-12-01

We present a Single-Cell Motion Characterization System (SiCMoCS) to automatically extract bacterial cell morphological features from microscope images and use those features to automatically classify cell motion for rod shaped motile bacterial cells. In some imaging based studies, bacteria cells need to be attached to the surface for time-lapse observation of cellular processes such as cell membrane-protein interactions and membrane elasticity. These studies often generate large volumes of images. Extracting accurate bacterial cell morphology features from these images is critical for quantitative assessment. Using SiCMoCS, we demonstrated simultaneous and automated motion tracking and classification of hundreds of individual cells in an image sequence of several hundred frames. This is a significant improvement from traditional manual and semi-automated approaches to segmenting bacterial cells based on empirical thresholds, and a first attempt to automatically classify bacterial motion types for motile rod shaped bacterial cells, which enables rapid and quantitative analysis of various types of bacterial motion.

Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.

PubMed

Keskin, Furkan; Suhre, Alexander; Kose, Kivanc; Ersahin, Tulin; Cetin, A Enis; Cetin-Atalay, Rengul

2013-01-01

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.

Image Classification of Human Carcinoma Cells Using Complex Wavelet-Based Covariance Descriptors

PubMed Central

Keskin, Furkan; Suhre, Alexander; Kose, Kivanc; Ersahin, Tulin; Cetin, A. Enis; Cetin-Atalay, Rengul

2013-01-01

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-WT) coefficients and several morphological attributes are computed. Directionally selective DT-WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html. PMID:23341908

Detection of viability of micro-algae cells by optofluidic hologram pattern.

PubMed

Wang, Junsheng; Yu, Xiaomei; Wang, Yanjuan; Pan, Xinxiang; Li, Dongqing

2018-03-01

A rapid detection of micro-algae activity is critical for analysis of ship ballast water. A new method for detecting micro-algae activity based on lens-free optofluidic holographic imaging is presented in this paper. A compact lens-free optofluidic holographic imaging device was developed. This device is mainly composed of a light source, a small through-hole, a light propagation module, a microfluidic chip, and an image acquisition and processing module. The excited light from the light source passes through a small hole to reach the surface of the micro-algae cells in the microfluidic chip, and a holographic image is formed by the diffraction light of surface of micro-algae cells. The relation between the characteristics in the hologram pattern and the activity of micro-algae cells was investigated by using this device. The characteristics of the hologram pattern were extracted to represent the activity of micro-algae cells. To demonstrate the accuracy of the presented method and device, four species of micro-algae cells were employed as the test samples and the comparison experiments between the alive and dead cells of four species of micro-algae were conducted. The results show that the developed method and device can determine live/dead microalgae cells accurately.

Adult stem cell lineage tracing and deep tissue imaging

PubMed Central

Fink, Juergen; Andersson-Rolf, Amanda; Koo, Bon-Kyoung

2015-01-01

Lineage tracing is a widely used method for understanding cellular dynamics in multicellular organisms during processes such as development, adult tissue maintenance, injury repair and tumorigenesis. Advances in tracing or tracking methods, from light microscopy-based live cell tracking to fluorescent label-tracing with two-photon microscopy, together with emerging tissue clearing strategies and intravital imaging approaches have enabled scientists to decipher adult stem and progenitor cell properties in various tissues and in a wide variety of biological processes. Although technical advances have enabled time-controlled genetic labeling and simultaneous live imaging, a number of obstacles still need to be overcome. In this review, we aim to provide an in-depth description of the traditional use of lineage tracing as well as current strategies and upcoming new methods of labeling and imaging. [BMB Reports 2015; 48(12): 655-667] PMID:26634741

Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue

PubMed Central

Bjornsson, Christopher S; Lin, Gang; Al-Kofahi, Yousef; Narayanaswamy, Arunachalam; Smith, Karen L; Shain, William; Roysam, Badrinath

2009-01-01

Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic ‘divide and conquer’ methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick (~100 μm) slices of rat brain tissue were labeled using 3 – 5 fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81–92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system. PMID:18294697

Label-free DNA imaging in vivo with stimulated Raman scattering microscopy

PubMed Central

Lu, Fa-Ke; Basu, Srinjan; Igras, Vivien; Hoang, Mai P.; Ji, Minbiao; Fu, Dan; Holtom, Gary R.; Neel, Victor A.; Freudiger, Christian W.; Fisher, David E.; Xie, X. Sunney

2015-01-01

Label-free DNA imaging is highly desirable in biology and medicine to perform live imaging without affecting cell function and to obtain instant histological tissue examination during surgical procedures. Here we show a label-free DNA imaging method with stimulated Raman scattering (SRS) microscopy for visualization of the cell nuclei in live animals and intact fresh human tissues with subcellular resolution. Relying on the distinct Raman spectral features of the carbon-hydrogen bonds in DNA, the distribution of DNA is retrieved from the strong background of proteins and lipids by linear decomposition of SRS images at three optimally selected Raman shifts. Based on changes on DNA condensation in the nucleus, we were able to capture chromosome dynamics during cell division both in vitro and in vivo. We tracked mouse skin cell proliferation, induced by drug treatment, through in vivo counting of the mitotic rate. Furthermore, we demonstrated a label-free histology method for human skin cancer diagnosis that provides comparable results to other conventional tissue staining methods such as H&E. Our approach exhibits higher sensitivity than SRS imaging of DNA in the fingerprint spectral region. Compared with spontaneous Raman imaging of DNA, our approach is three orders of magnitude faster, allowing both chromatin dynamic studies and label-free optical histology in real time. PMID:26324899

Live Imaging of Cell Motility and Actin Cytoskeleton of Individual Neurons and Neural Crest Cells in Zebrafish Embryos

PubMed Central

Andersen, Erica; Asuri, Namrata; Clay, Matthew; Halloran, Mary

2010-01-01

The zebrafish is an ideal model for imaging cell behaviors during development in vivo. Zebrafish embryos are externally fertilized and thus easily accessible at all stages of development. Moreover, their optical clarity allows high resolution imaging of cell and molecular dynamics in the natural environment of the intact embryo. We are using a live imaging approach to analyze cell behaviors during neural crest cell migration and the outgrowth and guidance of neuronal axons. Live imaging is particularly useful for understanding mechanisms that regulate cell motility processes. To visualize details of cell motility, such as protrusive activity and molecular dynamics, it is advantageous to label individual cells. In zebrafish, plasmid DNA injection yields a transient mosaic expression pattern and offers distinct benefits over other cell labeling methods. For example, transgenic lines often label entire cell populations and thus may obscure visualization of the fine protrusions (or changes in molecular distribution) in a single cell. In addition, injection of DNA at the one-cell stage is less invasive and more precise than dye injections at later stages. Here we describe a method for labeling individual developing neurons or neural crest cells and imaging their behavior in vivo. We inject plasmid DNA into 1-cell stage embryos, which results in mosaic transgene expression. The vectors contain cell-specific promoters that drive expression of a gene of interest in a subset of sensory neurons or neural crest cells. We provide examples of cells labeled with membrane targeted GFP or with a biosensor probe that allows visualization of F-actin in living cells1. Erica Andersen, Namrata Asuri, and Matthew Clay contributed equally to this work. PMID:20130524

Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images.

PubMed

Jaccard, Nicolas; Griffin, Lewis D; Keser, Ana; Macown, Rhys J; Super, Alexandre; Veraitch, Farlan S; Szita, Nicolas

2014-03-01

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non-invasive determination of these characteristics. We present an image-processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source-code for MATLAB and ImageJ is freely available under a permissive open-source license. © 2013 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

High-throughput microfluidic line scan imaging for cytological characterization

NASA Astrophysics Data System (ADS)

Hutcheson, Joshua A.; Powless, Amy J.; Majid, Aneeka A.; Claycomb, Adair; Fritsch, Ingrid; Balachandran, Kartik; Muldoon, Timothy J.

2015-03-01

Imaging cells in a microfluidic chamber with an area scan camera is difficult due to motion blur and data loss during frame readout causing discontinuity of data acquisition as cells move at relatively high speeds through the chamber. We have developed a method to continuously acquire high-resolution images of cells in motion through a microfluidics chamber using a high-speed line scan camera. The sensor acquires images in a line-by-line fashion in order to continuously image moving objects without motion blur. The optical setup comprises an epi-illuminated microscope with a 40X oil immersion, 1.4 NA objective and a 150 mm tube lens focused on a microfluidic channel. Samples containing suspended cells fluorescently stained with 0.01% (w/v) proflavine in saline are introduced into the microfluidics chamber via a syringe pump; illumination is provided by a blue LED (455 nm). Images were taken of samples at the focal plane using an ELiiXA+ 8k/4k monochrome line-scan camera at a line rate of up to 40 kHz. The system's line rate and fluid velocity are tightly controlled to reduce image distortion and are validated using fluorescent microspheres. Image acquisition was controlled via MATLAB's Image Acquisition toolbox. Data sets comprise discrete images of every detectable cell which may be subsequently mined for morphological statistics and definable features by a custom texture analysis algorithm. This high-throughput screening method, comparable to cell counting by flow cytometry, provided efficient examination including counting, classification, and differentiation of saliva, blood, and cultured human cancer cells.

Automated Method for the Rapid and Precise Estimation of Adherent Cell Culture Characteristics from Phase Contrast Microscopy Images

PubMed Central

Jaccard, Nicolas; Griffin, Lewis D; Keser, Ana; Macown, Rhys J; Super, Alexandre; Veraitch, Farlan S; Szita, Nicolas

2014-01-01

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non-invasive determination of these characteristics. We present an image-processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source-code for MATLAB and ImageJ is freely available under a permissive open-source license. Biotechnol. Bioeng. 2014;111: 504–517. © 2013 Wiley Periodicals, Inc. PMID:24037521

Quantitative visualization of synchronized insulin secretion from 3D-cultured cells

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

Suzuki, Takahiro; Kanamori, Takao; Inouye, Satoshi

Quantitative visualization of synchronized insulin secretion was performed in an isolated rat pancreatic islet and a spheroid of rat pancreatic beta cell line using a method of video-rate bioluminescence imaging. Video-rate images of insulin secretion from 3D-cultured cells were obtained by expressing the fusion protein of insulin and Gaussia luciferase (Insulin-GLase). A subclonal rat INS-1E cell line stably expressing Insulin-GLase, named iGL, was established and a cluster of iGL cells showed oscillatory insulin secretion that was completely synchronized in response to high glucose. Furthermore, we demonstrated the effect of an antidiabetic drug, glibenclamide, on synchronized insulin secretion from 2D- andmore » 3D-cultured iGL cells. The amount of secreted Insulin-GLase from iGL cells was also determined by a luminometer. Thus, our bioluminescence imaging method could generally be used for investigating protein secretion from living 3D-cultured cells. In addition, iGL cell line would be valuable for evaluating antidiabetic drugs. - Highlights: • An imaging method for protein secretion from 3D-cultured cells was established. • The fused protein of insulin to GLase, Insulin-GLase, was used as a reporter. • Synchronous insulin secretion was visualized in rat islets and spheroidal beta cells. • A rat beta cell line stably expressing Insulin-GLase, named iGL, was established. • Effect of an antidiabetic drug on insulin secretion was visualized in iGL cells.« less

Visual Estimation of Bacterial Growth Level in Microfluidic Culture Systems.

PubMed

Kim, Kyukwang; Kim, Seunggyu; Jeon, Jessie S

2018-02-03

Microfluidic devices are an emerging platform for a variety of experiments involving bacterial cell culture, and has advantages including cost and convenience. One inevitable step during bacterial cell culture is the measurement of cell concentration in the channel. The optical density measurement technique is generally used for bacterial growth estimation, but it is not applicable to microfluidic devices due to the small sample volumes in microfluidics. Alternately, cell counting or colony-forming unit methods may be applied, but these do not work in situ; nor do these methods show measurement results immediately. To this end, we present a new vision-based method to estimate the growth level of the bacteria in microfluidic channels. We use Fast Fourier transform (FFT) to detect the frequency level change of the microscopic image, focusing on the fact that the microscopic image becomes rough as the number of cells in the field of view increases, adding high frequencies to the spectrum of the image. Two types of microfluidic devices are used to culture bacteria in liquid and agar gel medium, and time-lapsed images are captured. The images obtained are analyzed using FFT, resulting in an increase in high-frequency noise proportional to the time passed. Furthermore, we apply the developed method in the microfluidic antibiotics susceptibility test by recognizing the regional concentration change of the bacteria that are cultured in the antibiotics gradient. Finally, a deep learning-based data regression is performed on the data obtained by the proposed vision-based method for robust reporting of data.

Measurement of RBC agglutination with microscopic cell image analysis in a microchannel chip.

PubMed

Cho, Chi Hyun; Kim, Ju Yeon; Nyeck, Agnes E; Lim, Chae Seung; Hur, Dae Sung; Chung, Chanil; Chang, Jun Keun; An, Seong Soo A; Shin, Sehyun

2014-01-01

Since Landsteiner's discovery of ABO blood groups, RBC agglutination has been one of the most important immunohematologic techniques for ABO and RhD blood groupings. The conventional RBC agglutination grading system for RhD blood typings relies on macroscopic reading, followed by the assignment of a grade ranging from (-) to (4+) to the degree of red blood cells clumping. However, with the new scoring method introduced in this report, microscopically captured cell images of agglutinated RBCs, placed in a microchannel chip, are used for analysis. Indeed, the cell images' pixel number first allows the differentiation of agglutinated and non-agglutinated red blood cells. Finally, the ratio of agglutinated RBCs per total RBC counts (CRAT) from 90 captured images is then calculated. During the trial, it was observed that the agglutinated group's CRAT was significantly higher (3.77-0.003) than that of the normal control (0). Based on these facts, it was established that the microchannel method was more suitable for the discrimination between agglutinated RBCs and non-agglutinated RhD negative, and thus more reliable for the grading of RBCs agglutination than the conventional method.

Ptychography: use of quantitative phase information for high-contrast label free time-lapse imaging of living cells

NASA Astrophysics Data System (ADS)

Suman, Rakesh; O'Toole, Peter

2014-03-01

Here we report a novel label free, high contrast and quantitative method for imaging live cells. The technique reconstructs an image from overlapping diffraction patterns using a ptychographical algorithm. The algorithm utilises both amplitude and phase data from the sample to report on quantitative changes related to the refractive index (RI) and thickness of the specimen. We report the ability of this technique to generate high contrast images, to visualise neurite elongation in neuronal cells, and to provide measure of cell proliferation.

Joint level-set and spatio-temporal motion detection for cell segmentation.

PubMed

Boukari, Fatima; Makrogiannis, Sokratis

2016-08-10

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images. In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result. We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques, and 4 % compared to the nonlinear spatio-temporal diffusion method. Despite the wide variation in cell shape, density, mitotic events, and image quality among the datasets, our proposed method produced promising segmentation results. These results indicate the efficiency and robustness of this method especially for mitotic events and low SNR imaging, enabling the application of subsequent quantification tasks.

Imaging modalities for the in vivo surveillance of mesenchymal stromal cells.

PubMed

Hossain, Mohammad Ayaz; Chowdhury, Tina; Bagul, Atul

2015-11-01

Bone marrow stromal cells exist as mesenchymal stromal cells (MSCs) and have the capacity to differentiate into multiple tissue types when subjected to appropriate culture conditions. This property of MSCs creates therapeutic opportunities in regenerative medicine for the treatment of damage to neural, cardiac and musculoskeletal tissues or acute kidney injury. The prerequisite for successful cell therapy is delivery of cells to the target tissue. Assessment of therapeutic outcomes utilize traditional methods to examine cell function of MSC populations involving routine biochemical or histological analysis for cell proliferation, protein synthesis and gene expression. However, these methods do not provide sufficient spatial and temporal information. In vivo surveillance of MSC migration to the site of interest can be performed through a variety of imaging modalities such as the use of radiolabelling, fluc protein expression bioluminescence imaging and paramagnetic nanoparticle magnetic resonance imaging. This review will outline the current methods of in vivo surveillance of exogenously administered MSCs in regenerative medicine while addressing potential technological developments. Furthermore, nanoparticles and microparticles for cellular labelling have shown that migration of MSCs can be spatially and temporally monitored. In vivo surveillance therefore permits time-stratified assessment in animal models without disruption of the target organ. In vivo tracking of MSCs is non-invasive, repeatable and non-toxic. Despite the excitement that nanoparticles for tracking MSCs offer, delivery methods are difficult because of the challenges with imaging three-dimensional systems. The current advances and growth in MSC research, is likely to provide a wealth of evidence overcoming these issues. Copyright © 2014 John Wiley & Sons, Ltd.

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.

PubMed

Joutsijoki, Henry; Haponen, Markus; Rasku, Jyrki; Aalto-Setälä, Katriina; Juhola, Martti

2016-01-01

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies.

Region-based multifocus image fusion for the precise acquisition of Pap smear images.

PubMed

Tello-Mijares, Santiago; Bescós, Jesús

2018-05-01

A multifocus image fusion method to obtain a single focused image from a sequence of microscopic high-magnification Papanicolau source (Pap smear) images is presented. These images, captured each in a different position of the microscope lens, frequently show partially focused cells or parts of cells, which makes them unpractical for the direct application of image analysis techniques. The proposed method obtains a focused image with a high preservation of original pixels information while achieving a negligible visibility of the fusion artifacts. The method starts by identifying the best-focused image of the sequence; then, it performs a mean-shift segmentation over this image; the focus level of the segmented regions is evaluated in all the images of the sequence, and best-focused regions are merged in a single combined image; finally, this image is processed with an adaptive artifact removal process. The combination of a region-oriented approach, instead of block-based approaches, and a minimum modification of the value of focused pixels in the original images achieve a highly contrasted image with no visible artifacts, which makes this method especially convenient for the medical imaging domain. The proposed method is compared with several state-of-the-art alternatives over a representative dataset. The experimental results show that our proposal obtains the best and more stable quality indicators. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

A novel concentration and viability detection method for Brettanomyces using the Cellometer image cytometry.

PubMed

Martyniak, Brian; Bolton, Jason; Kuksin, Dmitry; Shahin, Suzanne M; Chan, Leo Li-Ying

2017-01-01

Brettanomyces spp. can present unique cell morphologies comprised of excessive pseudohyphae and budding, leading to difficulties in enumerating cells. The current cell counting methods include manual counting of methylene blue-stained yeasts or measuring optical densities using a spectrophotometer. However, manual counting can be time-consuming and has high operator-dependent variations due to subjectivity. Optical density measurement can also introduce uncertainties where instead of individual cells counted, an average of a cell population is measured. In contrast, by utilizing the fluorescence capability of an image cytometer to detect acridine orange and propidium iodide viability dyes, individual cell nuclei can be counted directly in the pseudohyphae chains, which can improve the accuracy and efficiency of cell counting, as well as eliminating the subjectivity from manual counting. In this work, two experiments were performed to demonstrate the capability of Cellometer image cytometer to monitor Brettanomyces concentrations, viabilities, and budding/pseudohyphae percentages. First, a yeast propagation experiment was conducted to optimize software counting parameters for monitoring the growth of Brettanomyces clausenii, Brettanomyces bruxellensis, and Brettanomyces lambicus, which showed increasing cell concentrations, and varying pseudohyphae percentages. The pseudohyphae formed during propagation were counted either as multiple nuclei or a single multi-nuclei organism, where the results of counting the yeast as a single multi-nuclei organism were directly compared to manual counting. Second, a yeast fermentation experiment was conducted to demonstrate that the proposed image cytometric analysis method can monitor the growth pattern of B. lambicus and B. clausenii during beer fermentation. The results from both experiments displayed different growth patterns, viability, and budding/pseudohyphae percentages for each Brettanomyces species. The proposed Cellometer image cytometry method can improve efficiency and eliminate operator-dependent variations of cell counting compared with the traditional methods, which can potentially improve the quality of beverage products employing Brettanomyces yeasts.

Exploring the Leishmania Hydrophilic Acylated Surface Protein B (HASPB) Export Pathway by Live Cell Imaging Methods.

PubMed

MacLean, Lorna; Price, Helen; O'Toole, Peter

2016-01-01

Leishmania major is a human-infective protozoan parasite transmitted by the bite of the female phlebotomine sand fly. The L. major hydrophilic acylated surface protein B (HASPB) is only expressed in infective parasite stages suggesting a role in parasite virulence. HASPB is a "nonclassically" secreted protein that lacks a conventional signal peptide, reaching the cell surface by an alternative route to the classical ER-Golgi pathway. Instead HASPB trafficking to and exposure on the parasite plasma membrane requires dual N-terminal acylation. Here, we use live cell imaging methods to further explore this pathway allowing visualization of key events in real time at the individual cell level. These methods include live cell imaging using fluorescent reporters to determine the subcellular localization of wild type and acylation site mutation HASPB18-GFP fusion proteins, fluorescence recovery after photobleaching (FRAP) to analyze the dynamics of HASPB in live cells, and live antibody staining to detect surface exposure of HASPB by confocal microscopy.

Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images.

PubMed

Nateghi, Ramin; Danyali, Habibollah; Helfroush, Mohammad Sadegh

2017-08-14

Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. This task is very difficult and time-consuming even for experts. In this paper, a fully automated method is presented for accurate detection of mitosis cells in histopathology slide images. First a method based on maximum-likelihood is employed for segmentation and extraction of mitosis cell. Then a novel Maximized Inter-class Weighted Mean (MIWM) method is proposed that aims at reducing the number of extracted non-mitosis candidates that results in reducing the false positive mitosis detection rate. Finally, segmented candidates are classified into mitosis and non-mitosis classes by using a support vector machine (SVM) classifier. Experimental results demonstrate a significant improvement in accuracy of mitosis cells detection in different grades of breast cancer histopathological images.

Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information.

PubMed

Wang, Y; Wang, C; Zhang, Z

2018-05-01

Automated cell segmentation plays a key role in characterisations of cell behaviours for both biology research and clinical practices. Currently, the segmentation of clustered cells still remains as a challenge and is the main reason for false segmentation. In this study, the emphasis was put on the segmentation of clustered cells in negative phase contrast images. A new method was proposed to combine both light intensity and cell shape information through the construction of grey-weighted distance transform (GWDT) within preliminarily segmented areas. With the constructed GWDT, the clustered cells can be detected and then separated with a modified region skeleton-based method. Moreover, a contour expansion operation was applied to get optimised detection of cell boundaries. In this paper, the working principle and detailed procedure of the proposed method are described, followed by the evaluation of the method on clustered cell segmentation. Results show that the proposed method achieves an improved performance in clustered cell segmentation compared with other methods, with 85.8% and 97.16% accuracy rate for clustered cells and all cells, respectively. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

Quantification of epithelial cells in coculture with fibroblasts by fluorescence image analysis.

PubMed

Krtolica, Ana; Ortiz de Solorzano, Carlos; Lockett, Stephen; Campisi, Judith

2002-10-01

To demonstrate that senescent fibroblasts stimulate the proliferation and neoplastic transformation of premalignant epithelial cells (Krtolica et al.: Proc Natl Acad Sci USA 98:12072-12077, 2001), we developed methods to quantify the proliferation of epithelial cells cocultured with fibroblasts. We stained epithelial-fibroblast cocultures with the fluorescent DNA-intercalating dye 4,6-diamidino-2-phenylindole (DAPI), or expressed green fluorescent protein (GFP) in the epithelial cells, and then cultured them with fibroblasts. The cocultures were photographed under an inverted microscope with appropriate filters, and the fluorescent images were captured with a digital camera. We modified an image analysis program to selectively recognize the smaller, more intensely fluorescent epithelial cell nuclei in DAPI-stained cultures and used the program to quantify areas with DAPI fluorescence generated by epithelial nuclei or GFP fluorescence generated by epithelial cells in each field. Analysis of the image areas with DAPI and GFP fluorescences produced nearly identical quantification of epithelial cells in coculture with fibroblasts. We confirmed these results by manual counting. In addition, GFP labeling permitted kinetic studies of the same coculture over multiple time points. The image analysis-based quantification method we describe here is an easy and reliable way to monitor cells in coculture and should be useful for a variety of cell biological studies. Copyright 2002 Wiley-Liss, Inc.

A linear programming approach to reconstructing subcellular structures from confocal images for automated generation of representative 3D cellular models.

PubMed

Wood, Scott T; Dean, Brian C; Dean, Delphine

2013-04-01

This paper presents a novel computer vision algorithm to analyze 3D stacks of confocal images of fluorescently stained single cells. The goal of the algorithm is to create representative in silico model structures that can be imported into finite element analysis software for mechanical characterization. Segmentation of cell and nucleus boundaries is accomplished via standard thresholding methods. Using novel linear programming methods, a representative actin stress fiber network is generated by computing a linear superposition of fibers having minimum discrepancy compared with an experimental 3D confocal image. Qualitative validation is performed through analysis of seven 3D confocal image stacks of adherent vascular smooth muscle cells (VSMCs) grown in 2D culture. The presented method is able to automatically generate 3D geometries of the cell's boundary, nucleus, and representative F-actin network based on standard cell microscopy data. These geometries can be used for direct importation and implementation in structural finite element models for analysis of the mechanics of a single cell to potentially speed discoveries in the fields of regenerative medicine, mechanobiology, and drug discovery. Copyright © 2012 Elsevier B.V. All rights reserved.

Cell labeling with magnetic nanoparticles: Opportunity for magnetic cell imaging and cell manipulation

PubMed Central

2013-01-01

This tutorial describes a method of controlled cell labeling with citrate-coated ultra small superparamagnetic iron oxide nanoparticles. This method may provide basically all kinds of cells with sufficient magnetization to allow cell detection by high-resolution magnetic resonance imaging (MRI) and to enable potential magnetic manipulation. In order to efficiently exploit labeled cells, quantify the magnetic load and deliver or follow-up magnetic cells, we herein describe the main requirements that should be applied during the labeling procedure. Moreover we present some recommendations for cell detection and quantification by MRI and detail magnetic guiding on some real-case studies in vitro and in vivo. PMID:24564857

Normalized Polarization Ratios for the Analysis of Cell Polarity

PubMed Central

Shimoni, Raz; Pham, Kim; Yassin, Mohammed; Ludford-Menting, Mandy J.; Gu, Min; Russell, Sarah M.

2014-01-01

The quantification and analysis of molecular localization in living cells is increasingly important for elucidating biological pathways, and new methods are rapidly emerging. The quantification of cell polarity has generated much interest recently, and ratiometric analysis of fluorescence microscopy images provides one means to quantify cell polarity. However, detection of fluorescence, and the ratiometric measurement, is likely to be sensitive to acquisition settings and image processing parameters. Using imaging of EGFP-expressing cells and computer simulations of variations in fluorescence ratios, we characterized the dependence of ratiometric measurements on processing parameters. This analysis showed that image settings alter polarization measurements; and that clustered localization is more susceptible to artifacts than homogeneous localization. To correct for such inconsistencies, we developed and validated a method for choosing the most appropriate analysis settings, and for incorporating internal controls to ensure fidelity of polarity measurements. This approach is applicable to testing polarity in all cells where the axis of polarity is known. PMID:24963926

Novel image analysis methods for quantification of in situ 3-D tendon cell and matrix strain.

PubMed

Fung, Ashley K; Paredes, J J; Andarawis-Puri, Nelly

2018-01-23

Macroscopic tendon loads modulate the cellular microenvironment leading to biological outcomes such as degeneration or repair. Previous studies have shown that damage accumulation and the phases of tendon healing are marked by significant changes in the extracellular matrix, but it remains unknown how mechanical forces of the extracellular matrix are translated to mechanotransduction pathways that ultimately drive the biological response. Our overarching hypothesis is that the unique relationship between extracellular matrix strain and cell deformation will dictate biological outcomes, prompting the need for quantitative methods to characterize the local strain environment. While 2-D methods have successfully calculated matrix strain and cell deformation, 3-D methods are necessary to capture the increased complexity that can arise due to high levels of anisotropy and out-of-plane motion, particularly in the disorganized, highly cellular, injured state. In this study, we validated the use of digital volume correlation methods to quantify 3-D matrix strain using images of naïve tendon cells, the collagen fiber matrix, and injured tendon cells. Additionally, naïve tendon cell images were used to develop novel methods for 3-D cell deformation and 3-D cell-matrix strain, which is defined as a quantitative measure of the relationship between matrix strain and cell deformation. The results support that these methods can be used to detect strains with high accuracy and can be further extended to an in vivo setting for observing temporal changes in cell and matrix mechanics during degeneration and healing. Copyright © 2017. Published by Elsevier Ltd.

Multi-scale Gaussian representation and outline-learning based cell image segmentation.

PubMed

Farhan, Muhammad; Ruusuvuori, Pekka; Emmenlauer, Mario; Rämö, Pauli; Dehio, Christoph; Yli-Harja, Olli

2013-01-01

High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks.

Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers

PubMed Central

Karaçalı, Bilge; Vamvakidou, Alexandra P; Tözeren, Aydın

2007-01-01

Background Three-dimensional in vitro culture of cancer cells are used to predict the effects of prospective anti-cancer drugs in vivo. In this study, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images. Methods Histologic cross sections of breast tumoroids developed in co-culture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using k-means clustering. Automated segmentation was used to identify image regions composed of cells expressing a given biomarker. Synthesized images were created to check the accuracy of the image processing system. Results Accuracy of automated segmentation was over 95% in identifying regions of interest in synthesized images. Image analysis of adjacent histology slides stained, respectively, for Ecad and PR, accurately predicted regions of different cell phenotypes. Image analysis of tumoroid cross sections from different tumoroids obtained under the same co-culture conditions indicated the variation of cellular composition from one tumoroid to another. Variations in the compositions of cross sections obtained from the same tumoroid were established by parallel analysis of Ecad and PR-stained cross section images. Conclusion Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development. PMID:17822559

Correlation of live-cell imaging with volume scanning electron microscopy.

PubMed

Lucas, Miriam S; Günthert, Maja; Bittermann, Anne Greet; de Marco, Alex; Wepf, Roger

2017-01-01

Live-cell imaging is one of the most widely applied methods in live science. Here we describe two setups for live-cell imaging, which can easily be combined with volume SEM for correlative studies. The first procedure applies cell culture dishes with a gridded glass support, which can be used for any light microscopy modality. The second approach is a flow-chamber setup based on Ibidi μ-slides. Both live-cell imaging strategies can be followed up with serial blockface- or focused ion beam-scanning electron microscopy. Two types of resin embedding after heavy metal staining and dehydration are presented making best use of the particular advantages of each imaging modality: classical en-bloc embedding and thin-layer plastification. The latter can be used only for focused ion beam-scanning electron microscopy, but is advantageous for studying cell-interactions with specific substrates, or when the substrate cannot be removed. En-bloc embedding has diverse applications and can be applied for both described volume scanning electron microscopy techniques. Finally, strategies for relocating the cell of interest are discussed for both embedding approaches and in respect to the applied light and scanning electron microscopy methods. Copyright © 2017 Elsevier Inc. All rights reserved.

High-throughput, image-based screening of pooled genetic variant libraries

PubMed Central

Emanuel, George; Moffitt, Jeffrey R.; Zhuang, Xiaowei

2018-01-01

Image-based, high-throughput screening of genetic perturbations will advance both biology and biotechnology. We report a high-throughput screening method that allows diverse genotypes and corresponding phenotypes to be imaged in numerous individual cells. We achieve genotyping by introducing barcoded genetic variants into cells and using massively multiplexed FISH to measure the barcodes. We demonstrated this method by screening mutants of the fluorescent protein YFAST, yielding brighter and more photostable YFAST variants. PMID:29083401

Leukocyte Recognition Using EM-Algorithm

NASA Astrophysics Data System (ADS)

Colunga, Mario Chirinos; Siordia, Oscar Sánchez; Maybank, Stephen J.

This document describes a method for classifying images of blood cells. Three different classes of cells are used: Band Neutrophils, Eosinophils and Lymphocytes. The image pattern is projected down to a lower dimensional sub space using PCA; the probability density function for each class is modeled with a Gaussian mixture using the EM-Algorithm. A new cell image is classified using the maximum a posteriori decision rule.

Microtubules in Plant Cells: Strategies and Methods for Immunofluorescence, Transmission Electron Microscopy and Live Cell Imaging

PubMed Central

Celler, Katherine; Fujita, Miki; Kawamura, Eiko; Ambrose, Chris; Herburger, Klaus; Wasteneys, Geoffrey O.

2016-01-01

Microtubules are required throughout plant development for a wide variety of processes, and different strategies have evolved to visualize and analyze them. This chapter provides specific methods that can be used to analyze microtubule organization and dynamic properties in plant systems and summarizes the advantages and limitations for each technique. We outline basic methods for preparing samples for immunofluorescence labelling, including an enzyme-based permeabilization method, and a freeze-shattering method, which generates microfractures in the cell wall to provide antibodies access to cells in cuticle-laden aerial organs such as leaves. We discuss current options for live cell imaging of MTs with fluorescently tagged proteins (FPs), and provide chemical fixation, high pressure freezing/freeze substitution, and post-fixation staining protocols for preserving MTs for transmission electron microscopy and tomography. PMID:26498784

The morphological classification of normal and abnormal red blood cell using Self Organizing Map

NASA Astrophysics Data System (ADS)

Rahmat, R. F.; Wulandari, F. S.; Faza, S.; Muchtar, M. A.; Siregar, I.

2018-02-01

Blood is an essential component of living creatures in the vascular space. For possible disease identification, it can be tested through a blood test, one of which can be seen from the form of red blood cells. The normal and abnormal morphology of the red blood cells of a patient is very helpful to doctors in detecting a disease. With the advancement of digital image processing technology can be used to identify normal and abnormal blood cells of a patient. This research used self-organizing map method to classify the normal and abnormal form of red blood cells in the digital image. The use of self-organizing map neural network method can be implemented to classify the normal and abnormal form of red blood cells in the input image with 93,78% accuracy testing.

3D quantitative phase imaging of neural networks using WDT

NASA Astrophysics Data System (ADS)

Kim, Taewoo; Liu, S. C.; Iyer, Raj; Gillette, Martha U.; Popescu, Gabriel

2015-03-01

White-light diffraction tomography (WDT) is a recently developed 3D imaging technique based on a quantitative phase imaging system called spatial light interference microscopy (SLIM). The technique has achieved a sub-micron resolution in all three directions with high sensitivity granted by the low-coherence of a white-light source. Demonstrations of the technique on single cell imaging have been presented previously; however, imaging on any larger sample, including a cluster of cells, has not been demonstrated using the technique. Neurons in an animal body form a highly complex and spatially organized 3D structure, which can be characterized by neuronal networks or circuits. Currently, the most common method of studying the 3D structure of neuron networks is by using a confocal fluorescence microscope, which requires fluorescence tagging with either transient membrane dyes or after fixation of the cells. Therefore, studies on neurons are often limited to samples that are chemically treated and/or dead. WDT presents a solution for imaging live neuron networks with a high spatial and temporal resolution, because it is a 3D imaging method that is label-free and non-invasive. Using this method, a mouse or rat hippocampal neuron culture and a mouse dorsal root ganglion (DRG) neuron culture have been imaged in order to see the extension of processes between the cells in 3D. Furthermore, the tomogram is compared with a confocal fluorescence image in order to investigate the 3D structure at synapses.

Automatic detection and analysis of cell motility in phase-contrast time-lapse images using a combination of maximally stable extremal regions and Kalman filter approaches.

PubMed

Kaakinen, M; Huttunen, S; Paavolainen, L; Marjomäki, V; Heikkilä, J; Eklund, L

2014-01-01

Phase-contrast illumination is simple and most commonly used microscopic method to observe nonstained living cells. Automatic cell segmentation and motion analysis provide tools to analyze single cell motility in large cell populations. However, the challenge is to find a sophisticated method that is sufficiently accurate to generate reliable results, robust to function under the wide range of illumination conditions encountered in phase-contrast microscopy, and also computationally light for efficient analysis of large number of cells and image frames. To develop better automatic tools for analysis of low magnification phase-contrast images in time-lapse cell migration movies, we investigated the performance of cell segmentation method that is based on the intrinsic properties of maximally stable extremal regions (MSER). MSER was found to be reliable and effective in a wide range of experimental conditions. When compared to the commonly used segmentation approaches, MSER required negligible preoptimization steps thus dramatically reducing the computation time. To analyze cell migration characteristics in time-lapse movies, the MSER-based automatic cell detection was accompanied by a Kalman filter multiobject tracker that efficiently tracked individual cells even in confluent cell populations. This allowed quantitative cell motion analysis resulting in accurate measurements of the migration magnitude and direction of individual cells, as well as characteristics of collective migration of cell groups. Our results demonstrate that MSER accompanied by temporal data association is a powerful tool for accurate and reliable analysis of the dynamic behaviour of cells in phase-contrast image sequences. These techniques tolerate varying and nonoptimal imaging conditions and due to their relatively light computational requirements they should help to resolve problems in computationally demanding and often time-consuming large-scale dynamical analysis of cultured cells. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Probing amyloid protein aggregation with optical superresolution methods: from the test tube to models of disease

PubMed Central

Kaminski, Clemens F.; Kaminski Schierle, Gabriele S.

2016-01-01

Abstract. The misfolding and self-assembly of intrinsically disordered proteins into insoluble amyloid structures are central to many neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases. Optical imaging of this self-assembly process in vitro and in cells is revolutionizing our understanding of the molecular mechanisms behind these devastating conditions. In contrast to conventional biophysical methods, optical imaging and, in particular, optical superresolution imaging, permits the dynamic investigation of the molecular self-assembly process in vitro and in cells, at molecular-level resolution. In this article, current state-of-the-art imaging methods are reviewed and discussed in the context of research into neurodegeneration. PMID:27413767

An approach for characterising cellular polymeric foam structures using computed tomography

NASA Astrophysics Data System (ADS)

Chen, Youming; Das, Raj; Battley, Mark

2018-02-01

Global properties of foams depend on foam base materials and microstructures. Characterisation of foam microstructures is important for developing numerical foam models. In this study, the microstructures of four polymeric structural foams were imaged using a micro-CT scanner. Image processing and analysis methods were proposed to quantify the relative density, cell wall thickness and cell size of these foams from the captured CT images. Overall, the cells in these foams are fairly isotropic, and cell walls are rather straight. The measured average relative densities are in good agreement with the actual values. Relative density, cell size and cell wall thickness in these foams are found to vary along the thickness of foam panel direction. Cell walls in two of these foams are found to be filled with secondary pores. In addition, it is found that the average cell wall thickness measured from 2D images is around 1.4 times of that measured from 3D images, and the average cell size measured from 3D images is 1.16 times of that measured from 2D images. The distributions of cell wall thickness and cell size measured from 2D images exhibit lager dispersion in comparison to those measured from 3D images.

Analysis of spectrally resolved autofluorescence images by support vector machines

NASA Astrophysics Data System (ADS)

Mateasik, A.; Chorvat, D.; Chorvatova, A.

2013-02-01

Spectral analysis of the autofluorescence images of isolated cardiac cells was performed to evaluate and to classify the metabolic state of the cells in respect to the responses to metabolic modulators. The classification was done using machine learning approach based on support vector machine with the set of the automatically calculated features from recorded spectral profile of spectral autofluorescence images. This classification method was compared with the classical approach where the individual spectral components contributing to cell autofluorescence were estimated by spectral analysis, namely by blind source separation using non-negative matrix factorization. Comparison of both methods showed that machine learning can effectively classify the spectrally resolved autofluorescence images without the need of detailed knowledge about the sources of autofluorescence and their spectral properties.

Glioma grading using cell nuclei morphologic features in digital pathology images

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Iftekharuddin, Khan M.

2016-03-01

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

A novel multiphoton microscopy images segmentation method based on superpixel and watershed.

PubMed

Wu, Weilin; Lin, Jinyong; Wang, Shu; Li, Yan; Liu, Mingyu; Liu, Gaoqiang; Cai, Jianyong; Chen, Guannan; Chen, Rong

2017-04-01

Multiphoton microscopy (MPM) imaging technique based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) shows fantastic performance for biological imaging. The automatic segmentation of cellular architectural properties for biomedical diagnosis based on MPM images is still a challenging issue. A novel multiphoton microscopy images segmentation method based on superpixels and watershed (MSW) is presented here to provide good segmentation results for MPM images. The proposed method uses SLIC superpixels instead of pixels to analyze MPM images for the first time. The superpixels segmentation based on a new distance metric combined with spatial, CIE Lab color space and phase congruency features, divides the images into patches which keep the details of the cell boundaries. Then the superpixels are used to reconstruct new images by defining an average value of superpixels as image pixels intensity level. Finally, the marker-controlled watershed is utilized to segment the cell boundaries from the reconstructed images. Experimental results show that cellular boundaries can be extracted from MPM images by MSW with higher accuracy and robustness. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

CP-CHARM: segmentation-free image classification made accessible.

PubMed

Uhlmann, Virginie; Singh, Shantanu; Carpenter, Anne E

2016-01-27

Automated classification using machine learning often relies on features derived from segmenting individual objects, which can be difficult to automate. WND-CHARM is a previously developed classification algorithm in which features are computed on the whole image, thereby avoiding the need for segmentation. The algorithm obtained encouraging results but requires considerable computational expertise to execute. Furthermore, some benchmark sets have been shown to be subject to confounding artifacts that overestimate classification accuracy. We developed CP-CHARM, a user-friendly image-based classification algorithm inspired by WND-CHARM in (i) its ability to capture a wide variety of morphological aspects of the image, and (ii) the absence of requirement for segmentation. In order to make such an image-based classification method easily accessible to the biological research community, CP-CHARM relies on the widely-used open-source image analysis software CellProfiler for feature extraction. To validate our method, we reproduced WND-CHARM's results and ensured that CP-CHARM obtained comparable performance. We then successfully applied our approach on cell-based assay data and on tissue images. We designed these new training and test sets to reduce the effect of batch-related artifacts. The proposed method preserves the strengths of WND-CHARM - it extracts a wide variety of morphological features directly on whole images thereby avoiding the need for cell segmentation, but additionally, it makes the methods easily accessible for researchers without computational expertise by implementing them as a CellProfiler pipeline. It has been demonstrated to perform well on a wide range of bioimage classification problems, including on new datasets that have been carefully selected and annotated to minimize batch effects. This provides for the first time a realistic and reliable assessment of the whole image classification strategy.

White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks.

PubMed

Choi, Jin Woo; Ku, Yunseo; Yoo, Byeong Wook; Kim, Jung-Ah; Lee, Dong Soon; Chai, Young Jun; Kong, Hyoun-Joong; Kim, Hee Chan

2017-01-01

The white blood cell differential count of the bone marrow provides information concerning the distribution of immature and mature cells within maturation stages. The results of such examinations are important for the diagnosis of various diseases and for follow-up care after chemotherapy. However, manual, labor-intensive methods to determine the differential count lead to inter- and intra-variations among the results obtained by hematologists. Therefore, an automated system to conduct the white blood cell differential count is highly desirable, but several difficulties hinder progress. There are variations in the white blood cells of each maturation stage, small inter-class differences within each stage, and variations in images because of the different acquisition and staining processes. Moreover, a large number of classes need to be classified for bone marrow smear analysis, and the high density of touching cells in bone marrow smears renders difficult the segmentation of single cells, which is crucial to traditional image processing and machine learning. Few studies have attempted to discriminate bone marrow cells, and even these have either discriminated only a few classes or yielded insufficient performance. In this study, we propose an automated white blood cell differential counting system from bone marrow smear images using a dual-stage convolutional neural network (CNN). A total of 2,174 patch images were collected for training and testing. The dual-stage CNN classified images into 10 classes of the myeloid and erythroid maturation series, and achieved an accuracy of 97.06%, a precision of 97.13%, a recall of 97.06%, and an F-1 score of 97.1%. The proposed method not only showed high classification performance, but also successfully classified raw images without single cell segmentation and manual feature extraction by implementing CNN. Moreover, it demonstrated rotation and location invariance. These results highlight the promise of the proposed method as an automated white blood cell differential count system.

White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks

PubMed Central

Choi, Jin Woo; Ku, Yunseo; Yoo, Byeong Wook; Kim, Jung-Ah; Lee, Dong Soon; Chai, Young Jun; Kong, Hyoun-Joong

2017-01-01

The white blood cell differential count of the bone marrow provides information concerning the distribution of immature and mature cells within maturation stages. The results of such examinations are important for the diagnosis of various diseases and for follow-up care after chemotherapy. However, manual, labor-intensive methods to determine the differential count lead to inter- and intra-variations among the results obtained by hematologists. Therefore, an automated system to conduct the white blood cell differential count is highly desirable, but several difficulties hinder progress. There are variations in the white blood cells of each maturation stage, small inter-class differences within each stage, and variations in images because of the different acquisition and staining processes. Moreover, a large number of classes need to be classified for bone marrow smear analysis, and the high density of touching cells in bone marrow smears renders difficult the segmentation of single cells, which is crucial to traditional image processing and machine learning. Few studies have attempted to discriminate bone marrow cells, and even these have either discriminated only a few classes or yielded insufficient performance. In this study, we propose an automated white blood cell differential counting system from bone marrow smear images using a dual-stage convolutional neural network (CNN). A total of 2,174 patch images were collected for training and testing. The dual-stage CNN classified images into 10 classes of the myeloid and erythroid maturation series, and achieved an accuracy of 97.06%, a precision of 97.13%, a recall of 97.06%, and an F-1 score of 97.1%. The proposed method not only showed high classification performance, but also successfully classified raw images without single cell segmentation and manual feature extraction by implementing CNN. Moreover, it demonstrated rotation and location invariance. These results highlight the promise of the proposed method as an automated white blood cell differential count system. PMID:29228051

Biological imaging by soft x-ray diffraction microscopy

DOE PAGES

Shapiro, D.; Thibault, P.; Beetz, T.; ...

2005-10-25

We have used the method of x-ray diffraction microscopy to image the complex-valued exit wave of an intact and unstained yeast cell. The images of the freeze-dried cell, obtained by using 750-eV x-rays from different angular orientations, portray several of the cell's major internal components to 30-nm resolution. The good agreement among the independently recovered structures demonstrates the accuracy of the imaging technique. To obtain the best possible reconstructions, we have implemented procedures for handling noisy and incomplete diffraction data, and we propose a method for determining the reconstructed resolution. This work represents a previously uncharacterized application of x-ray diffractionmore » microscopy to a specimen of this complexity and provides confidence in the feasibility of the ultimate goal of imaging biological specimens at 10-nm resolution in three dimensions.« less

Setup for functional cell ablation with lasers: coupling of a laser to a microscope.

PubMed

Sweeney, Sean T; Hidalgo, Alicia; de Belle, J Steven; Keshishian, Haig

2012-06-01

The selective removal of cells by ablation is a powerful tool in the study of eukaryotic developmental biology, providing much information about their origin, fate, or function in the developing organism. In Drosophila, three main methods have been used to ablate cells: chemical, genetic, and laser ablation. Each method has its own applicability with regard to developmental stage and the cells to be ablated, and its own limitations. The primary advantage of laser-based ablation is the flexibility provided by the method: The operations can be performed in any cell pattern and at any time in development. Laser-based techniques permit manipulation of structures within cells, even to the molecular level. They can also be used for gene activation. However, laser ablation can be expensive, labor-intensive, and time-consuming. Although live cells can be difficult to image in Drosophila embryos, the use of vital fluorescent imaging methods has made laser-mediated cell manipulation methods more appealing; the methods are relatively straightforward. This article provides the information necessary for setting up and using a laser microscope for lasesr ablation studies.

Characterization of fission gas bubbles in irradiated U-10Mo fuel

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

Casella, Andrew M.; Burkes, Douglas E.; MacFarlan, Paul J.

2017-09-01

Irradiated U-10Mo fuel samples were prepared with traditional mechanical potting and polishing methods with in a hot cell. They were then removed and imaged with an SEM located outside of a hot cell. The images were then processed with basic imaging techniques from 3 separate software packages. The results were compared and a baseline method for characterization of fission gas bubbles in the samples is proposed. It is hoped that through adoption of or comparison to this baseline method that sample characterization can be somewhat standardized across the field of post irradiated examination of metal fuels.

Long Term Non-Invasive Imaging of Embryonic Stem Cells Using Reporter Genes

PubMed Central

Sun, Ning; Lee, Andrew; Wu, Joseph C.

2013-01-01

Development of non-invasive and accurate methods to track cell fate following delivery will greatly expedite transition of embryonic stem (ES) cell therapy to the clinic. Here we describe a protocol for the in vivo monitoring of stem cell survival, proliferation, and migration using reporter genes. We established stable ES cell lines constitutively expressing double fusion (DF; enhanced green fluorescent protein and firefly luciferase) or triple fusion (TF; monomeric red fluorescent protein, firefly luciferase, and herpes simplex virus thymidine kinase) reporter genes using lentiviral transduction. We used fluorescence activated cell sorting to purify these populations in vitro, bioluminescence imaging and positron emission tomography imaging to track them in vivo, and fluorescence immunostaining to confirm the results ex vivo. Unlike other methods of cell tracking such as iron particle and radionuclide labeling, reporter genes are inherited genetically and can be used to monitor cell proliferation and survival for the lifetime of transplanted cells and their progeny. PMID:19617890

Fast and Accurate Cell Tracking by a Novel Optical-Digital Hybrid Method

NASA Astrophysics Data System (ADS)

Torres-Cisneros, M.; Aviña-Cervantes, J. G.; Pérez-Careta, E.; Ambriz-Colín, F.; Tinoco, Verónica; Ibarra-Manzano, O. G.; Plascencia-Mora, H.; Aguilera-Gómez, E.; Ibarra-Manzano, M. A.; Guzman-Cabrera, R.; Debeir, Olivier; Sánchez-Mondragón, J. J.

2013-09-01

An innovative methodology to detect and track cells using microscope images enhanced by optical cross-correlation techniques is proposed in this paper. In order to increase the tracking sensibility, image pre-processing has been implemented as a morphological operator on the microscope image. Results show that the pre-processing process allows for additional frames of cell tracking, therefore increasing its robustness. The proposed methodology can be used in analyzing different problems such as mitosis, cell collisions, and cell overlapping, ultimately designed to identify and treat illnesses and malignancies.

Cell type classifiers for breast cancer microscopic images based on fractal dimension texture analysis of image color layers.

PubMed

Jitaree, Sirinapa; Phinyomark, Angkoon; Boonyaphiphat, Pleumjit; Phukpattaranont, Pornchai

2015-01-01

Having a classifier of cell types in a breast cancer microscopic image (BCMI), obtained with immunohistochemical staining, is required as part of a computer-aided system that counts the cancer cells in such BCMI. Such quantitation by cell counting is very useful in supporting decisions and planning of the medical treatment of breast cancer. This study proposes and evaluates features based on texture analysis by fractal dimension (FD), for the classification of histological structures in a BCMI into either cancer cells or non-cancer cells. The cancer cells include positive cells (PC) and negative cells (NC), while the normal cells comprise stromal cells (SC) and lymphocyte cells (LC). The FD feature values were calculated with the box-counting method from binarized images, obtained by automatic thresholding with Otsu's method of the grayscale images for various color channels. A total of 12 color channels from four color spaces (RGB, CIE-L*a*b*, HSV, and YCbCr) were investigated, and the FD feature values from them were used with decision tree classifiers. The BCMI data consisted of 1,400, 1,200, and 800 images with pixel resolutions 128 × 128, 192 × 192, and 256 × 256, respectively. The best cross-validated classification accuracy was 93.87%, for distinguishing between cancer and non-cancer cells, obtained using the Cr color channel with window size 256. The results indicate that the proposed algorithm, based on fractal dimension features extracted from a color channel, performs well in the automatic classification of the histology in a BCMI. This might support accurate automatic cell counting in a computer-assisted system for breast cancer diagnosis. © Wiley Periodicals, Inc.

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

Lu, Fa-Ke; Basu, Srinjan; Igras, Vivien

Label-free DNA imaging is highly desirable in biology and medicine to perform live imaging without affecting cell function and to obtain instant histological tissue examination during surgical procedures. Here we show a label-free DNA imaging method with stimulated Raman scattering (SRS) microscopy for visualization of the cell nuclei in live animals and intact fresh human tissues with subcellular resolution. Relying on the distinct Raman spectral features of the carbon-hydrogen bonds in DNA, the distribution of DNA is retrieved from the strong background of proteins and lipids by linear decomposition of SRS images at three optimally selected Raman shifts. Based onmore » changes on DNA condensation in the nucleus, we were able to capture chromosome dynamics during cell division both in vitro and in vivo. We tracked mouse skin cell proliferation, induced by drug treatment, through in vivo counting of the mitotic rate. Moreover, we demonstrated a label-free histology method for human skin cancer diagnosis that provides comparable results to other conventional tissue staining methods such as H&E. In conclusion, our approach exhibits higher sensitivity than SRS imaging of DNA in the fingerprint spectral region. Compared with spontaneous Raman imaging of DNA, our approach is three orders of magnitude faster, allowing both chromatin dynamic studies and label-free optical histology in real time.« less

Imaging light responses of foveal ganglion cells in the living macaque eye.

PubMed

Yin, Lu; Masella, Benjamin; Dalkara, Deniz; Zhang, Jie; Flannery, John G; Schaffer, David V; Williams, David R; Merigan, William H

2014-05-07

The fovea dominates primate vision, and its anatomy and perceptual abilities are well studied, but its physiology has been little explored because of limitations of current physiological methods. In this study, we adapted a novel in vivo imaging method, originally developed in mouse retina, to explore foveal physiology in the macaque, which permits the repeated imaging of the functional response of many retinal ganglion cells (RGCs) simultaneously. A genetically encoded calcium indicator, G-CaMP5, was inserted into foveal RGCs, followed by calcium imaging of the displacement of foveal RGCs from their receptive fields, and their intensity-response functions. The spatial offset of foveal RGCs from their cone inputs makes this method especially appropriate for fovea by permitting imaging of RGC responses without excessive light adaptation of cones. This new method will permit the tracking of visual development, progression of retinal disease, or therapeutic interventions, such as insertion of visual prostheses.

Classification of yeast cells from image features to evaluate pathogen conditions

NASA Astrophysics Data System (ADS)

van der Putten, Peter; Bertens, Laura; Liu, Jinshuo; Hagen, Ferry; Boekhout, Teun; Verbeek, Fons J.

2007-01-01

Morphometrics from images, image analysis, may reveal differences between classes of objects present in the images. We have performed an image-features-based classification for the pathogenic yeast Cryptococcus neoformans. Building and analyzing image collections from the yeast under different environmental or genetic conditions may help to diagnose a new "unseen" situation. Diagnosis here means that retrieval of the relevant information from the image collection is at hand each time a new "sample" is presented. The basidiomycetous yeast Cryptococcus neoformans can cause infections such as meningitis or pneumonia. The presence of an extra-cellular capsule is known to be related to virulence. This paper reports on the approach towards developing classifiers for detecting potentially more or less virulent cells in a sample, i.e. an image, by using a range of features derived from the shape or density distribution. The classifier can henceforth be used for automating screening and annotating existing image collections. In addition we will present our methods for creating samples, collecting images, image preprocessing, identifying "yeast cells" and creating feature extraction from the images. We compare various expertise based and fully automated methods of feature selection and benchmark a range of classification algorithms and illustrate successful application to this particular domain.

Imaging of targeted lipid microbubbles to detect cancer cells using third harmonic generation microscopy

PubMed Central

Harpel, Kaitlin; Baker, Robert Dawson; Amirsolaimani, Babak; Mehravar, Soroush; Vagner, Josef; Matsunaga, Terry O.; Banerjee, Bhaskar; Kieu, Khanh

2016-01-01

The use of receptor-targeted lipid microbubbles imaged by ultrasound is an innovative method of detecting and localizing disease. However, since ultrasound requires a medium between the transducer and the object being imaged, it is impractical to apply to an exposed surface in a surgical setting where sterile fields need be maintained and ultrasound gel may cause the bubbles to collapse. Multiphoton microscopy (MPM) is an emerging tool for accurate, label-free imaging of tissues and cells with high resolution and contrast. We have recently determined a novel application of MPM to be used for detecting targeted microbubble adherence to the upregulated plectin-receptor on pancreatic tumor cells. Specifically, the third-harmonic generation response can be used to detect bound microbubbles to various cell types presenting MPM as an alternative and useful imaging method. This is an interesting technique that can potentially be translated as a diagnostic tool for the early detection of cancer and inflammatory disorders. PMID:27446711

Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.

PubMed

de Groot, Reinoud; Lüthi, Joel; Lindsay, Helen; Holtackers, René; Pelkmans, Lucas

2018-01-23

High-content imaging using automated microscopy and computer vision allows multivariate profiling of single-cell phenotypes. Here, we present methods for the application of the CISPR-Cas9 system in large-scale, image-based, gene perturbation experiments. We show that CRISPR-Cas9-mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image-based phenotyping. We developed a pipeline to construct a large-scale arrayed library of 2,281 sequence-verified CRISPR-Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine-learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in-depth characterization of gene perturbation effects. This approach enables genome-scale image-based multivariate gene perturbation profiling using CRISPR-Cas9. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

Automatic evaluation of skin histopathological images for melanocytic features

NASA Astrophysics Data System (ADS)

Koosha, Mohaddeseh; Hoseini Alinodehi, S. Pourya; Nicolescu, Mircea; Safaei Naraghi, Zahra

2017-03-01

Successfully detecting melanocyte cells in the skin epidermis has great significance in skin histopathology. Because of the existence of cells with similar appearance to melanocytes in hematoxylin and eosin (HE) images of the epidermis, detecting melanocytes becomes a challenging task. This paper proposes a novel technique for the detection of melanocytes in HE images of the epidermis, based on the melanocyte color features, in the HSI color domain. Initially, an effective soft morphological filter is applied to the HE images in the HSI color domain to remove noise. Then a novel threshold-based technique is applied to distinguish the candidate melanocytes' nuclei. Similarly, the method is applied to find the candidate surrounding halos of the melanocytes. The candidate nuclei are associated with their surrounding halos using the suggested logical and statistical inferences. Finally, a fuzzy inference system is proposed, based on the HSI color information of a typical melanocyte in the epidermis, to calculate the similarity ratio of each candidate cell to a melanocyte. As our review on the literature shows, this is the first method evaluating epidermis cells for melanocyte similarity ratio. Experimental results on various images with different zooming factors show that the proposed method improves the results of previous works.

ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes

PubMed Central

Mosaliganti, Kishore R.; Noche, Ramil R.; Xiong, Fengzhu; Swinburne, Ian A.; Megason, Sean G.

2012-01-01

The quantification of cell shape, cell migration, and cell rearrangements is important for addressing classical questions in developmental biology such as patterning and tissue morphogenesis. Time-lapse microscopic imaging of transgenic embryos expressing fluorescent reporters is the method of choice for tracking morphogenetic changes and establishing cell lineages and fate maps in vivo. However, the manual steps involved in curating thousands of putative cell segmentations have been a major bottleneck in the application of these technologies especially for cell membranes. Segmentation of cell membranes while more difficult than nuclear segmentation is necessary for quantifying the relations between changes in cell morphology and morphogenesis. We present a novel and fully automated method to first reconstruct membrane signals and then segment out cells from 3D membrane images even in dense tissues. The approach has three stages: 1) detection of local membrane planes, 2) voting to fill structural gaps, and 3) region segmentation. We demonstrate the superior performance of the algorithms quantitatively on time-lapse confocal and two-photon images of zebrafish neuroectoderm and paraxial mesoderm by comparing its results with those derived from human inspection. We also compared with synthetic microscopic images generated by simulating the process of imaging with fluorescent reporters under varying conditions of noise. Both the over-segmentation and under-segmentation percentages of our method are around 5%. The volume overlap of individual cells, compared to expert manual segmentation, is consistently over 84%. By using our software (ACME) to study somite formation, we were able to segment touching cells with high accuracy and reliably quantify changes in morphogenetic parameters such as cell shape and size, and the arrangement of epithelial and mesenchymal cells. Our software has been developed and tested on Windows, Mac, and Linux platforms and is available publicly under an open source BSD license (https://github.com/krm15/ACME). PMID:23236265

Label-free DNA imaging in vivo with stimulated Raman scattering microscopy

DOE PAGES

Lu, Fa-Ke; Basu, Srinjan; Igras, Vivien; ...

2015-08-31

Label-free DNA imaging is highly desirable in biology and medicine to perform live imaging without affecting cell function and to obtain instant histological tissue examination during surgical procedures. Here we show a label-free DNA imaging method with stimulated Raman scattering (SRS) microscopy for visualization of the cell nuclei in live animals and intact fresh human tissues with subcellular resolution. Relying on the distinct Raman spectral features of the carbon-hydrogen bonds in DNA, the distribution of DNA is retrieved from the strong background of proteins and lipids by linear decomposition of SRS images at three optimally selected Raman shifts. Based onmore » changes on DNA condensation in the nucleus, we were able to capture chromosome dynamics during cell division both in vitro and in vivo. We tracked mouse skin cell proliferation, induced by drug treatment, through in vivo counting of the mitotic rate. Moreover, we demonstrated a label-free histology method for human skin cancer diagnosis that provides comparable results to other conventional tissue staining methods such as H&E. In conclusion, our approach exhibits higher sensitivity than SRS imaging of DNA in the fingerprint spectral region. Compared with spontaneous Raman imaging of DNA, our approach is three orders of magnitude faster, allowing both chromatin dynamic studies and label-free optical histology in real time.« less

High Resolution Live Cell Raman Imaging Using Subcellular Organelle-Targeting SERS-Sensitive Gold Nanoparticles with Highly Narrow Intra-Nanogap

PubMed Central

Kang, Jeon Woong; So, Peter T. C.; Dasari, Ramachandra R.; Lim, Dong-Kwon

2015-01-01

We report a method to achieve high speed and high resolution live cell Raman images using small spherical gold nanoparticles with highly narrow intra-nanogap structures responding to NIR excitation (785 nm) and high-speed confocal Raman microscopy. The three different Raman-active molecules placed in the narrow intra-nanogap showed a strong and uniform Raman intensity in solution even under transient exposure time (10 ms) and low input power of incident laser (200 μW), which lead to obtain high-resolution single cell image within 30 s without inducing significant cell damage. The high resolution Raman image showed the distributions of gold nanoparticles for their targeted sites such as cytoplasm, mitochondria, or nucleus. The high speed Raman-based live cell imaging allowed us to monitor rapidly changing cell morphologies during cell death induced by the addition of highly toxic KCN solution to cells. These results strongly suggest that the use of SERS-active nanoparticle can greatly improve the current temporal resolution and image quality of Raman-based cell images enough to obtain the detailed cell dynamics and/or the responses of cells to potential drug molecules. PMID:25646716

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images.

PubMed

Ruusuvuori, Pekka; Aijö, Tarmo; Chowdhury, Sharif; Garmendia-Torres, Cecilia; Selinummi, Jyrki; Birbaumer, Mirko; Dudley, Aimée M; Pelkmans, Lucas; Yli-Harja, Olli

2010-05-13

Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed. To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies. These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.

Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection

PubMed Central

Su, Hai; Xing, Fuyong; Yang, Lin

2016-01-01

Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F1 score = 0.96. PMID:26812706

Optical Pumping Spin Exchange 3He Gas Cells for Magnetic Resonance Imaging

NASA Astrophysics Data System (ADS)

Kim, W.; Stepanyan, S. S.; Kim, A.; Jung, Y.; Woo, S.; Yurov, M.; Jang, J.

2009-08-01

We present a device for spin-exchange optical pumping system to produce large quantities of polarized noble gases for Magnetic Resonance Imaging (MRI). A method and design of apparatus for pumping the polarization of noble gases is described. The method and apparatus enable production, storage and usage of hyperpolarized noble gases for different purposes, including Magnetic Resonance Imaging of human and animal subjects. Magnetic imaging agents breathed into lungs can be observed by the radio waves of the MRI scanner and report back physical and functional information about lung's health and desease. The technique known as spin exchange optical pumping is used. Nuclear magnetic resonance is implemented to measure the polarization of hyperpolarized gas. The cells prepared and sealed under high vacuum after handling Alkali metals into the cell and filling with the 3He-N2 mixture. The cells could be refilled. The 3He reaches around 50% polarization in 5-15 hours.

Video-Rate Confocal Microscopy for Single-Molecule Imaging in Live Cells and Superresolution Fluorescence Imaging

PubMed Central

Lee, Jinwoo; Miyanaga, Yukihiro; Ueda, Masahiro; Hohng, Sungchul

2012-01-01

There is no confocal microscope optimized for single-molecule imaging in live cells and superresolution fluorescence imaging. By combining the swiftness of the line-scanning method and the high sensitivity of wide-field detection, we have developed a, to our knowledge, novel confocal fluorescence microscope with a good optical-sectioning capability (1.0 μm), fast frame rates (<33 fps), and superior fluorescence detection efficiency. Full compatibility of the microscope with conventional cell-imaging techniques allowed us to do single-molecule imaging with a great ease at arbitrary depths of live cells. With the new microscope, we monitored diffusion motion of fluorescently labeled cAMP receptors of Dictyostelium discoideum at both the basal and apical surfaces and obtained superresolution fluorescence images of microtubules of COS-7 cells at depths in the range 0–85 μm from the surface of a coverglass. PMID:23083712

An Image Analysis Method for the Precise Selection and Quantitation of Fluorescently Labeled Cellular Constituents

PubMed Central

Agley, Chibeza C.; Velloso, Cristiana P.; Lazarus, Norman R.

2012-01-01

The accurate measurement of the morphological characteristics of cells with nonuniform conformations presents difficulties. We report here a straightforward method using immunofluorescent staining and the commercially available imaging program Adobe Photoshop, which allows objective and precise information to be gathered on irregularly shaped cells. We have applied this measurement technique to the analysis of human muscle cells and their immunologically marked intracellular constituents, as these cells are prone to adopting a highly branched phenotype in culture. Use of this method can be used to overcome many of the long-standing limitations of conventional approaches for quantifying muscle cell size in vitro. In addition, wider applications of Photoshop as a quantitative and semiquantitative tool in immunocytochemistry are explored. PMID:22511600

Dynamic Assessment of the Endothelialization of Tissue-Engineered Blood Vessels Using an Optical Coherence Tomography Catheter-Based Fluorescence Imaging System

PubMed Central

Gurjarpadhye, Abhijit Achyut; DeWitt, Matthew R.; Xu, Yong; Wang, Ge; Rylander, Marissa Nichole

2015-01-01

Background: Lumen endothelialization of bioengineered vascular scaffolds is essential to maintain small-diameter graft patency and prevent thrombosis postimplantation. Unfortunately, nondestructive imaging methods to visualize this dynamic process are lacking, thus slowing development and clinical translation of these potential tissue-engineering approaches. To meet this need, a fluorescence imaging system utilizing a commercial optical coherence tomography (OCT) catheter was designed to visualize graft endothelialization. Methods: C7 DragonFly™ intravascular OCT catheter was used as a channel for delivery and collection of excitation and emission spectra. Poly-dl-lactide (PDLLA) electrospun scaffolds were seeded with endothelial cells (ECs). Seeded cells were exposed to Calcein AM before imaging, causing the living cells to emit green fluorescence in response to blue laser. By positioning the catheter tip precisely over a specimen using high-fidelity electromechanical components, small regions of the specimen were excited selectively. The resulting fluorescence intensities were mapped on a two-dimensional digital grid to generate spatial distribution of fluorophores at single-cell-level resolution. Fluorescence imaging of endothelialization on glass and PDLLA scaffolds was performed using the OCT catheter-based imaging system as well as with a commercial fluorescence microscope. Cell coverage area was calculated for both image sets for quantitative comparison of imaging techniques. Tubular PDLLA scaffolds were maintained in a bioreactor on seeding with ECs, and endothelialization was monitored over 5 days using the OCT catheter-based imaging system. Results: No significant difference was observed in images obtained using our imaging system to those acquired with the fluorescence microscope. Cell area coverage calculated using the images yielded similar values. Nondestructive imaging of endothelialization on tubular scaffolds showed cell proliferation with cell coverage area increasing from 15±4% to 89±6% over 5 days. Conclusion: In this study, we showed the capability of an OCT catheter-based imaging system to obtain single-cell resolution and to quantify endothelialization in tubular electrospun scaffolds. We also compared the resulting images with traditional microscopy, showing high fidelity in image capability. This imaging system, used in conjunction with OCT, could potentially be a powerful tool for in vitro optimization of scaffold cellularization, ensuring long-term graft patency postimplantation. PMID:25539889

Nanoscale live cell optical imaging of the dynamics of intracellular microvesicles in neural cells.

PubMed

Lee, Sohee; Heo, Chaejeong; Suh, Minah; Lee, Young Hee

2013-11-01

Recent advances in biotechnology and imaging technology have provided great opportunities to investigate cellular dynamics. Conventional imaging methods such as transmission electron microscopy, scanning electron microscopy, and atomic force microscopy are powerful techniques for cellular imaging, even at the nanoscale level. However, these techniques have limitations applications in live cell imaging because of the experimental preparation required, namely cell fixation, and the innately small field of view. In this study, we developed a nanoscale optical imaging (NOI) system that combines a conventional optical microscope with a high resolution dark-field condenser (Cytoviva, Inc.) and halogen illuminator. The NOI system's maximum resolution for live cell imaging is around 100 nm. We utilized NOI to investigate the dynamics of intracellular microvesicles of neural cells without immunocytological analysis. In particular, we studied direct, active random, and moderate random dynamic motions of intracellular microvesicles and visualized lysosomal vesicle changes after treatment of cells with a lysosomal inhibitor (NH4Cl). Our results indicate that the NOI system is a feasible, high-resolution optical imaging system for live small organelles that does not require complicated optics or immunocytological staining processes.

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations.

PubMed

Gómez-Villafuertes, Rosa; Paniagua-Herranz, Lucía; Gascon, Sergio; de Agustín-Durán, David; Ferreras, María de la O; Gil-Redondo, Juan Carlos; Queipo, María José; Menendez-Mendez, Aida; Pérez-Sen, Ráquel; Delicado, Esmerilda G; Gualix, Javier; Costa, Marcos R; Schroeder, Timm; Miras-Portugal, María Teresa; Ortega, Felipe

2017-12-16

Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate decisions, will be crucial to design therapeutic strategies for many diseases affecting the nervous system. Current methods to track cell populations rely on their final outcomes in still images and they generally fail to provide sufficient temporal resolution to identify behavioral features in single cells. Moreover, variations in cell death, behavioral heterogeneity within a cell population, dilution, spreading, or the low efficiency of the markers used to analyze cells are all important handicaps that will lead to incomplete or incorrect read-outs of the results. Conversely, performing live imaging and single cell tracking under appropriate conditions represents a powerful tool to monitor each of these events. Here, a time-lapse video-microscopy protocol, followed by post-processing, is described to track neural populations with single cell resolution, employing specific software. The methods described enable researchers to address essential questions regarding the cell biology and lineage progression of distinct neural populations.

Segmentation of vessels cluttered with cells using a physics based model.

PubMed

Schmugge, Stephen J; Keller, Steve; Nguyen, Nhat; Souvenir, Richard; Huynh, Toan; Clemens, Mark; Shin, Min C

2008-01-01

Segmentation of vessels in biomedical images is important as it can provide insight into analysis of vascular morphology, topology and is required for kinetic analysis of flow velocity and vessel permeability. Intravital microscopy is a powerful tool as it enables in vivo imaging of both vasculature and circulating cells. However, the analysis of vasculature in those images is difficult due to the presence of cells and their image gradient. In this paper, we provide a novel method of segmenting vessels with a high level of cell related clutter. A set of virtual point pairs ("vessel probes") are moved reacting to forces including Vessel Vector Flow (VVF) and Vessel Boundary Vector Flow (VBVF) forces. Incorporating the cell detection, the VVF force attracts the probes toward the vessel, while the VBVF force attracts the virtual points of the probes to localize the vessel boundary without being distracted by the image features of the cells. The vessel probes are moved according to Newtonian Physics reacting to the net of forces applied on them. We demonstrate the results on a set of five real in vivo images of liver vasculature cluttered by white blood cells. When compared against the ground truth prepared by the technician, the Root Mean Squared Error (RMSE) of segmentation with VVF and VBVF was 55% lower than the method without VVF and VBVF.

Optical method for high magnification imaging and video recording of live cells at sub-micron resolution

NASA Astrophysics Data System (ADS)

Romo, Jaime E., Jr.

Optical microscopy, the most common technique for viewing living microorganisms, is limited in resolution by Abbe's criterion. Recent microscopy techniques focus on circumnavigating the light diffraction limit by using different methods to obtain the topography of the sample. Systems like the AFM and SEM provide images with fields of view in the nanometer range with high resolvable detail, however these techniques are expensive, and limited in their ability to document live cells. The Dino-Lite digital microscope coupled with the Zeiss Axiovert 25 CFL microscope delivers a cost-effective method for recording live cells. Fields of view ranging from 8 microns to 300 microns with fair resolution provide a reliable method for discovering native cell structures at the nanoscale. In this report, cultured HeLa cells are recorded using different optical configurations resulting in documentation of cell dynamics at high magnification and resolution.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.

PubMed

Xing, Fuyong; Yang, Lin

2016-01-01

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

Molecular Imaging of Human Embryonic Stem Cells Stably Expressing Human PET Reporter Genes After Zinc Finger Nuclease-Mediated Genome Editing.

PubMed

Wolfs, Esther; Holvoet, Bryan; Ordovas, Laura; Breuls, Natacha; Helsen, Nicky; Schönberger, Matthias; Raitano, Susanna; Struys, Tom; Vanbilloen, Bert; Casteels, Cindy; Sampaolesi, Maurilio; Van Laere, Koen; Lambrichts, Ivo; Verfaillie, Catherine M; Deroose, Christophe M

2017-10-01

Molecular imaging is indispensable for determining the fate and persistence of engrafted stem cells. Standard strategies for transgene induction involve the use of viral vectors prone to silencing and insertional mutagenesis or the use of nonhuman genes. Methods: We used zinc finger nucleases to induce stable expression of human imaging reporter genes into the safe-harbor locus adeno-associated virus integration site 1 in human embryonic stem cells. Plasmids were generated carrying reporter genes for fluorescence, bioluminescence imaging, and human PET reporter genes. Results: In vitro assays confirmed their functionality, and embryonic stem cells retained differentiation capacity. Teratoma formation assays were performed, and tumors were imaged over time with PET and bioluminescence imaging. Conclusion: This study demonstrates the application of genome editing for targeted integration of human imaging reporter genes in human embryonic stem cells for long-term molecular imaging. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

High-resolution imaging of basal cell carcinoma: a comparison between multiphoton microscopy with fluorescence lifetime imaging and reflectance confocal microscopy.

PubMed

Manfredini, Marco; Arginelli, Federica; Dunsby, Christopher; French, Paul; Talbot, Clifford; König, Karsten; Pellacani, Giovanni; Ponti, Giovanni; Seidenari, Stefania

2013-02-01

The aim of this study was to compare morphological aspects of basal cell carcinoma (BCC) as assessed by two different imaging methods: in vivo reflectance confocal microscopy (RCM) and multiphoton tomography with fluorescence lifetime imaging implementation (MPT-FLIM). The study comprised 16 BCCs for which a complete set of RCM and MPT-FLIM images were available. The presence of seven MPT-FLIM descriptors was evaluated. The presence of seven RCM equivalent parameters was scored in accordance to their extension. Chi-squared test with Fisher's exact test and Spearman's rank correlation coefficient were determined between MPT-FLIM scores and adjusted-RCM scores. MPT-FLIM and RCM descriptors of BCC were coupled to match the descriptors that define the same pathological structures. The comparison included: Streaming and Aligned elongated cells, Streaming with multiple directions and Double alignment, Palisading (RCM) and Palisading (MPT-FLIM), Typical tumor islands, and Cell islands surrounded by fibers, Dark silhouettes and Phantom islands, Plump bright cells and Melanophages, Vessels (RCM), and Vessels (MPT-FLIM). The parameters that were significantly correlated were Melanophages/Plump Bright Cells, Aligned elongated cells/Streaming, Double alignment/Streaming with multiple directions, and Palisading (MPT-FLIM)/Palisading (RCM). According to our data, both methods are suitable to image BCC's features. The concordance between MPT-FLIM and RCM is high, with some limitations due to the technical differences between the two devices. The hardest difficulty when comparing the images generated by the two imaging modalities is represented by their different field of view. © 2012 John Wiley & Sons A/S.

Classification of Shiga toxin-producing escherichia coli (STEC) serotypes with hyperspectral microscope imagery

NASA Astrophysics Data System (ADS)

Park, Bosoon; Windham, William R.; Ladely, Scott R.; Gurram, Prudhvi; Kwon, Heesung; Yoon, Seung-Chul; Lawrence, Kurt C.; Narang, Neelam; Cray, William C.

2012-05-01

Non-O157:H7 Shiga toxin-producing Escherichia coli (STEC) strains such as O26, O45, O103, O111, O121 and O145 are recognized as serious outbreak to cause human illness due to their toxicity. A conventional microbiological method for cell counting is laborious and needs long time for the results. Since optical detection method is promising for realtime, in-situ foodborne pathogen detection, acousto-optical tunable filters (AOTF)-based hyperspectral microscopic imaging (HMI) method has been developed for identifying pathogenic bacteria because of its capability to differentiate both spatial and spectral characteristics of each bacterial cell from microcolony samples. Using the AOTF-based HMI method, 89 contiguous spectral images could be acquired within approximately 30 seconds with 250 ms exposure time. From this study, we have successfully developed the protocol for live-cell immobilization on glass slides to acquire quality spectral images from STEC bacterial cells using the modified dry method. Among the contiguous spectral imagery between 450 and 800 nm, the intensity of spectral images at 458, 498, 522, 546, 570, 586, 670 and 690 nm were distinctive for STEC bacteria. With two different classification algorithms, Support Vector Machine (SVM) and Sparse Kernel-based Ensemble Learning (SKEL), a STEC serotype O45 could be classified with 92% detection accuracy.

In vivo imaging: shining a light on stem cells in the living animal.

PubMed

Nguyen, Phong Dang; Currie, Peter David

2018-03-28

Stem cells are undifferentiated cells that play crucial roles during development, growth and regeneration. Traditionally, these cells have been primarily characterised by histology, cell sorting, cell culture and ex vivo methods. However, as stem cells interact in a complex environment within specific tissue niches, there has been increasing interest in examining their in vivo behaviours, particularly in response to injury. Advances in imaging technologies and genetic tools have converged to enable unprecedented access to the endogenous stem cell niche. In this Spotlight article, we highlight how in vivo imaging can probe a range of biological processes that relate to stem cell activity, behaviour and control. © 2018. Published by The Company of Biologists Ltd.

Quantifying Solar Cell Cracks in Photovoltaic Modules by Electroluminescence Imaging

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

Spataru, Sergiu; Hacke, Peter; Sera, Dezso

2015-06-14

This article proposes a method for quantifying the percentage of partially and totally disconnected solar cell cracks by analyzing electroluminescence images of the photovoltaic module taken under high- and low-current forward bias. The method is based on the analysis of the module's electroluminescence intensity distribution, applied at module and cell level. These concepts are demonstrated on a crystalline silicon photovoltaic module that was subjected to several rounds of mechanical loading and humidity-freeze cycling, causing increasing levels of solar cell cracks. The proposed method can be used as a diagnostic tool to rate cell damage or quality of modules after transportation.more » Moreover, the method can be automated and used in quality control for module manufacturers, installers, or as a diagnostic tool by plant operators and diagnostic service providers.« less

Imaging in focus: Imaging the dynamics of endocytosis.

PubMed

Rosendale, Morgane; Perrais, David

2017-12-01

Endocytosis, the formation of membrane vesicles from the plasma membrane, is an essential feature of eukaryotic cell biology. Intense research effort has been dedicated to developing methods that can detect endocytosis events with the highest resolution. We have classified these methods into four families. They exploit the physical properties of endocytosis, namely: 1. Distinguishing extracellular from internalised cargo in fixed samples, 2. Monitoring endosomal acidification, 3. Measuring the turnover of endocytic zones and 4. Detecting vesicle scission. The last three families, all based on fluorescence imaging, are used to study endocytosis in living cells. We discuss the advantages and limitations of these methods and conclude on the future developments required to tackle the upcoming challenges in this fundamental field of cell biology. Copyright © 2017. Published by Elsevier Ltd.

Live visualization of genomic loci with BiFC-TALE

PubMed Central

Hu, Huan; Zhang, Hongmin; Wang, Sheng; Ding, Miao; An, Hui; Hou, Yingping; Yang, Xiaojing; Wei, Wensheng; Sun, Yujie; Tang, Chao

2017-01-01

Tracking the dynamics of genomic loci is important for understanding the mechanisms of fundamental intracellular processes. However, fluorescent labeling and imaging of such loci in live cells have been challenging. One of the major reasons is the low signal-to-background ratio (SBR) of images mainly caused by the background fluorescence from diffuse full-length fluorescent proteins (FPs) in the living nucleus, hampering the application of live cell genomic labeling methods. Here, combining bimolecular fluorescence complementation (BiFC) and transcription activator-like effector (TALE) technologies, we developed a novel method for labeling genomic loci (BiFC-TALE), which largely reduces the background fluorescence level. Using BiFC-TALE, we demonstrated a significantly improved SBR by imaging telomeres and centromeres in living cells in comparison with the methods using full-length FP. PMID:28074901

Live visualization of genomic loci with BiFC-TALE.

PubMed

Hu, Huan; Zhang, Hongmin; Wang, Sheng; Ding, Miao; An, Hui; Hou, Yingping; Yang, Xiaojing; Wei, Wensheng; Sun, Yujie; Tang, Chao

2017-01-11

Tracking the dynamics of genomic loci is important for understanding the mechanisms of fundamental intracellular processes. However, fluorescent labeling and imaging of such loci in live cells have been challenging. One of the major reasons is the low signal-to-background ratio (SBR) of images mainly caused by the background fluorescence from diffuse full-length fluorescent proteins (FPs) in the living nucleus, hampering the application of live cell genomic labeling methods. Here, combining bimolecular fluorescence complementation (BiFC) and transcription activator-like effector (TALE) technologies, we developed a novel method for labeling genomic loci (BiFC-TALE), which largely reduces the background fluorescence level. Using BiFC-TALE, we demonstrated a significantly improved SBR by imaging telomeres and centromeres in living cells in comparison with the methods using full-length FP.

An image cytometric technique is a concise method to detect adenoviruses and host cell proteins and to monitor the infection and cellular responses induced.

PubMed

Morinaga, Takao; Nguyễn, Thảo Thi Thanh; Zhong, Boya; Hanazono, Michiko; Shingyoji, Masato; Sekine, Ikuo; Tada, Yuji; Tatsumi, Koichiro; Shimada, Hideaki; Hiroshima, Kenzo; Tagawa, Masatoshi

2017-11-10

Genetically modified adenoviruses (Ad) with preferential replications in tumor cells have been examined for a possible clinical applicability as an anti-cancer agent. A simple method to detect viral and cellular proteins is valuable to monitor the viral infections and to predict the Ad-mediated cytotoxicity. We used type 5 Ad in which the expression of E1A gene was activated by 5'-regulatory sequences of genes that were augmented in the expression in human tumors. The Ad were further modified to have the fiber-knob region replaced with that derived from type 35 Ad. We infected human mesothelioma cells with the fiber-replaced Ad, and sequentially examined cytotoxic processes together with an expression level of the viral E1A, hexon, and cellular cleaved caspase-3 with image cytometric and Western blot analyses. The replication-competent Ad produced cytotoxicity on mesothelioma cells. The infected cells expressed E1A and hexon 24 h after the infection and then showed cleavage of caspase-3, all of which were detected with image cytometry and Western blot analysis. Image cytometry furthermore demonstrated that increased Ad doses did not enhance an expression level of E1A and hexon in an individual cell and that caspase-3-cleaved cells were found more frequently in hexon-positive cells than in E1A-positive cells. Image cytometry thus detected these molecular changes in a sensitive manner and at a single cell level. We also showed that an image cytometric technique detected expression changes of other host cell proteins, cyclin-E and phosphorylated histone H3 at a single cell level. Image cytometry is a concise procedure to detect expression changes of Ad and host cell proteins at a single cell level, and is useful to analyze molecular events after the infection.

Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting

PubMed Central

Schmitz, Christoph; Eastwood, Brian S.; Tappan, Susan J.; Glaser, Jack R.; Peterson, Daniel A.; Hof, Patrick R.

2014-01-01

Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “cell counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections. PMID:24847213

Coregistration of Magnetic Resonance and Single Photon Emission Computed Tomography Images for Noninvasive Localization of Stem Cells Grafted in the Infarcted Rat Myocardium

PubMed Central

Shen, Dinggang; Liu, Dengfeng; Cao, Zixiong; Acton, Paul D.; Zhou, Rong

2008-01-01

This paper demonstrates the application of mutual information based coregistration of radionuclide and magnetic resonance imaging (MRI) in an effort to use multimodality imaging for noninvasive localization of stem cells grafted in the infarcted myocardium in rats. Radionuclide imaging such as single photon emission computed tomography (SPECT) or positron emission tomography (PET) inherently has high sensitivity and is suitable for tracking of labeled stem cells, while high-resolution MRI is able to provide detailed anatomical and functional information of myocardium. Thus, coregistration of PET or SPECT images with MRI will map the location and distribution of stem cells on detailed myocardium structures. To validate this coregistration method, SPECT data were simulated by using a Monte Carlo-based projector that modeled the pinhole-imaging physics assuming nonzero diameter and photon penetration at the edge. Translational and rotational errors of the coregistration were examined with respect to various SPECT activities, and they are on average about 0.50 mm and 0.82°, respectively. Only the rotational error is dependent on activity of SPECT data. Stem cells were labeled with 111 Indium oxyquinoline and grafted in the ischemic myocardium of a rat model. Dual-tracer small-animal SPECT images were acquired, which allowed simultaneous detection of 111In-labeled stem cells and of [99mTc]sestamibi to assess myocardial perfusion deficit. The same animals were subjected to cardiac MRI. A mutual-information-based coregistration method was then applied to the SPECT and MRIs. By coregistration, the 111 In signal from labeled cells was mapped into the akinetic region identified on cine MRIs; the regional perfusion deficit on the SPECT images also coincided with the akinetic region on the MR image. PMID:17053860

Imaging of oxygenation in 3D tissue models with multi-modal phosphorescent probes

NASA Astrophysics Data System (ADS)

Papkovsky, Dmitri B.; Dmitriev, Ruslan I.; Borisov, Sergei

2015-03-01

Cell-penetrating phosphorescence based probes allow real-time, high-resolution imaging of O2 concentration in respiring cells and 3D tissue models. We have developed a panel of such probes, small molecule and nanoparticle structures, which have different spectral characteristics, cell penetrating and tissue staining behavior. The probes are compatible with conventional live cell imaging platforms and can be used in different detection modalities, including ratiometric intensity and PLIM (Phosphorescence Lifetime IMaging) under one- or two-photon excitation. Analytical performance of these probes and utility of the O2 imaging method have been demonstrated with different types of samples: 2D cell cultures, multi-cellular spheroids from cancer cell lines and primary neurons, excised slices from mouse brain, colon and bladder tissue, and live animals. They are particularly useful for hypoxia research, ex-vivo studies of tissue physiology, cell metabolism, cancer, inflammation, and multiplexing with many conventional fluorophors and markers of cellular function.

New Application of Hyperspectral Imaging for Bacterial Cell Classification

USDA-ARS?s Scientific Manuscript database

Hyperspectral microscopy has shown potential as a method for rapid detection of foodborne pathogenic bacteria with spectral characteristics from bacterial cells. Hyperspectral microscope images (HMIs) are collected from broiler chicken isolates of Salmonella serotypes Enteritidis, Typhimurium, Infa...

Segmentation of fluorescence microscopy images for quantitative analysis of cell nuclear architecture.

PubMed

Russell, Richard A; Adams, Niall M; Stephens, David A; Batty, Elizabeth; Jensen, Kirsten; Freemont, Paul S

2009-04-22

Considerable advances in microscopy, biophysics, and cell biology have provided a wealth of imaging data describing the functional organization of the cell nucleus. Until recently, cell nuclear architecture has largely been assessed by subjective visual inspection of fluorescently labeled components imaged by the optical microscope. This approach is inadequate to fully quantify spatial associations, especially when the patterns are indistinct, irregular, or highly punctate. Accurate image processing techniques as well as statistical and computational tools are thus necessary to interpret this data if meaningful spatial-function relationships are to be established. Here, we have developed a thresholding algorithm, stable count thresholding (SCT), to segment nuclear compartments in confocal laser scanning microscopy image stacks to facilitate objective and quantitative analysis of the three-dimensional organization of these objects using formal statistical methods. We validate the efficacy and performance of the SCT algorithm using real images of immunofluorescently stained nuclear compartments and fluorescent beads as well as simulated images. In all three cases, the SCT algorithm delivers a segmentation that is far better than standard thresholding methods, and more importantly, is comparable to manual thresholding results. By applying the SCT algorithm and statistical analysis, we quantify the spatial configuration of promyelocytic leukemia nuclear bodies with respect to irregular-shaped SC35 domains. We show that the compartments are closer than expected under a null model for their spatial point distribution, and furthermore that their spatial association varies according to cell state. The methods reported are general and can readily be applied to quantify the spatial interactions of other nuclear compartments.

Segmentation of Fluorescence Microscopy Images for Quantitative Analysis of Cell Nuclear Architecture

PubMed Central

Russell, Richard A.; Adams, Niall M.; Stephens, David A.; Batty, Elizabeth; Jensen, Kirsten; Freemont, Paul S.

2009-01-01

Abstract Considerable advances in microscopy, biophysics, and cell biology have provided a wealth of imaging data describing the functional organization of the cell nucleus. Until recently, cell nuclear architecture has largely been assessed by subjective visual inspection of fluorescently labeled components imaged by the optical microscope. This approach is inadequate to fully quantify spatial associations, especially when the patterns are indistinct, irregular, or highly punctate. Accurate image processing techniques as well as statistical and computational tools are thus necessary to interpret this data if meaningful spatial-function relationships are to be established. Here, we have developed a thresholding algorithm, stable count thresholding (SCT), to segment nuclear compartments in confocal laser scanning microscopy image stacks to facilitate objective and quantitative analysis of the three-dimensional organization of these objects using formal statistical methods. We validate the efficacy and performance of the SCT algorithm using real images of immunofluorescently stained nuclear compartments and fluorescent beads as well as simulated images. In all three cases, the SCT algorithm delivers a segmentation that is far better than standard thresholding methods, and more importantly, is comparable to manual thresholding results. By applying the SCT algorithm and statistical analysis, we quantify the spatial configuration of promyelocytic leukemia nuclear bodies with respect to irregular-shaped SC35 domains. We show that the compartments are closer than expected under a null model for their spatial point distribution, and furthermore that their spatial association varies according to cell state. The methods reported are general and can readily be applied to quantify the spatial interactions of other nuclear compartments. PMID:19383481

An entirely automated method to score DSS-induced colitis in mice by digital image analysis of pathology slides

PubMed Central

Kozlowski, Cleopatra; Jeet, Surinder; Beyer, Joseph; Guerrero, Steve; Lesch, Justin; Wang, Xiaoting; DeVoss, Jason; Diehl, Lauri

2013-01-01

SUMMARY The DSS (dextran sulfate sodium) model of colitis is a mouse model of inflammatory bowel disease. Microscopic symptoms include loss of crypt cells from the gut lining and infiltration of inflammatory cells into the colon. An experienced pathologist requires several hours per study to score histological changes in selected regions of the mouse gut. In order to increase the efficiency of scoring, Definiens Developer software was used to devise an entirely automated method to quantify histological changes in the whole H&E slide. When the algorithm was applied to slides from historical drug-discovery studies, automated scores classified 88% of drug candidates in the same way as pathologists’ scores. In addition, another automated image analysis method was developed to quantify colon-infiltrating macrophages, neutrophils, B cells and T cells in immunohistochemical stains of serial sections of the H&E slides. The timing of neutrophil and macrophage infiltration had the highest correlation to pathological changes, whereas T and B cell infiltration occurred later. Thus, automated image analysis enables quantitative comparisons between tissue morphology changes and cell-infiltration dynamics. PMID:23580198

Microscopic optical path length difference and polarization measurement system for cell analysis

NASA Astrophysics Data System (ADS)

Satake, H.; Ikeda, K.; Kowa, H.; Hoshiba, T.; Watanabe, E.

2018-03-01

In recent years, noninvasive, nonstaining, and nondestructive quantitative cell measurement techniques have become increasingly important in the medical field. These cell measurement techniques enable the quantitative analysis of living cells, and are therefore applied to various cell identification processes, such as those determining the passage number limit during cell culturing in regenerative medicine. To enable cell measurement, we developed a quantitative microscopic phase imaging system based on a Mach-Zehnder interferometer that measures the optical path length difference distribution without phase unwrapping using optical phase locking. The applicability of our phase imaging system was demonstrated by successful identification of breast cancer cells amongst normal cells. However, the cell identification method using this phase imaging system exhibited a false identification rate of approximately 7%. In this study, we implemented a polarimetric imaging system by introducing a polarimetric module to one arm of the Mach-Zehnder interferometer of our conventional phase imaging system. This module was comprised of a quarter wave plate and a rotational polarizer on the illumination side of the sample, and a linear polarizer on the optical detector side. In addition, we developed correction methods for the measurement errors of the optical path length and birefringence phase differences that arose through the influence of elements other than cells, such as the Petri dish. As the Petri dish holding the fluid specimens was transparent, it did not affect the amplitude information; however, the optical path length and birefringence phase differences were affected. Therefore, we proposed correction of the optical path length and birefringence phase for the influence of elements other than cells, as a prerequisite for obtaining highly precise phase and polarimetric images.

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

Fertig, Fabian, E-mail: fabian.fertig@ise.fraunhofer.de; Greulich, Johannes; Rein, Stefan

We present a spatially resolved method to determine the short-circuit current density of crystalline silicon solar cells by means of lock-in thermography. The method utilizes the property of crystalline silicon solar cells that the short-circuit current does not differ significantly from the illuminated current under moderate reverse bias. Since lock-in thermography images locally dissipated power density, this information is exploited to extract values of spatially resolved current density under short-circuit conditions. In order to obtain an accurate result, one or two illuminated lock-in thermography images and one dark lock-in thermography image need to be recorded. The method can be simplifiedmore » in a way that only one image is required to generate a meaningful short-circuit current density map. The proposed method is theoretically motivated, and experimentally validated for monochromatic illumination in comparison to the reference method of light-beam induced current.« less

Evaluating the extent of cell death in 3D high frequency ultrasound by registration with whole-mount tumor histopathology

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

Vlad, Roxana M.; Kolios, Michael C.; Moseley, Joanne L.

Purpose: High frequency ultrasound imaging, 10-30 MHz, has the capability to assess tumor response to radiotherapy in mouse tumors as early as 24 h after treatment administration. The advantage of this technique is that the image contrast is generated by changes in the physical properties of dying cells. Therefore, a subject can be imaged before and multiple times during the treatment without the requirement of injecting specialized contrast agents. This study is motivated by a need to provide metrics of comparison between the volume and localization of cell death, assessed from histology, with the volume and localization of cell deathmore » surrogate, assessed as regions with increased echogeneity from ultrasound images. Methods: The mice were exposed to radiation doses of 2, 4, and 8 Gy. Ultrasound images were collected from each tumor before and 24 h after exposure to radiation using a broadband 25 MHz center frequency transducer. After radiotherapy, tumors exhibited hyperechoic regions in ultrasound images that corresponded to areas of cell death in histology. The ultrasound and histological images were rigidly registered. The tumors and regions of cell death were manually outlined on histological images. Similarly, the tumors and hyperechoic regions were outlined on the ultrasound images. Each set of contours was converted to a volumetric mesh in order to compare the volumes and the localization of cell death in histological and ultrasound images. Results: A shrinkage factor of 17{+-}2% was calculated from the difference in the tumor volumes evaluated from histological and ultrasound images. This was used to correct the tumor and cell death volumes assessed from histology. After this correction, the average absolute difference between the volume of cell death assessed from ultrasound and histological images was 11{+-}14% and the volume overlap was 70{+-}12%. Conclusions: The method provided metrics of comparison between the volume of cell death assessed from histology and that assessed from ultrasound images. It was applied here to evaluate the capability of ultrasound imaging to assess early tumor response to radiotherapy in mouse tumors. Similarly, it can be applied in the future to evaluate the capability of ultrasound imaging to assess early tumor response to other modalities of cancer treatment. The study contributes to an understanding of the capabilities and limitation of ultrasound imaging at noninvasively detecting cell death. This provides a foundation for future developments regarding the use of ultrasound in preclinical and clinical applications to adapt treatments based on tumor response to cancer therapy.« less

A novel speckle pattern—Adaptive digital image correlation approach with robust strain calculation

NASA Astrophysics Data System (ADS)

Cofaru, Corneliu; Philips, Wilfried; Van Paepegem, Wim

2012-02-01

Digital image correlation (DIC) has seen widespread acceptance and usage as a non-contact method for the determination of full-field displacements and strains in experimental mechanics. The advances of imaging hardware in the last decades led to high resolution and speed cameras being more affordable than in the past making large amounts of data image available for typical DIC experimental scenarios. The work presented in this paper is aimed at maximizing both the accuracy and speed of DIC methods when employed with such images. A low-level framework for speckle image partitioning which replaces regularly shaped blocks with image-adaptive cells in the displacement calculation is introduced. The Newton-Raphson DIC method is modified to use the image pixels of the cells and to perform adaptive regularization to increase the spatial consistency of the displacements. Furthermore, a novel robust framework for strain calculation based also on the Newton-Raphson algorithm is introduced. The proposed methods are evaluated in five experimental scenarios, out of which four use numerically deformed images and one uses real experimental data. Results indicate that, as the desired strain density increases, significant computational gains can be obtained while maintaining or improving accuracy and rigid-body rotation sensitivity.

Optical texture analysis for automatic cytology and histology: a Markovian approach

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

Pressman, N.J.

1976-10-12

Markovian analysis is a method to measure optical texture based on gray-level transition probabilities in digitized images. The experiments described in this dissertation investigate the classification performance of parameters generated by this method. Three types of data sets are used: images of (1) human blood leukocytes (nuclei of monocytes, neutrophils, and lymphocytes; Wright stain; (0.125 ..mu..m)/sup 2//picture point), (2) cervical exfoliative cells (nuclei of normal intermediate squamous cells and dysplastic and carcinoma in situ cells; azure-A/Feulgen stain; (0.125 ..mu..m)/sup 2//picture point), and (3) lymph-node tissue sections (6-..mu..m thick sections from normal, acute lymphadenitis, and Hodgkin lymph nodes; hematoxylin and eosinmore » stain; (0.625 ..mu..m)/sup 2/ picture point). Each image consists of 128 x 128 picture points originally scanned with a 256 gray-level resolution. Each image class is defined by 75 images.« less

Live Cell in Vitro and in Vivo Imaging Applications: Accelerating Drug Discovery

PubMed Central

Isherwood, Beverley; Timpson, Paul; McGhee, Ewan J; Anderson, Kurt I; Canel, Marta; Serrels, Alan; Brunton, Valerie G; Carragher, Neil O

2011-01-01

Dynamic regulation of specific molecular processes and cellular phenotypes in live cell systems reveal unique insights into cell fate and drug pharmacology that are not gained from traditional fixed endpoint assays. Recent advances in microscopic imaging platform technology combined with the development of novel optical biosensors and sophisticated image analysis solutions have increased the scope of live cell imaging applications in drug discovery. We highlight recent literature examples where live cell imaging has uncovered novel insight into biological mechanism or drug mode-of-action. We survey distinct types of optical biosensors and associated analytical methods for monitoring molecular dynamics, in vitro and in vivo. We describe the recent expansion of live cell imaging into automated target validation and drug screening activities through the development of dedicated brightfield and fluorescence kinetic imaging platforms. We provide specific examples of how temporal profiling of phenotypic response signatures using such kinetic imaging platforms can increase the value of in vitro high-content screening. Finally, we offer a prospective view of how further application and development of live cell imaging technology and reagents can accelerate preclinical lead optimization cycles and enhance the in vitro to in vivo translation of drug candidates. PMID:24310493

Red Blood Cell Count Automation Using Microscopic Hyperspectral Imaging Technology.

PubMed

Li, Qingli; Zhou, Mei; Liu, Hongying; Wang, Yiting; Guo, Fangmin

2015-12-01

Red blood cell counts have been proven to be one of the most frequently performed blood tests and are valuable for early diagnosis of some diseases. This paper describes an automated red blood cell counting method based on microscopic hyperspectral imaging technology. Unlike the light microscopy-based red blood count methods, a combined spatial and spectral algorithm is proposed to identify red blood cells by integrating active contour models and automated two-dimensional k-means with spectral angle mapper algorithm. Experimental results show that the proposed algorithm has better performance than spatial based algorithm because the new algorithm can jointly use the spatial and spectral information of blood cells.

Recent Developments in Vascular Imaging Techniques in Tissue Engineering and Regenerative Medicine

PubMed Central

Upputuri, Paul Kumar; Sivasubramanian, Kathyayini; Mark, Chong Seow Khoon; Pramanik, Manojit

2015-01-01

Adequate vascularisation is key in determining the clinical outcome of stem cells and engineered tissue in regenerative medicine. Numerous imaging modalities have been developed and used for the visualization of vascularisation in tissue engineering. In this review, we briefly discuss the very recent advances aiming at high performance imaging of vasculature. We classify the vascular imaging modalities into three major groups: nonoptical methods (X-ray, magnetic resonance, ultrasound, and positron emission imaging), optical methods (optical coherence, fluorescence, multiphoton, and laser speckle imaging), and hybrid methods (photoacoustic imaging). We then summarize the strengths and challenges of these methods for preclinical and clinical applications. PMID:25821821

Steganography anomaly detection using simple one-class classification

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M.; Peterson, Gilbert L.; Agaian, Sos S.

2007-04-01

There are several security issues tied to multimedia when implementing the various applications in the cellular phone and wireless industry. One primary concern is the potential ease of implementing a steganography system. Traditionally, the only mechanism to embed information into a media file has been with a desktop computer. However, as the cellular phone and wireless industry matures, it becomes much simpler for the same techniques to be performed using a cell phone. In this paper, two methods are compared that classify cell phone images as either an anomaly or clean, where a clean image is one in which no alterations have been made and an anomalous image is one in which information has been hidden within the image. An image in which information has been hidden is known as a stego image. The main concern in detecting steganographic content with machine learning using cell phone images is in training specific embedding procedures to determine if the method has been used to generate a stego image. This leads to a possible flaw in the system when the learned model of stego is faced with a new stego method which doesn't match the existing model. The proposed solution to this problem is to develop systems that detect steganography as anomalies, making the embedding method irrelevant in detection. Two applicable classification methods for solving the anomaly detection of steganographic content problem are single class support vector machines (SVM) and Parzen-window. Empirical comparison of the two approaches shows that Parzen-window outperforms the single class SVM most likely due to the fact that Parzen-window generalizes less.

Tracking the Spatial and Functional Gradient of Monocyte-To-Macrophage Differentiation in Inflamed Lung.

PubMed

Sen, Debasish; Jones, Stephen M; Oswald, Erin M; Pinkard, Henry; Corbin, Kaitlin; Krummel, Matthew F

2016-01-01

Myeloid-derived cells such as monocytes, dendritic cells (DCs), and macrophages are at the heart of the immune effector function in an inflammatory response. But because of the lack of an efficient imaging system to trace these cells live during their migration and maturation in their native environment at sub-cellular resolution, our knowledge is limited to data available from specific time-points analyzed by flow cytometry, histology, genomics and other immunological methods. Here, we have developed a ratiometric imaging method for measuring monocyte maturation in inflamed mouse lungs in situ using real-time using 2-photon imaging and complementary methods. We visualized that while undifferentiated monocytes were predominantly found only in the vasculature, a semi-differentiated monocyte/macrophage population could enter the tissue and resembled more mature and differentiated populations by morphology and surface phenotype. As these cells entered and differentiated, they were already selectively localized near inflamed airways and their entry was associated with changes in motility and morphology. We were able to visualize these during the act of differentiation, a process that can be demonstrated in this way to be faster on a per-cell basis under inflammatory conditions. Finally, our in situ analyses demonstrated increases, in the differentiating cells, for both antigen uptake and the ability to mediate interactions with T cells. This work, while largely confirming proposed models for in situ differentiation, provides important in situ data on the coordinated site-specific recruitment and differentiation of these cells and helps elaborate the predominance of immune pathology at the airways. Our novel imaging technology to trace immunogenic cell maturation in situ will complement existing information available on in situ differentiation deduced from other immunological methods, and assist better understanding of the spatio-temporal cellular behavior during an inflammatory response.

Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields

PubMed Central

Robinson, Sean; Guyon, Laurent; Nevalainen, Jaakko; Toriseva, Mervi

2015-01-01

Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy. PMID:26630674

Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields.

PubMed

Robinson, Sean; Guyon, Laurent; Nevalainen, Jaakko; Toriseva, Mervi; Åkerfelt, Malin; Nees, Matthias

2015-01-01

Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.

[Clinical applications of molecular imaging methods for patients with ischemic stroke].

PubMed

Yamauchi, Hiroshi; Fukuyama, Hidenao

2007-02-01

Several molecular imaging methods have been developed to visualize pathophysiology of cerebral ischemia in humans in vivo. PET and SPECT with specific ligands have been mainly used as diagnostic tools for the clinical usage of molecular imaging in patients with ischemic stroke. Recently, cellular MR imaging with specific contrast agents has been developed to visualize targeted cells in human stroke patients. This article reviews the current status in the clinical applications of those molecular imaging methods for patients with ischemic stroke.

A multispectral imaging approach for diagnostics of skin pathologies

NASA Astrophysics Data System (ADS)

Lihacova, Ilze; Derjabo, Aleksandrs; Spigulis, Janis

2013-06-01

Noninvasive multispectral imaging method was applied for different skin pathology such as nevus, basal cell carcinoma, and melanoma diagnostics. Developed melanoma diagnostic parameter, using three spectral bands (540 nm, 650 nm and 950 nm), was calculated for nevus, melanoma and basal cell carcinoma. Simple multispectral diagnostic device was established and applied for skin assessment. Development and application of multispectral diagnostics method described further in this article.

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis.

PubMed

Collins, Tony J; Ylanko, Jarkko; Geng, Fei; Andrews, David W

2015-11-01

A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose-response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds.

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis

PubMed Central

Collins, Tony J.; Ylanko, Jarkko; Geng, Fei

2015-01-01

Abstract A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose–response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds. PMID:26422066

Image classification of unlabeled malaria parasites in red blood cells.

PubMed

Zheng Zhang; Ong, L L Sharon; Kong Fang; Matthew, Athul; Dauwels, Justin; Ming Dao; Asada, Harry

2016-08-01

This paper presents a method to detect unlabeled malaria parasites in red blood cells. The current "gold standard" for malaria diagnosis is microscopic examination of thick blood smear, a time consuming process requiring extensive training. Our goal is to develop an automate process to identify malaria infected red blood cells. Major issues in automated analysis of microscopy images of unstained blood smears include overlapping cells and oddly shaped cells. Our approach creates robust templates to detect infected and uninfected red cells. Histogram of Oriented Gradients (HOGs) features are extracted from templates and used to train a classifier offline. Next, the ViolaJones object detection framework is applied to detect infected and uninfected red cells and the image background. Results show our approach out-performs classification approaches with PCA features by 50% and cell detection algorithms applying Hough transforms by 24%. Majority of related work are designed to automatically detect stained parasites in blood smears where the cells are fixed. Although it is more challenging to design algorithms for unstained parasites, our methods will allow analysis of parasite progression in live cells under different drug treatments.

Lineage mapper: A versatile cell and particle tracker

NASA Astrophysics Data System (ADS)

Chalfoun, Joe; Majurski, Michael; Dima, Alden; Halter, Michael; Bhadriraju, Kiran; Brady, Mary

2016-11-01

The ability to accurately track cells and particles from images is critical to many biomedical problems. To address this, we developed Lineage Mapper, an open-source tracker for time-lapse images of biological cells, colonies, and particles. Lineage Mapper tracks objects independently of the segmentation method, detects mitosis in confluence, separates cell clumps mistakenly segmented as a single cell, provides accuracy and scalability even on terabyte-sized datasets, and creates division and/or fusion lineages. Lineage Mapper has been tested and validated on multiple biological and simulated problems. The software is available in ImageJ and Matlab at isg.nist.gov.

Quantitative imaging assay for NF-κB nuclear translocation in primary human macrophages

PubMed Central

Noursadeghi, Mahdad; Tsang, Jhen; Haustein, Thomas; Miller, Robert F.; Chain, Benjamin M.; Katz, David R.

2008-01-01

Quantitative measurement of NF-κB nuclear translocation is an important research tool in cellular immunology. Established methodologies have a number of limitations, such as poor sensitivity, high cost or dependence on cell lines. Novel imaging methods to measure nuclear translocation of transcriptionally active components of NF-κB are being used but are also partly limited by the need for specialist imaging equipment or image analysis software. Herein we present a method for quantitative detection of NF-κB rel A nuclear translocation, using immunofluorescence microscopy and the public domain image analysis software ImageJ that can be easily adopted for cellular immunology research without the need for specialist image analysis expertise and at low cost. The method presented here is validated by demonstrating the time course and dose response of NF-κB nuclear translocation in primary human macrophages stimulated with LPS, and by comparison with a commercial NF-κB activation reporter cell line. PMID:18036607

Nanoscale imaging of whole cells using a liquid enclosure and a scanning transmission electron microscope.

PubMed

Peckys, Diana B; Veith, Gabriel M; Joy, David C; de Jonge, Niels

2009-12-14

Nanoscale imaging techniques are needed to investigate cellular function at the level of individual proteins and to study the interaction of nanomaterials with biological systems. We imaged whole fixed cells in liquid state with a scanning transmission electron microscope (STEM) using a micrometer-sized liquid enclosure with electron transparent windows providing a wet specimen environment. Wet-STEM images were obtained of fixed E. coli bacteria labeled with gold nanoparticles attached to surface membrane proteins. Mammalian cells (COS7) were incubated with gold-tagged epidermal growth factor and fixed. STEM imaging of these cells resulted in a resolution of 3 nm for the gold nanoparticles. The wet-STEM method has several advantages over conventional imaging techniques. Most important is the capability to image whole fixed cells in a wet environment with nanometer resolution, which can be used, e.g., to map individual protein distributions in/on whole cells. The sample preparation is compatible with that used for fluorescent microscopy on fixed cells for experiments involving nanoparticles. Thirdly, the system is rather simple and involves only minimal new equipment in an electron microscopy (EM) laboratory.

Unravel lipid accumulation mechanism in oleaginous yeast through single cell systems biology study

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

Xie, Xiaoliang; Ding, Shiyou

Searching for alternative and clean energy is one of the most important tasks today. Our research aimed at finding the best living condition for certain types of oleaginous yeasts for efficient lipid production. We found that R. glutinis yeast cells has great variability in lipid production among cells while Y. lipolytica cells has similar oil production ability. We found some individual cells shows much higher level of oil production. In order to further study these cases, we employed a label-free chemical sensitive microscopy method call stimulated Raman scattering (SRS). With SRS, we could measure the lipid content in each cell.more » We combined SRS microscopy with microfluidic device so that we can isolate cells with high fat content. We also developed SRS imaging technique that has higher imaging speed, which is highly desirable for high throughput cell screening and sorting. Since these cells has similar genome, it must be the transcriptome caused their difference in oil production. We developed a single cell transcriptome sequencing method to study which genes are responsible for elevated oil production. These methods that are developed for this project can easily be applied for many other areas of research. For example, the single transcriptome can be used to study the transcriptomes of other cell types. The high-speed SRS microscopy techniques can be used to speed up chemical imaging for lablefree histology or imaging distribution of chemicals in tissues of live mice or in humans. The developed microfluidic platform can be used to sort other type of cells, e.g., white blood cells for diagnosis of cancer or other blood diseases.« less

Thiol Specific and Mitochondria Selective Fluorogenic Benzofurazan Sulfide for Live Cell Nonprotein Thiol Imaging and Quantification in Mitochondria.

PubMed

Wang, Shenggang; Yin, Huihui; Huang, Yue; Guan, Xiangming

2018-06-11

Cellular thiols are divided into two major categories: nonprotein thiols (NPSH) and protein thiols (PSH). Thiols are unevenly distributed inside the cell and compartmentalized in subcellular structures. Most of our knowledge on functions/dysfunctions of cellular/subcellular thiols is based on the quantification of cellular/subcellular thiols through homogenization of cellular/subcellular structures followed by a thiol quantification method. We would like to report a thiol-specific mitochondria-selective fluorogenic benzofurazan sulfide {7,7'-thiobis( N-rhodamine-benzo[c][1,2,5]oxadiazole-4-sulfonamide) (TBROS)} that can effectively image and quantify live cell NPSH in mitochondria through fluorescence intensity. Limited methods are available for imaging thiols in mitochondria in live cells especially in a quantitative manner. The thiol specificity of TBROS was demonstrated by its ability to react with thiols and inability to react with biologically relevant nucleophilic functional groups other than thiols. TBROS, with minimal fluorescence, formed strong fluorescent thiol adducts (λ ex = 550 nm, λ em = 580 nm) when reacting with NPSH confirming its fluorogenicity. TBROS failed to react with PSH from bovine serum albumin and cell homogenate proteins. The high mitochondrial thiol selectivity of TBROS was achieved by its mitochondria targeting structure and its higher reaction rate with NPSH at mitochondrial pH. Imaging of mitochondrial NPSH in live cells was confirmed by two colocalization methods and use of a thiol-depleting reagent. TBROS effectively imaged NPSH changes in a quantitative manner in mitochondria in live cells. The reagent will be a useful tool in exploring physiological and pathological roles of mitochondrial thiols.

Fluorescence of Picrosirius Red Multiplexed With Immunohistochemistry for the Quantitative Assessment of Collagen in Tissue Sections.

PubMed

Wegner, Kyle A; Keikhosravi, Adib; Eliceiri, Kevin W; Vezina, Chad M

2017-08-01

The low cost and simplicity of picrosirius red (PSR) staining have driven its popularity for collagen detection in tissue sections. We extended the versatility of this method by using fluorescent imaging to detect the PSR signal and applying automated quantification tools. We also developed the first PSR protocol that is fully compatible with multiplex immunostaining, making it possible to test whether collagen structure differs across immunohistochemically labeled regions of the tissue landscape. We compared our imaging method with two gold standards in collagen imaging, linear polarized light microscopy and second harmonic generation imaging, and found that it is at least as sensitive and robust to changes in sample orientation. As proof of principle, we used a genetic approach to overexpress beta catenin in a patchy subset of mouse prostate epithelial cells distinguished only by immunolabeling. We showed that collagen fiber length is significantly greater near beta catenin overexpressing cells than near control cells. Our fluorescent PSR imaging method is sensitive, reproducible, and offers a new way to guide region of interest selection for quantifying collagen in tissue sections.

A new fluorescence/PET probe for targeting intracellular human telomerase reverse transcriptase (hTERT) using Tat peptide-conjugated IgM

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

Jung, Kyung oh; Biomedical Sciences, Seoul National University College of Medicine; Cancer Research Institute, Seoul National University College of Medicine

Despite an increasing need for methods to visualize intracellular proteins in vivo, the majority of antibody-based imaging methods available can only detect membrane proteins. The human telomerase reverse transcriptase (hTERT) is an intracellular target of great interest because of its high expression in several types of cancer. In this study, we developed a new probe for hTERT using the Tat peptide. An hTERT antibody (IgG or IgM) was conjugated with the Tat peptide, a fluorescence dye and {sup 64}Cu. HT29 (hTERT+) and U2OS (hTERT−) were used to visualize the intracellular hTERT. The hTERT was detected by RT-PCR and western blot. Fluorescencemore » signals for hTERT were obtained by confocal microscopy, live cell imaging, and analyzed by Tissue-FAXS. In nude mice, tumors were visualized using the fluorescence imaging devices Maestro™ and PETBOX. In RT-PCR and western blot, the expression of hTERT was detected in HT29 cells, but not in U2OS cells. Fluorescence signals were clearly observed in HT29 cells and in U2OS cells after 1 h of treatment, but signals were only detected in HT29 cells after 24 h. Confocal microscopy showed that 9.65% of U2OS and 78.54% of HT29 cells had positive hTERT signals. 3D animation images showed that the probe could target intranuclear hTERT in the nucleus. In mice models, fluorescence and PET imaging showed that hTERT in HT29 tumors could be efficiently visualized. In summary, we developed a new method to visualize intracellular and intranuclear proteins both in vitro and in vivo. - Highlights: • We developed new probes for imaging hTERT using Tat-conjugated IgM antibodies labeled with a fluorescent dye and radioisotope. • This probes could be used to overcome limitation of conventional antibody imaging system in live cell imaging. • This system could be applicable to monitor intracellular and intranuclear proteins in vitro and in vivo.« less

Quantitative phase microscopy for cellular dynamics based on transport of intensity equation.

PubMed

Li, Ying; Di, Jianglei; Ma, Chaojie; Zhang, Jiwei; Zhong, Jinzhan; Wang, Kaiqiang; Xi, Teli; Zhao, Jianlin

2018-01-08

We demonstrate a simple method for quantitative phase imaging of tiny transparent objects such as living cells based on the transport of intensity equation. The experiments are performed using an inverted bright field microscope upgraded with a flipping imaging module, which enables to simultaneously create two laterally separated images with unequal defocus distances. This add-on module does not include any lenses or gratings and is cost-effective and easy-to-alignment. The validity of this method is confirmed by the measurement of microlens array and human osteoblastic cells in culture, indicating its potential in the applications of dynamically measuring living cells and other transparent specimens in a quantitative, non-invasive and label-free manner.

Use of Nanoparticle Contrast Agents for Cell Tracking with Computed Tomography

PubMed Central

2017-01-01

Efforts to develop novel cell-based therapies originated with the first bone marrow transplant on a leukemia patient in 1956. Preclinical and clinical examples of cell-based treatment strategies have shown promising results across many disciplines in medicine, with recent advances in immune cell therapies for cancer producing remarkable response rates, even in patients with multiple treatment failures. However, cell-based therapies suffer from inconsistent outcomes, motivating the search for tools that allow monitoring of cell delivery and behavior in vivo. Noninvasive cell imaging techniques, also known as cell tracking, have been developed to address this issue. These tools can allow real-time, quantitative, and long-term monitoring of transplanted cells in the recipient, providing insight on cell migration, distribution, viability, differentiation, and fate, all of which play crucial roles in treatment efficacy. Understanding these parameters allows the optimization of cell choice, delivery route, and dosage for therapy and advances cell-based therapy for specific clinical uses. To date, most cell tracking work has centered on imaging modalities such as MRI, radionuclide imaging, and optical imaging. However, X-ray computed tomography (CT) is an emerging method for cell tracking that has several strengths such as high spatial and temporal resolution, and excellent quantitative capabilities. The advantages of CT for cell tracking are enhanced by its wide availability and cost effectiveness, allowing CT to become one of the most popular clinical imaging modalities and a key asset in disease diagnosis. In this review, we will discuss recent advances in cell tracking methods using X-ray CT in various applications, in addition to predictions on how the field will progress. PMID:28485976

Identification and High-Resolution Imaging of α-Tocopherol from Human Cells to Whole Animals by TOF-SIMS Tandem Mass Spectrometry

NASA Astrophysics Data System (ADS)

Bruinen, Anne L.; Fisher, Gregory L.; Balez, Rachelle; van der Sar, Astrid M.; Ooi, Lezanne; Heeren, Ron M. A.

2018-06-01

A unique method for identification of biomolecular components in different biological specimens, while preserving the capability for high speed 2D and 3D molecular imaging, is employed to investigate cellular response to oxidative stress. The employed method enables observing the distribution of the antioxidant α-tocopherol and other molecules in cellular structures via time-of-flight secondary ion mass spectrometry (TOF-SIMS (MS1)) imaging in parallel with tandem mass spectrometry (MS2) imaging, collected simultaneously. The described method is employed to examine a network formed by neuronal cells differentiated from human induced pluripotent stem cells (iPSCs), a model for investigating human neurons in vitro. The antioxidant α-tocopherol is identified in situ within different cellular layers utilizing a 3D TOF-SIMS tandem MS imaging analysis. As oxidative stress also plays an important role in mediating inflammation, the study was expanded to whole body tissue sections of M. marinum-infected zebrafish, a model organism for tuberculosis. The TOF-SIMS tandem MS imaging results reveal an increased presence of α-tocopherol in response to the pathogen. [Figure not available: see fulltext.

Modeling of optical quadrature microscopy for imaging mouse embryos

NASA Astrophysics Data System (ADS)

Warger, William C., II; DiMarzio, Charles A.

2008-02-01

Optical quadrature microscopy (OQM) has been shown to provide the optical path difference through a mouse embryo, and has led to a novel method to count the total number of cells further into development than current non-toxic imaging techniques used in the clinic. The cell counting method has the potential to provide an additional quantitative viability marker for blastocyst transfer during in vitro fertilization. OQM uses a 633 nm laser within a modified Mach-Zehnder interferometer configuration to measure the amplitude and phase of the signal beam that travels through the embryo. Four cameras preceded by multiple beamsplitters record the four interferograms that are used within a reconstruction algorithm to produce an image of the complex electric field amplitude. Here we present a model for the electric field through the primary optical components in the imaging configuration and the reconstruction algorithm to calculate the signal to noise ratio when imaging mouse embryos. The model includes magnitude and phase errors in the individual reference and sample paths, fixed pattern noise, and noise within the laser and detectors. This analysis provides the foundation for determining the imaging limitations of OQM and the basis to optimize the cell counting method in order to introduce additional quantitative viability markers.

Automated analysis of clonal cancer cells by intravital imaging

PubMed Central

Coffey, Sarah Earley; Giedt, Randy J; Weissleder, Ralph

2013-01-01

Longitudinal analyses of single cell lineages over prolonged periods have been challenging particularly in processes characterized by high cell turn-over such as inflammation, proliferation, or cancer. RGB marking has emerged as an elegant approach for enabling such investigations. However, methods for automated image analysis continue to be lacking. Here, to address this, we created a number of different multicolored poly- and monoclonal cancer cell lines for in vitro and in vivo use. To classify these cells in large scale data sets, we subsequently developed and tested an automated algorithm based on hue selection. Our results showed that this method allows accurate analyses at a fraction of the computational time required by more complex color classification methods. Moreover, the methodology should be broadly applicable to both in vitro and in vivo analyses. PMID:24349895

Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens

PubMed Central

Yin, Zheng; Zhou, Xiaobo; Bakal, Chris; Li, Fuhai; Sun, Youxian; Perrimon, Norbert; Wong, Stephen TC

2008-01-01

Background The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens. Results Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms. Conclusion We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens. PMID:18534020

Method for enhancing cell penetration of Gd3+-based MRI contrast agents by conjugation with hydrophobic fluorescent dyes.

PubMed

Yamane, Takehiro; Hanaoka, Kenjiro; Muramatsu, Yasuaki; Tamura, Keita; Adachi, Yusuke; Miyashita, Yasushi; Hirata, Yasunobu; Nagano, Tetsuo

2011-11-16

Gadolinium ion (Gd(3+)) complexes are commonly used as magnetic resonance imaging (MRI) contrast agents to enhance signals in T(1)-weighted MR images. Recently, several methods to achieve cell-permeation of Gd(3+) complexes have been reported, but more general and efficient methodology is needed. In this report, we describe a novel method to achieve cell permeation of Gd(3+) complexes by using hydrophobic fluorescent dyes as a cell-permeability-enhancing unit. We synthesized Gd(3+) complexes conjugated with boron dipyrromethene (BDP-Gd) and Cy7 dye (Cy7-Gd), and showed that these conjugates can be introduced efficiently into cells. To examine the relationship between cell permeability and dye structure, we further synthesized a series of Cy7-Gd derivatives. On the basis of MR imaging, flow cytometry, and ICP-MS analysis of cells loaded with Cy7-Gd derivatives, highly hydrophobic and nonanionic dyes were effective for enhancing cell permeation of Gd(3+) complexes. Furthermore, the behavior of these Cy7-Gd derivatives was examined in mice. Thus, conjugation of hydrophobic fluorescent dyes appears to be an effective approach to improve the cell permeability of Gd(3+) complexes, and should be applicable for further development of Gd(3+)-based MRI contrast agents.

In-Vivo Imaging of Cell Migration Using Contrast Enhanced MRI and SVM Based Post-Processing.

PubMed

Weis, Christian; Hess, Andreas; Budinsky, Lubos; Fabry, Ben

2015-01-01

The migration of cells within a living organism can be observed with magnetic resonance imaging (MRI) in combination with iron oxide nanoparticles as an intracellular contrast agent. This method, however, suffers from low sensitivity and specificty. Here, we developed a quantitative non-invasive in-vivo cell localization method using contrast enhanced multiparametric MRI and support vector machines (SVM) based post-processing. Imaging phantoms consisting of agarose with compartments containing different concentrations of cancer cells labeled with iron oxide nanoparticles were used to train and evaluate the SVM for cell localization. From the magnitude and phase data acquired with a series of T2*-weighted gradient-echo scans at different echo-times, we extracted features that are characteristic for the presence of superparamagnetic nanoparticles, in particular hyper- and hypointensities, relaxation rates, short-range phase perturbations, and perturbation dynamics. High detection quality was achieved by SVM analysis of the multiparametric feature-space. The in-vivo applicability was validated in animal studies. The SVM detected the presence of iron oxide nanoparticles in the imaging phantoms with high specificity and sensitivity with a detection limit of 30 labeled cells per mm3, corresponding to 19 μM of iron oxide. As proof-of-concept, we applied the method to follow the migration of labeled cancer cells injected in rats. The combination of iron oxide labeled cells, multiparametric MRI and a SVM based post processing provides high spatial resolution, specificity, and sensitivity, and is therefore suitable for non-invasive in-vivo cell detection and cell migration studies over prolonged time periods.

Global gray-level thresholding based on object size.

PubMed

Ranefall, Petter; Wählby, Carolina

2016-04-01

In this article, we propose a fast and robust global gray-level thresholding method based on object size, where the selection of threshold level is based on recall and maximum precision with regard to objects within a given size interval. The method relies on the component tree representation, which can be computed in quasi-linear time. Feature-based segmentation is especially suitable for biomedical microscopy applications where objects often vary in number, but have limited variation in size. We show that for real images of cell nuclei and synthetic data sets mimicking fluorescent spots the proposed method is more robust than all standard global thresholding methods available for microscopy applications in ImageJ and CellProfiler. The proposed method, provided as ImageJ and CellProfiler plugins, is simple to use and the only required input is an interval of the expected object sizes. © 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

Quantitative in vivo cell-surface receptor imaging in oncology: kinetic modeling & paired-agent principles from nuclear medicine and optical imaging

PubMed Central

Tichauer, Kenneth M.; Wang, Yu; Pogue, Brian W.; Liu, Jonathan T. C.

2015-01-01

The development of methods to accurately quantify cell-surface receptors in living tissues would have a seminal impact in oncology. For example, accurate measures of receptor density in vivo could enhance early detection or surgical resection of tumors via protein-based contrast, allowing removal of cancer with high phenotype specificity. Alternatively, accurate receptor expression estimation could be used as a biomarker to guide patient-specific clinical oncology targeting of the same molecular pathway. Unfortunately, conventional molecular contrast-based imaging approaches are not well adapted to accurately estimating the nanomolar-level cell-surface receptor concentrations in tumors, as most images are dominated by nonspecific sources of contrast such as high vascular permeability and lymphatic inhibition. This article reviews approaches for overcoming these limitations based upon tracer kinetic modeling and the use of emerging protocols to estimate binding potential and the related receptor concentration. Methods such as using single time point imaging or a reference-tissue approach tend to have low accuracy in tumors, whereas paired-agent methods or advanced kinetic analyses are more promising to eliminate the dominance of interstitial space in the signals. Nuclear medicine and optical molecular imaging are the primary modalities used, as they have the nanomolar level sensitivity needed to quantify cell-surface receptor concentrations present in tissue, although each likely has a different clinical niche. PMID:26134619

Accurate cell counts in live mouse embryos using optical quadrature and differential interference contrast microscopy

NASA Astrophysics Data System (ADS)

Warger, William C., II; Newmark, Judith A.; Zhao, Bing; Warner, Carol M.; DiMarzio, Charles A.

2006-02-01

Present imaging techniques used in in vitro fertilization (IVF) clinics are unable to produce accurate cell counts in developing embryos past the eight-cell stage. We have developed a method that has produced accurate cell counts in live mouse embryos ranging from 13-25 cells by combining Differential Interference Contrast (DIC) and Optical Quadrature Microscopy. Optical Quadrature Microscopy is an interferometric imaging modality that measures the amplitude and phase of the signal beam that travels through the embryo. The phase is transformed into an image of optical path length difference, which is used to determine the maximum optical path length deviation of a single cell. DIC microscopy gives distinct cell boundaries for cells within the focal plane when other cells do not lie in the path to the objective. Fitting an ellipse to the boundary of a single cell in the DIC image and combining it with the maximum optical path length deviation of a single cell creates an ellipsoidal model cell of optical path length deviation. Subtracting the model cell from the Optical Quadrature image will either show the optical path length deviation of the culture medium or reveal another cell underneath. Once all the boundaries are used in the DIC image, the subtracted Optical Quadrature image is analyzed to determine the cell boundaries of the remaining cells. The final cell count is produced when no more cells can be subtracted. We have produced exact cell counts on 5 samples, which have been validated by Epi-Fluorescence images of Hoechst stained nuclei.

Real-time assessment of corneal endothelial cell damage following graft preparation and donor insertion for DMEK

PubMed Central

Bhogal, Maninder; Lwin, Chan N.; Seah, Xin-Yi; Murugan, Elavazhagan; Adnan, Khadijah; Lin, Shu-Jun; Mehta, Jodhbir S.

2017-01-01

Purpose To establish a method for assessing graft viability, in-vivo, following corneal transplantation. Methods Optimization of calcein AM fluorescence and toxicity assessment was performed in cultured human corneal endothelial cells and ex-vivo corneal tissue. Descemet membrane endothelial keratoplasty grafts were incubated with calcein AM and imaged pre and post preparation, and in-situ after insertion and unfolding in a pig eye model. Global, macroscopic images of the entire graft and individual cell resolution could be attained by altering the magnification of a clinical confocal scanning laser microscope. Patterns of cell loss observed in situ were compared to those seen using standard ex-vivo techniques. Results Calcein AM showed a positive dose-fluorescence relationship. A dose of 2.67μmol was sufficient to allow clear discrimination between viable and non-viable areas (sensitivity of 96.6% with a specificity of 96.1%) and was not toxic to cultured endothelial cells or ex-vivo corneal tissue. Patterns of cell loss seen in-situ closely matched those seen on ex-vivo assessment with fluorescence viability imaging, trypan blue/alizarin red staining or scanning electron microscopy. Iatrogenic graft damage from preparation and insertion varied between 7–35% and incarceration of the graft tissue within surgical wounds was identified as a significant cause of endothelial damage. Conclusions In-situ graft viability assessment using clinical imaging devices provides comparable information to ex-vivo methods. This method shows high sensitivity and specificity, is non-toxic and can be used to evaluate immediate cell viability in new grafting techniques in-vivo. PMID:28977017

Live-cell stimulated Raman scattering imaging of alkyne-tagged biomolecules.

PubMed

Hong, Senlian; Chen, Tao; Zhu, Yuntao; Li, Ang; Huang, Yanyi; Chen, Xing

2014-06-02

Alkynes can be metabolically incorporated into biomolecules including nucleic acids, proteins, lipids, and glycans. In addition to the clickable chemical reactivity, alkynes possess a unique Raman scattering within the Raman-silent region of a cell. Coupling this spectroscopic signature with Raman microscopy yields a new imaging modality beyond fluorescence and label-free microscopies. The bioorthogonal Raman imaging of various biomolecules tagged with an alkyne by a state-of-the-art Raman imaging technique, stimulated Raman scattering (SRS) microscopy, is reported. This imaging method affords non-invasiveness, high sensitivity, and molecular specificity and therefore should find broad applications in live-cell imaging. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantum dots-based double imaging combined with organic dye imaging to establish an automatic computerized method for cancer Ki67 measurement.

PubMed

Wang, Lin-Wei; Qu, Ai-Ping; Liu, Wen-Lou; Chen, Jia-Mei; Yuan, Jing-Ping; Wu, Han; Li, Yan; Liu, Juan

2016-02-03

As a widely used proliferative marker, Ki67 has important impacts on cancer prognosis, especially for breast cancer (BC). However, variations in analytical practice make it difficult for pathologists to manually measure Ki67 index. This study is to establish quantum dots (QDs)-based double imaging of nuclear Ki67 as red signal by QDs-655, cytoplasmic cytokeratin (CK) as yellow signal by QDs-585, and organic dye imaging of cell nucleus as blue signal by 4',6-diamidino-2-phenylindole (DAPI), and to develop a computer-aided automatic method for Ki67 index measurement. The newly developed automatic computerized Ki67 measurement could efficiently recognize and count Ki67-positive cancer cell nuclei with red signals and cancer cell nuclei with blue signals within cancer cell cytoplasmic with yellow signals. Comparisons of computerized Ki67 index, visual Ki67 index, and marked Ki67 index for 30 patients of 90 images with Ki67 ≤ 10% (low grade), 10% < Ki67 < 50% (moderate grade), and Ki67 ≥ 50% (high grade) showed computerized Ki67 counting is better than visual Ki67 counting, especially for Ki67 low and moderate grades. Based on QDs-based double imaging and organic dye imaging on BC tissues, this study successfully developed an automatic computerized Ki67 counting method to measure Ki67 index.

A novel measure and significance testing in data analysis of cell image segmentation.

PubMed

Wu, Jin Chu; Halter, Michael; Kacker, Raghu N; Elliott, John T; Plant, Anne L

2017-03-14

Cell image segmentation (CIS) is an essential part of quantitative imaging of biological cells. Designing a performance measure and conducting significance testing are critical for evaluating and comparing the CIS algorithms for image-based cell assays in cytometry. Many measures and methods have been proposed and implemented to evaluate segmentation methods. However, computing the standard errors (SE) of the measures and their correlation coefficient is not described, and thus the statistical significance of performance differences between CIS algorithms cannot be assessed. We propose the total error rate (TER), a novel performance measure for segmenting all cells in the supervised evaluation. The TER statistically aggregates all misclassification error rates (MER) by taking cell sizes as weights. The MERs are for segmenting each single cell in the population. The TER is fully supported by the pairwise comparisons of MERs using 106 manually segmented ground-truth cells with different sizes and seven CIS algorithms taken from ImageJ. Further, the SE and 95% confidence interval (CI) of TER are computed based on the SE of MER that is calculated using the bootstrap method. An algorithm for computing the correlation coefficient of TERs between two CIS algorithms is also provided. Hence, the 95% CI error bars can be used to classify CIS algorithms. The SEs of TERs and their correlation coefficient can be employed to conduct the hypothesis testing, while the CIs overlap, to determine the statistical significance of the performance differences between CIS algorithms. A novel measure TER of CIS is proposed. The TER's SEs and correlation coefficient are computed. Thereafter, CIS algorithms can be evaluated and compared statistically by conducting the significance testing.

Quantitative fluorescence imaging of protein diffusion and interaction in living cells.

PubMed

Capoulade, Jérémie; Wachsmuth, Malte; Hufnagel, Lars; Knop, Michael

2011-08-07

Diffusion processes and local dynamic equilibria inside cells lead to nonuniform spatial distributions of molecules, which are essential for processes such as nuclear organization and signaling in cell division, differentiation and migration. To understand these mechanisms, spatially resolved quantitative measurements of protein abundance, mobilities and interactions are needed, but current methods have limited capabilities to study dynamic parameters. Here we describe a microscope based on light-sheet illumination that allows massively parallel fluorescence correlation spectroscopy (FCS) measurements and use it to visualize the diffusion and interactions of proteins in mammalian cells and in isolated fly tissue. Imaging the mobility of heterochromatin protein HP1α (ref. 4) in cell nuclei we could provide high-resolution diffusion maps that reveal euchromatin areas with heterochromatin-like HP1α-chromatin interactions. We expect that FCS imaging will become a useful method for the precise characterization of cellular reaction-diffusion processes.

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

PubMed

Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

2017-06-28

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

Image analysis driven single-cell analytics for systems microbiology.

PubMed

Balomenos, Athanasios D; Tsakanikas, Panagiotis; Aspridou, Zafiro; Tampakaki, Anastasia P; Koutsoumanis, Konstantinos P; Manolakos, Elias S

2017-04-04

Time-lapse microscopy is an essential tool for capturing and correlating bacterial morphology and gene expression dynamics at single-cell resolution. However state-of-the-art computational methods are limited in terms of the complexity of cell movies that they can analyze and lack of automation. The proposed Bacterial image analysis driven Single Cell Analytics (BaSCA) computational pipeline addresses these limitations thus enabling high throughput systems microbiology. BaSCA can segment and track multiple bacterial colonies and single-cells, as they grow and divide over time (cell segmentation and lineage tree construction) to give rise to dense communities with thousands of interacting cells in the field of view. It combines advanced image processing and machine learning methods to deliver very accurate bacterial cell segmentation and tracking (F-measure over 95%) even when processing images of imperfect quality with several overcrowded colonies in the field of view. In addition, BaSCA extracts on the fly a plethora of single-cell properties, which get organized into a database summarizing the analysis of the cell movie. We present alternative ways to analyze and visually explore the spatiotemporal evolution of single-cell properties in order to understand trends and epigenetic effects across cell generations. The robustness of BaSCA is demonstrated across different imaging modalities and microscopy types. BaSCA can be used to analyze accurately and efficiently cell movies both at a high resolution (single-cell level) and at a large scale (communities with many dense colonies) as needed to shed light on e.g. how bacterial community effects and epigenetic information transfer play a role on important phenomena for human health, such as biofilm formation, persisters' emergence etc. Moreover, it enables studying the role of single-cell stochasticity without losing sight of community effects that may drive it.

Benzofurazan Sulfides for Thiol Imaging and Quantification in Live Cells through Fluorescence Microscopy

PubMed Central

Li, Yinghong; Yang, Yang; Guan, Xiangming

2012-01-01

Thiol groups play a significant role in various cellular functions. Cellular thiol concentrations can be affected by various physiological or pathological factors. A fluorescence imaging agent that can effectively and specifically image thiols in live cells through fluorescence microscopy is desirable for live cell thiol monitoring. Benzofurazan sulfides 1a–e were synthesized and found to be thiol specific fluorogenic agents except 1d. They are not fluorescent but form strong fluorescent thiol adducts after reacting with thiols through a sulfide-thiol exchange reaction. On the other hand, they exhibit no reaction with other biologically relevant nucleophilic functional groups such as -NH2, -OH, or -COOH revealing the specificity for the detection of thiols. Sulfide 1a was selected to confirm its ability to image cellular thiols through fluorescence microscopy. The compound was demonstrated to effectively image and quantify thiol changes in live cells through fluorescence microscopy using 430 nm and 520 nm as the excitation and emission wavelengths respectively. The quantification results of total thiol in live cells obtained from fluorescence microscopy were validated by an HPLC/UV total thiol assay method. The reagents and method will be of a great value to thiol redox-related research. PMID:22794193

Fluorine-19 MRI Contrast Agents for Cell Tracking and Lung Imaging

PubMed Central

Fox, Matthew S.; Gaudet, Jeffrey M.; Foster, Paula J.

2015-01-01

Fluorine-19 (19F)-based contrast agents for magnetic resonance imaging stand to revolutionize imaging-based research and clinical trials in several fields of medical intervention. First, their use in characterizing in vivo cell behavior may help bring cellular therapy closer to clinical acceptance. Second, their use in lung imaging provides novel noninvasive interrogation of the ventilated airspaces without the need for complicated, hard-to-distribute hardware. This article reviews the current state of 19F-based cell tracking and lung imaging using magnetic resonance imaging and describes the link between the methods across these fields and how they may mutually benefit from solutions to mutual problems encountered when imaging 19F-containing compounds, as well as hardware and software advancements. PMID:27042089

In vivo imaging of neural activity

PubMed Central

Yang, Weijian; Yuste, Rafael

2017-01-01

Since the introduction of calcium imaging to monitor neuronal activity with single-cell resolution, optical imaging methods have revolutionized neuroscience by enabling systematic recordings of neuronal circuits in living animals. The plethora of methods for functional neural imaging can be daunting to the nonexpert to navigate. Here we review advanced microscopy techniques for in vivo functional imaging and offer guidelines for which technologies are best suited for particular applications. PMID:28362436

Automatic neuron segmentation and neural network analysis method for phase contrast microscopy images.

PubMed

Pang, Jincheng; Özkucur, Nurdan; Ren, Michael; Kaplan, David L; Levin, Michael; Miller, Eric L

2015-11-01

Phase Contrast Microscopy (PCM) is an important tool for the long term study of living cells. Unlike fluorescence methods which suffer from photobleaching of fluorophore or dye molecules, PCM image contrast is generated by the natural variations in optical index of refraction. Unfortunately, the same physical principles which allow for these studies give rise to complex artifacts in the raw PCM imagery. Of particular interest in this paper are neuron images where these image imperfections manifest in very different ways for the two structures of specific interest: cell bodies (somas) and dendrites. To address these challenges, we introduce a novel parametric image model using the level set framework and an associated variational approach which simultaneously restores and segments this class of images. Using this technique as the basis for an automated image analysis pipeline, results for both the synthetic and real images validate and demonstrate the advantages of our approach.

SU-E-I-39: Molecular Image Guided Cancer Stem Cells Therapy

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

Abdollahi, H

Purpose: Cancer stem cells resistance to radiation is a problematic issue that has caused a big fail in cancer treatment. Methods: As a primary work, molecular imaging can indicate the main mechanisms of radiation resistance of cancer stem cells. By developing and commissioning new probes and nanomolecules and biomarkers, radiation scientist will able to identify the essential pathways of radiation resistance of cancer stem cells. As the second solution, molecular imaging is a best way to find biological target volume and delineate cancer stem cell tissues. In the other hand, by molecular imaging techniques one can image the treatment responsemore » in tumor and also in normal tissue. In this issue, the response of cancer stem cells to radiation during therapy course can be imaged, also the main mechanisms of radiation resistance and finding the best radiation modifiers (sensitizers) can be achieved by molecular imaging modalities. In adaptive radiotherapy the molecular imaging plays a vital role to have higher tumor control probability by delivering high radiation doses to cancer stem cells in any time of treatment. The outcome of a feasible treatment is dependent to high cancer stem cells response to radiation and removing all of which, so a good imaging modality can show this issue and preventing of tumor recurrence and metastasis. Results: Our results are dependent to use of molecular imaging as a new modality in the clinic. We propose molecular imaging as a new radiobiological technique to solve radiation therapy problems due to cancer stem cells. Conclusion: Molecular imaging guided cancer stem cell diagnosis and therapy is a new approach in the field of cancer treatment. This new radiobiological imaging technique should be developed in all clinics as a feasible tool that is more biological than physical imaging.« less

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

Fertig, Fabian, E-mail: fabian.fertig@ise.fraunhofer.de; Greulich, Johannes; Rein, Stefan

Spatially resolved determination of solar cell parameters is beneficial for loss analysis and optimization of conversion efficiency. One key parameter that has been challenging to access by an imaging technique on solar cell level is short-circuit current density. This work discusses the robustness of a recently suggested approach to determine short-circuit current density spatially resolved based on a series of lock-in thermography images and options for a simplified image acquisition procedure. For an accurate result, one or two emissivity-corrected illuminated lock-in thermography images and one dark lock-in thermography image have to be recorded. The dark lock-in thermography image can bemore » omitted if local shunts are negligible. Furthermore, it is shown that omitting the correction of lock-in thermography images for local emissivity variations only leads to minor distortions for standard silicon solar cells. Hence, adequate acquisition of one image only is sufficient to generate a meaningful map of short-circuit current density. Beyond that, this work illustrates the underlying physics of the recently proposed method and demonstrates its robustness concerning varying excitation conditions and locally increased series resistance. Experimentally gained short-circuit current density images are validated for monochromatic illumination in comparison to the reference method of light-beam induced current.« less

Vibrational spectroscopy for imaging single microbial cells in complex biological samples

DOE PAGES

Harrison, Jesse P.; Berry, David

2017-04-13

Here, vibrational spectroscopy is increasingly used for the rapid and non-destructive imaging of environmental and medical samples. Both Raman and Fourier-transform infrared (FT-IR) imaging have been applied to obtain detailed information on the chemical composition of biological materials, ranging from single microbial cells to tissues. Due to its compatibility with methods such as stable isotope labeling for the monitoring of cellular activities, vibrational spectroscopy also holds considerable power as a tool in microbial ecology. Chemical imaging of undisturbed biological systems (such as live cells in their native habitats) presents unique challenges due to the physical and chemical complexity of themore » samples, potential for spectral interference, and frequent need for real-time measurements. This Mini Review provides a critical synthesis of recent applications of Raman and FT-IR spectroscopy for characterizing complex biological samples, with a focus on developments in single-cell imaging. We also discuss how new spectroscopic methods could be used to overcome current limitations of singlecell analyses. Given the inherent complementarity of Raman and FT-IR spectroscopic methods, we discuss how combining these approaches could enable us to obtain new insights into biological activities either in situ or under conditions that simulate selected properties of the natural environment.« less

Vibrational spectroscopy for imaging single microbial cells in complex biological samples

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

Harrison, Jesse P.; Berry, David

Here, vibrational spectroscopy is increasingly used for the rapid and non-destructive imaging of environmental and medical samples. Both Raman and Fourier-transform infrared (FT-IR) imaging have been applied to obtain detailed information on the chemical composition of biological materials, ranging from single microbial cells to tissues. Due to its compatibility with methods such as stable isotope labeling for the monitoring of cellular activities, vibrational spectroscopy also holds considerable power as a tool in microbial ecology. Chemical imaging of undisturbed biological systems (such as live cells in their native habitats) presents unique challenges due to the physical and chemical complexity of themore » samples, potential for spectral interference, and frequent need for real-time measurements. This Mini Review provides a critical synthesis of recent applications of Raman and FT-IR spectroscopy for characterizing complex biological samples, with a focus on developments in single-cell imaging. We also discuss how new spectroscopic methods could be used to overcome current limitations of singlecell analyses. Given the inherent complementarity of Raman and FT-IR spectroscopic methods, we discuss how combining these approaches could enable us to obtain new insights into biological activities either in situ or under conditions that simulate selected properties of the natural environment.« less

Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software.

PubMed

Moon, Andres; Smith, Geoffrey H; Kong, Jun; Rogers, Thomas E; Ellis, Carla L; Farris, Alton B Brad

2018-02-01

Renal allograft rejection diagnosis depends on assessment of parameters such as interstitial inflammation; however, studies have shown interobserver variability regarding interstitial inflammation assessment. Since automated image analysis quantitation can be reproducible, we devised customized analysis methods for CD3+ T-cell staining density as a measure of rejection severity and compared them with established commercial methods along with visual assessment. Renal biopsy CD3 immunohistochemistry slides (n = 45), including renal allografts with various degrees of acute cellular rejection (ACR) were scanned for whole slide images (WSIs). Inflammation was quantitated in the WSIs using pathologist visual assessment, commercial algorithms (Aperio nuclear algorithm for CD3+ cells/mm 2 and Aperio positive pixel count algorithm), and customized open source algorithms developed in ImageJ with thresholding/positive pixel counting (custom CD3+%) and identification of pixels fulfilling "maxima" criteria for CD3 expression (custom CD3+ cells/mm 2 ). Based on visual inspections of "markup" images, CD3 quantitation algorithms produced adequate accuracy. Additionally, CD3 quantitation algorithms correlated between each other and also with visual assessment in a statistically significant manner (r = 0.44 to 0.94, p = 0.003 to < 0.0001). Methods for assessing inflammation suggested a progression through the tubulointerstitial ACR grades, with statistically different results in borderline versus other ACR types, in all but the custom methods. Assessment of CD3-stained slides using various open source image analysis algorithms presents salient correlations with established methods of CD3 quantitation. These analysis techniques are promising and highly customizable, providing a form of on-slide "flow cytometry" that can facilitate additional diagnostic accuracy in tissue-based assessments.

Blue intensity matters for cell cycle profiling in fluorescence DAPI-stained images.

PubMed

Ferro, Anabela; Mestre, Tânia; Carneiro, Patrícia; Sahumbaiev, Ivan; Seruca, Raquel; Sanches, João M

2017-05-01

In the past decades, there has been an amazing progress in the understanding of the molecular mechanisms of the cell cycle. This has been possible largely due to a better conceptualization of the cycle itself, but also as a consequence of technological advances. Herein, we propose a new fluorescence image-based framework targeted at the identification and segmentation of stained nuclei with the purpose to determine DNA content in distinct cell cycle stages. The method is based on discriminative features, such as total intensity and area, retrieved from in situ stained nuclei by fluorescence microscopy, allowing the determination of the cell cycle phase of both single and sub-population of cells. The analysis framework was built on a modified k-means clustering strategy and refined with a Gaussian mixture model classifier, which enabled the definition of highly accurate classification clusters corresponding to G1, S and G2 phases. Using the information retrieved from area and fluorescence total intensity, the modified k-means (k=3) cluster imaging framework classified 64.7% of the imaged nuclei, as being at G1 phase, 12.0% at G2 phase and 23.2% at S phase. Performance of the imaging framework was ascertained with normal murine mammary gland cells constitutively expressing the Fucci2 technology, exhibiting an overall sensitivity of 94.0%. Further, the results indicate that the imaging framework has a robust capacity to both identify a given DAPI-stained nucleus to its correct cell cycle phase, as well as to determine, with very high probability, true negatives. Importantly, this novel imaging approach is a non-disruptive method that allows an integrative and simultaneous quantitative analysis of molecular and morphological parameters, thus awarding the possibility of cell cycle profiling in cytological and histological samples.

Neuronal Tracing with Magnetic Labels: NMR Imaging Methods, Preliminary Results, and New Optimized Coils.

NASA Astrophysics Data System (ADS)

Ghosh, Pratik

1992-01-01

The investigations focussed on in vivo NMR imaging studies of magnetic particles with and within neural cells. NMR imaging methods, both Fourier transform and projection reconstruction, were implemented and new protocols were developed to perform "Neuronal Tracing with Magnetic Labels" on small animal brains. Having performed the preliminary experiments with neuronal tracing, new optimized coils and experimental set-up were devised. A novel gradient coil technology along with new rf-coils were implemented, and optimized for future use with small animals in them. A new magnetic labelling procedure was developed that allowed labelling of billions of cells with ultra -small magnetite particles in a short time. The relationships among the viability of such cells, the amount of label and the contrast in the images were studied as quantitatively as possible. Intracerebral grafting of magnetite labelled fetal rat brain cells made it possible for the first time to attempt monitoring in vivo the survival, differentiation, and possible migration of both host and grafted cells in the host rat brain. This constituted the early steps toward future experiments that may lead to the monitoring of human brain grafts of fetal brain cells. Preliminary experiments with direct injection of horse radish peroxidase-conjugated magnetite particles into neurons, followed by NMR imaging, revealed a possible non-invasive alternative, allowing serial study of the dynamic transport pattern of tracers in single living animals. New gradient coils were built by using parallel solid-conductor ribbon cables that could be wrapped easily and quickly. Rapid rise times provided by these coils allowed implementation of fast imaging methods. Optimized rf-coil circuit development made it possible to understand better the sample-coil properties and the associated trade -offs in cases of small but conducting samples.

Peering into Cells One Molecule at a Time: Single-molecule and plasmon-enhanced fluorescence super-resolution imaging

NASA Astrophysics Data System (ADS)

Biteen, Julie

2013-03-01

Single-molecule fluorescence brings the resolution of optical microscopy down to the nanometer scale, allowing us to unlock the mysteries of how biomolecules work together to achieve the complexity that is a cell. This high-resolution, non-destructive method for examining subcellular events has opened up an exciting new frontier: the study of macromolecular localization and dynamics in living cells. We have developed methods for single-molecule investigations of live bacterial cells, and have used these techniques to investigate thee important prokaryotic systems: membrane-bound transcription activation in Vibrio cholerae, carbohydrate catabolism in Bacteroides thetaiotaomicron, and DNA mismatch repair in Bacillus subtilis. Each system presents unique challenges, and we will discuss the important methods developed for each system. Furthermore, we use the plasmon modes of bio-compatible metal nanoparticles to enhance the emissivity of single-molecule fluorophores. The resolution of single-molecule imaging in cells is generally limited to 20-40 nm, far worse than the 1.5-nm localization accuracies which have been attained in vitro. We use plasmonics to improve the brightness and stability of single-molecule probes, and in particular fluorescent proteins, which are widely used for bio-imaging. We find that gold-coupled fluorophores demonstrate brighter, longer-lived emission, yielding an overall enhancement in total photons detected. Ultimately, this results in increased localization accuracy for single-molecule imaging. Furthermore, since fluorescence intensity is proportional to local electromagnetic field intensity, these changes in decay intensity and rate serve as a nm-scale read-out of the field intensity. Our work indicates that plasmonic substrates are uniquely advantageous for super-resolution imaging, and that plasmon-enhanced imaging is a promising technique for improving live cell single-molecule microscopy.

Near-infrared emitting fluorescent nanocrystals-labeled natural killer cells as a platform technology for the optical imaging of immunotherapeutic cells-based cancer therapy

NASA Astrophysics Data System (ADS)

Taik Lim, Yong; Cho, Mi Young; Noh, Young-Woock; Chung, Jin Woong; Chung, Bong Hyun

2009-11-01

This study describes the development of near-infrared optical imaging technology for the monitoring of immunotherapeutic cell-based cancer therapy using natural killer (NK) cells labeled with fluorescent nanocrystals. Although NK cell-based immunotherapeutic strategies have drawn interest as potent preclinical or clinical methods of cancer therapy, there are few reports documenting the molecular imaging of NK cell-based cancer therapy, primarily due to the difficulty of labeling of NK cells with imaging probes. Human natural killer cells (NK92MI) were labeled with anti-human CD56 antibody-coated quantum dots (QD705) for fluorescence imaging. FACS analysis showed that the NK92MI cells labeled with anti-human CD56 antibody-coated QD705 have no effect on the cell viability. The effect of anti-human CD56 antibody-coated QD705 labeling on the NK92MI cell function was investigated by measuring interferon gamma (IFN- γ) production and cytolytic activity. Finally, the NK92MI cells labeled with anti-human CD56 antibody-coated QD705 showed a therapeutic effect similar to that of unlabeled NK92MI cells. Images of intratumorally injected NK92MI cells labeled with anti-human CD56 antibody-coated could be acquired using near-infrared optical imaging both in vivo and in vitro. This result demonstrates that the immunotherapeutic cells labeled with fluorescent nanocrystals can be a versatile platform for the effective tracking of injected therapeutic cells using optical imaging technology, which is very important in cell-based cancer therapies.

Video-rate confocal microscopy for single-molecule imaging in live cells and superresolution fluorescence imaging.

PubMed

Lee, Jinwoo; Miyanaga, Yukihiro; Ueda, Masahiro; Hohng, Sungchul

2012-10-17

There is no confocal microscope optimized for single-molecule imaging in live cells and superresolution fluorescence imaging. By combining the swiftness of the line-scanning method and the high sensitivity of wide-field detection, we have developed a, to our knowledge, novel confocal fluorescence microscope with a good optical-sectioning capability (1.0 μm), fast frame rates (<33 fps), and superior fluorescence detection efficiency. Full compatibility of the microscope with conventional cell-imaging techniques allowed us to do single-molecule imaging with a great ease at arbitrary depths of live cells. With the new microscope, we monitored diffusion motion of fluorescently labeled cAMP receptors of Dictyostelium discoideum at both the basal and apical surfaces and obtained superresolution fluorescence images of microtubules of COS-7 cells at depths in the range 0-85 μm from the surface of a coverglass. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Imaging immune response of skin mast cells in vivo with two-photon microscopy

NASA Astrophysics Data System (ADS)

Li, Chunqiang; Pastila, Riikka K.; Lin, Charles P.

2012-02-01

Intravital multiphoton microscopy has provided insightful information of the dynamic process of immune cells in vivo. However, the use of exogenous labeling agents limits its applications. There is no method to perform functional imaging of mast cells, a population of innate tissue-resident immune cells. Mast cells are widely recognized as the effector cells in allergy. Recently their roles as immunoregulatory cells in certain innate and adaptive immune responses are being actively investigated. Here we report in vivo mouse skin mast cells imaging with two-photon microscopy using endogenous tryptophan as the fluorophore. We studied the following processes. 1) Mast cells degranulation, the first step in the mast cell activation process in which the granules are released into peripheral tissue to trigger downstream reactions. 2) Mast cell reconstitution, a procedure commonly used to study mast cells functioning by comparing the data from wild type mice, mast cell-deficient mice, and mast-cell deficient mice reconstituted with bone marrow-derived mast cells (BMMCs). Imaging the BMMCs engraftment in tissue reveals the mast cells development and the efficiency of BMMCs reconstitution. We observed the reconstitution process for 6 weeks in the ear skin of mast cell-deficient Kit wsh/ w-sh mice by two-photon imaging. Our finding is the first instance of imaging mast cells in vivo with endogenous contrast.

Shape tunable plasmonic nanoparticles

DOEpatents

El-Sayed, Mostafa A.; El-Sayed, Ivan Homer

2017-03-07

Noble metal nanoparticles and methods of their use are provided. Certain aspects provided solid noble metal nanoparticles tuned to the near infrared. The disclosed nanoparticles can be used in molecular imaging, diagnosis, and treatment. Methods for imaging cells are also provided.

A novel method to visually determine the intracellular pH of xenografted tumor in vivo by utilizing fluorescent protein as an indicator.

PubMed

Tanaka, Shotaro; Harada, Hiroshi; Hiraoka, Masahiro

2015-09-04

The alkalization of intracellular pH (pHin) advances together with enhancement of aerobic glycolysis within tumor cells (the Warburg effect), and that is responsible for the progression of tumor malignancy together with hypoxia and angiogenesis. But how they correlate each other during tumor growth is poorly understood, partly due to the lack of suitable imaging methods. In present study, we propose a novel method to visually determine the pHin of tumor xenograft model from fluorescent image ratios. We utilized tandemly-linked two fluorescent proteins as a pH indicator; yellow fluorescent protein (YFP, pH sensitive) as an indicator, and red fluorescent protein (RFP, pH insensitive) as a reference. This method can eliminate the influence of optical factors from tissue as well as of the diverse expression level of pH indicator in the grafted cells. In addition, that can be operated by filter-based fluorescent imagers that are generally used in small animal study. The efficacy of the pH indicator, RFP-YFP, was confirmed by studies using recombinant protein in vitro and HeLa cells expressing RFP-YFP in vivo. Furthermore, we prepared nude mice subcutaneously xenografted HeLa cells expressing RFP-YFP cells as tumor model. The image ratios (YFP/RFP) of the tumor at the day 5 after surgery clearly showed the heterogeneous distribution of diverse pHin cells in the tumor tissue. Concomitantly acquired angiography using near-infrared fluorescence (680 nm for emission) also indicated that the relative alkaline pHin cells located in the region far from tumor vessels in which tumor aerobic glycolysis would be facilitated by progression of hypoxia and nutrient starvation. Applying the present method for a multi-wavelength imaging concerning pO2 and/or nutrient starvation states in addition to pHin and angiogenesis would provide valuable information about complicated alteration of tumoral cell states during tumorigenesis. Copyright © 2015 Elsevier Inc. All rights reserved.

Scanning electron microscopy of bone.

PubMed

Boyde, Alan

2012-01-01

This chapter described methods for Scanning Electron Microscopical imaging of bone and bone cells. Backscattered electron (BSE) imaging is by far the most useful in the bone field, followed by secondary electrons (SE) and the energy dispersive X-ray (EDX) analytical modes. This chapter considers preparing and imaging samples of unembedded bone having 3D detail in a 3D surface, topography-free, polished or micromilled, resin-embedded block surfaces, and resin casts of space in bone matrix. The chapter considers methods for fixation, drying, looking at undersides of bone cells, and coating. Maceration with alkaline bacterial pronase, hypochlorite, hydrogen peroxide, and sodium or potassium hydroxide to remove cells and unmineralised matrix is described in detail. Attention is given especially to methods for 3D BSE SEM imaging of bone samples and recommendations for the types of resin embedding of bone for BSE imaging are given. Correlated confocal and SEM imaging of PMMA-embedded bone requires the use of glycerol to coverslip. Cathodoluminescence (CL) mode SEM imaging is an alternative for visualising fluorescent mineralising front labels such as calcein and tetracyclines. Making spatial casts from PMMA or other resin embedded samples is an important use of this material. Correlation with other imaging means, including microradiography and microtomography is important. Shipping wet bone samples between labs is best done in glycerol. Environmental SEM (ESEM, controlled vacuum mode) is valuable in eliminating -"charging" problems which are common with complex, cancellous bone samples.

Development of image analysis software for quantification of viable cells in microchips.

PubMed

Georg, Maximilian; Fernández-Cabada, Tamara; Bourguignon, Natalia; Karp, Paola; Peñaherrera, Ana B; Helguera, Gustavo; Lerner, Betiana; Pérez, Maximiliano S; Mertelsmann, Roland

2018-01-01

Over the past few years, image analysis has emerged as a powerful tool for analyzing various cell biology parameters in an unprecedented and highly specific manner. The amount of data that is generated requires automated methods for the processing and analysis of all the resulting information. The software available so far are suitable for the processing of fluorescence and phase contrast images, but often do not provide good results from transmission light microscopy images, due to the intrinsic variation of the acquisition of images technique itself (adjustment of brightness / contrast, for instance) and the variability between image acquisition introduced by operators / equipment. In this contribution, it has been presented an image processing software, Python based image analysis for cell growth (PIACG), that is able to calculate the total area of the well occupied by cells with fusiform and rounded morphology in response to different concentrations of fetal bovine serum in microfluidic chips, from microscopy images in transmission light, in a highly efficient way.

Simple and fast spectral domain algorithm for quantitative phase imaging of living cells with digital holographic microscopy

NASA Astrophysics Data System (ADS)

Min, Junwei; Yao, Baoli; Ketelhut, Steffi; Kemper, Björn

2017-02-01

The modular combination of optical microscopes with digital holographic microscopy (DHM) has been proven to be a powerful tool for quantitative live cell imaging. The introduction of condenser and different microscope objectives (MO) simplifies the usage of the technique and makes it easier to measure different kinds of specimens with different magnifications. However, the high flexibility of illumination and imaging also causes variable phase aberrations that need to be eliminated for high resolution quantitative phase imaging. The existent phase aberrations compensation methods either require add additional elements into the reference arm or need specimen free reference areas or separate reference holograms to build up suitable digital phase masks. These inherent requirements make them unpractical for usage with highly variable illumination and imaging systems and prevent on-line monitoring of living cells. In this paper, we present a simple numerical method for phase aberration compensation based on the analysis of holograms in spatial frequency domain with capabilities for on-line quantitative phase imaging. From a single shot off-axis hologram, the whole phase aberration can be eliminated automatically without numerical fitting or pre-knowledge of the setup. The capabilities and robustness for quantitative phase imaging of living cancer cells are demonstrated.

HPASubC: A suite of tools for user subclassification of human protein atlas tissue images

PubMed Central

Cornish, Toby C.; Chakravarti, Aravinda; Kapoor, Ashish; Halushka, Marc K.

2015-01-01

Background: The human protein atlas (HPA) is a powerful proteomic tool for visualizing the distribution of protein expression across most human tissues and many common malignancies. The HPA includes immunohistochemically-stained images from tissue microarrays (TMAs) that cover 48 tissue types and 20 common malignancies. The TMA data are used to provide expression information at the tissue, cellular, and occasionally, subcellular level. The HPA also provides subcellular data from confocal immunofluorescence data on three cell lines. Despite the availability of localization data, many unique patterns of cellular and subcellular expression are not documented. Materials and Methods: To get at this more granular data, we have developed a suite of Python scripts, HPASubC, to aid in subcellular, and cell-type specific classification of HPA images. This method allows the user to download and optimize specific HPA TMA images for review. Then, using a playstation-style video game controller, a trained observer can rapidly step through 10's of 1000's of images to identify patterns of interest. Results: We have successfully used this method to identify 703 endothelial cell (EC) and/or smooth muscle cell (SMCs) specific proteins discovered within 49,200 heart TMA images. This list will assist us in subdividing cardiac gene or protein array data into expression by one of the predominant cell types of the myocardium: Myocytes, SMCs or ECs. Conclusions: The opportunity to further characterize unique staining patterns across a range of human tissues and malignancies will accelerate our understanding of disease processes and point to novel markers for tissue evaluation in surgical pathology. PMID:26167380

Building and degradation of secondary cell walls: are there common patterns of lamellar assembly of cellulose microfibrils and cell wall delamination?

PubMed

De Micco, Veronica; Ruel, Katia; Joseleau, Jean-Paul; Aronne, Giovanna

2010-08-01

During cell wall formation and degradation, it is possible to detect cellulose microfibrils assembled into thicker and thinner lamellar structures, respectively, following inverse parallel patterns. The aim of this study was to analyse such patterns of microfibril aggregation and cell wall delamination. The thickness of microfibrils and lamellae was measured on digital images of both growing and degrading cell walls viewed by means of transmission electron microscopy. To objectively detect, measure and classify microfibrils and lamellae into thickness classes, a method based on the application of computerized image analysis combined with graphical and statistical methods was developed. The method allowed common classes of microfibrils and lamellae in cell walls to be identified from different origins. During both the formation and degradation of cell walls, a preferential formation of structures with specific thickness was evidenced. The results obtained with the developed method allowed objective analysis of patterns of microfibril aggregation and evidenced a trend of doubling/halving lamellar structures, during cell wall formation/degradation in materials from different origin and which have undergone different treatments.

Single-Molecule and Superresolution Imaging in Live Bacteria Cells

PubMed Central

Biteen, Julie S.; Moerner, W.E.

2010-01-01

Single-molecule imaging enables biophysical measurements devoid of ensemble averaging, gives enhanced spatial resolution beyond the diffraction limit, and permits superresolution reconstructions. Here, single-molecule and superresolution imaging are applied to the study of proteins in live Caulobacter crescentus cells to illustrate the power of these methods in bacterial imaging. Based on these techniques, the diffusion coefficient and dynamics of the histidine protein kinase PleC, the localization behavior of the polar protein PopZ, and the treadmilling behavior and protein superstructure of the structural protein MreB are investigated with sub-40-nm spatial resolution, all in live cells. PMID:20300204

Biomarker-specific conjugated nanopolyplexes for the active coloring of stem-like cancer cells

NASA Astrophysics Data System (ADS)

Hong, Yoochan; Lee, Eugene; Choi, Jihye; Haam, Seungjoo; Suh, Jin-Suck; Yang, Jaemoon

2016-06-01

Stem-like cancer cells possess intrinsic features and their CD44 regulate redox balance in cancer cells to survive under stress conditions. Thus, we have fabricated biomarker-specific conjugated polyplexes using CD44-targetable hyaluronic acid and redox-sensible polyaniline based on a nanoemulsion method. For the most sensitive recognition of the cellular redox at a single nanoparticle scale, a nano-scattering spectrum imaging analyzer system was introduced. The conjugated polyplexes showed a specific targeting ability toward CD44-expressing cancer cells as well as a dramatic change in its color, which depended on the redox potential in the light-scattered images. Therefore, these polyaniline-based conjugated polyplexes as well as analytical processes that include light-scattering imaging and measurements of scattering spectra, clearly establish a systematic method for the detection and monitoring of cancer microenvironments.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

PubMed Central

Xing, Fuyong; Yang, Lin

2016-01-01

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to inter-observer variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literatures. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast (DIC), fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation. PMID:26742143

Label-free imaging of trabecular meshwork cells using Coherent Anti-Stokes Raman Scattering (CARS) microscopy

PubMed Central

Lei, Tim C.; Ammar, David A.; Masihzadeh, Omid; Gibson, Emily A.

2011-01-01

Purpose To image the human trabecular meshwork (TM) using a non-invasive, non-destructive technique without the application of exogenous label. Methods Flat-mounted TM samples from a human cadaver eye were imaged using two nonlinear optical techniques: coherent anti-Stokes Raman scattering (CARS) and two-photon autofluorescence (TPAF). In TPAF, two optical photons are simultaneously absorbed and excite molecules in the sample that then emit a higher energy photon. The signal is predominately from collagen and elastin. The CARS technique uses two laser frequencies to specifically excite carbon-hydrogen bonds, allowing the visualization of lipid-rich cell membranes. Multiple images were taken along an axis perpendicular to the surface of the TM for subsequent analysis. Results Analysis of multiple TPAF images of the TM reveals the characteristic overlapping bundles of collagen of various sizes. Simultaneous CARS imaging revealed elliptical structures of ~7×10 µm in diameter populating the meshwork which were consistent with TM cells. Irregularly shaped objects of ~4 µm diameter appeared in both the TPAF and CARS channels, and are consistent with melanin granules. Conclusions CARS techniques were successful in imaging live TM cells in freshly isolated human TM samples. Similar images have been obtained with standard histological techniques, however the method described here has the advantage of being performed on unprocessed, unfixed tissue free from the potential distortions of the fine tissue morphology that can occur due to infusion of fixatives and treatment with alcohols. CARS imaging of the TM represents a new avenue for exploring details of aqueous outflow and TM cell physiology. PMID:22025898

Non-invasive and non-destructive measurements of confluence in cultured adherent cell lines.

PubMed

Busschots, Steven; O'Toole, Sharon; O'Leary, John J; Stordal, Britta

2015-01-01

Many protocols used for measuring the growth of adherent monolayer cells in vitro are invasive, destructive and do not allow for the continued, undisturbed growth of cells within flasks. Protocols often use indirect methods for measuring proliferation. Microscopy techniques can analyse cell proliferation in a non-invasive or non-destructive manner but often use expensive equipment and software algorithms. In this method images of cells within flasks are captured by photographing under a standard inverted phase contract light microscope using a digital camera with a camera lens adaptor. Images are analysed for confluence using ImageJ freeware resulting in a measure of confluence known as an Area Fraction (AF) output. An example of the AF method in use on OVCAR8 and UPN251 cell lines is included. •Measurements of confluence from growing adherent cell lines in cell culture flasks is obtained in a non-invasive, non-destructive, label-free manner.•The technique is quick, affordable and eliminates sample manipulation.•The technique provides an objective, consistent measure of when cells reach confluence and is highly correlated to manual counting with a haemocytometer. The average correlation co-efficient from a Spearman correlation (n = 3) was 0.99 ± 0.008 for OVCAR8 (p = 0.01) and 0.99 ± 0.01 for UPN251 (p = 0.01) cell lines.

A method for rapid quantitative assessment of biofilms with biomolecular staining and image analysis

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

Larimer, Curtis J.; Winder, Eric M.; Jeters, Robert T.

Here, the accumulation of bacteria in surface attached biofilms, or biofouling, can be detrimental to human health, dental hygiene, and many industrial processes. A critical need in identifying and preventing the deleterious effects of biofilms is the ability to observe and quantify their development. Analytical methods capable of assessing early stage fouling are cumbersome or lab-confined, subjective, and qualitative. Herein, a novel photographic method is described that uses biomolecular staining and image analysis to enhance contrast of early stage biofouling. A robust algorithm was developed to objectively and quantitatively measure surface accumulation of Pseudomonas putida from photographs and results weremore » compared to independent measurements of cell density. Results from image analysis quantified biofilm growth intensity accurately and with approximately the same precision of the more laborious cell counting method. This simple method for early stage biofilm detection enables quantifiable measurement of surface fouling and is flexible enough to be applied from the laboratory to the field. Broad spectrum staining highlights fouling biomass, photography quickly captures a large area of interest, and image analysis rapidly quantifies fouling in the image.« less

A method for rapid quantitative assessment of biofilms with biomolecular staining and image analysis

DOE PAGES

Larimer, Curtis J.; Winder, Eric M.; Jeters, Robert T.; ...

2015-12-07

Here, the accumulation of bacteria in surface attached biofilms, or biofouling, can be detrimental to human health, dental hygiene, and many industrial processes. A critical need in identifying and preventing the deleterious effects of biofilms is the ability to observe and quantify their development. Analytical methods capable of assessing early stage fouling are cumbersome or lab-confined, subjective, and qualitative. Herein, a novel photographic method is described that uses biomolecular staining and image analysis to enhance contrast of early stage biofouling. A robust algorithm was developed to objectively and quantitatively measure surface accumulation of Pseudomonas putida from photographs and results weremore » compared to independent measurements of cell density. Results from image analysis quantified biofilm growth intensity accurately and with approximately the same precision of the more laborious cell counting method. This simple method for early stage biofilm detection enables quantifiable measurement of surface fouling and is flexible enough to be applied from the laboratory to the field. Broad spectrum staining highlights fouling biomass, photography quickly captures a large area of interest, and image analysis rapidly quantifies fouling in the image.« less

Multimodality Molecular Imaging-Guided Tumor Border Delineation and Photothermal Therapy Analysis Based on Graphene Oxide-Conjugated Gold Nanoparticles Chelated with Gd.

PubMed

Ma, Xibo; Jin, Yushen; Wang, Yi; Zhang, Shuai; Peng, Dong; Yang, Xin; Wei, Shoushui; Chai, Wei; Li, Xuejun; Tian, Jie

2018-01-01

Tumor cell complete extinction is a crucial measure to evaluate antitumor efficacy. The difficulties in defining tumor margins and finding satellite metastases are the reason for tumor recurrence. A synergistic method based on multimodality molecular imaging needs to be developed so as to achieve the complete extinction of the tumor cells. In this study, graphene oxide conjugated with gold nanostars and chelated with Gd through 1,4,7,10-tetraazacyclododecane-N,N',N,N'-tetraacetic acid (DOTA) (GO-AuNS-DOTA-Gd) were prepared to target HCC-LM3-fLuc cells and used for therapy. For subcutaneous tumor, multimodality molecular imaging including photoacoustic imaging (PAI) and magnetic resonance imaging (MRI) and the related processing techniques were used to monitor the pharmacokinetics process of GO-AuNS-DOTA-Gd in order to determine the optimal time for treatment. For orthotopic tumor, MRI was used to delineate the tumor location and margin in vivo before treatment. Then handheld photoacoustic imaging system was used to determine the tumor location during the surgery and guided the photothermal therapy. The experiment result based on orthotopic tumor demonstrated that this synergistic method could effectively reduce tumor residual and satellite metastases by 85.71% compared with the routine photothermal method without handheld PAI guidance. These results indicate that this multimodality molecular imaging-guided photothermal therapy method is promising with a good prospect in clinical application.

Parallel excitation-emission multiplexed fluorescence lifetime confocal microscopy for live cell imaging.

PubMed

Zhao, Ming; Li, Yu; Peng, Leilei

2014-05-05

We present a novel excitation-emission multiplexed fluorescence lifetime microscopy (FLIM) method that surpasses current FLIM techniques in multiplexing capability. The method employs Fourier multiplexing to simultaneously acquire confocal fluorescence lifetime images of multiple excitation wavelength and emission color combinations at 44,000 pixels/sec. The system is built with low-cost CW laser sources and standard PMTs with versatile spectral configuration, which can be implemented as an add-on to commercial confocal microscopes. The Fourier lifetime confocal method allows fast multiplexed FLIM imaging, which makes it possible to monitor multiple biological processes in live cells. The low cost and compatibility with commercial systems could also make multiplexed FLIM more accessible to biological research community.

Automatic extraction of nuclei centroids of mouse embryonic cells from fluorescence microscopy images.

PubMed

Bashar, Md Khayrul; Komatsu, Koji; Fujimori, Toshihiko; Kobayashi, Tetsuya J

2012-01-01

Accurate identification of cell nuclei and their tracking using three dimensional (3D) microscopic images is a demanding task in many biological studies. Manual identification of nuclei centroids from images is an error-prone task, sometimes impossible to accomplish due to low contrast and the presence of noise. Nonetheless, only a few methods are available for 3D bioimaging applications, which sharply contrast with 2D analysis, where many methods already exist. In addition, most methods essentially adopt segmentation for which a reliable solution is still unknown, especially for 3D bio-images having juxtaposed cells. In this work, we propose a new method that can directly extract nuclei centroids from fluorescence microscopy images. This method involves three steps: (i) Pre-processing, (ii) Local enhancement, and (iii) Centroid extraction. The first step includes two variations: first variation (Variant-1) uses the whole 3D pre-processed image, whereas the second one (Variant-2) modifies the preprocessed image to the candidate regions or the candidate hybrid image for further processing. At the second step, a multiscale cube filtering is employed in order to locally enhance the pre-processed image. Centroid extraction in the third step consists of three stages. In Stage-1, we compute a local characteristic ratio at every voxel and extract local maxima regions as candidate centroids using a ratio threshold. Stage-2 processing removes spurious centroids from Stage-1 results by analyzing shapes of intensity profiles from the enhanced image. An iterative procedure based on the nearest neighborhood principle is then proposed to combine if there are fragmented nuclei. Both qualitative and quantitative analyses on a set of 100 images of 3D mouse embryo are performed. Investigations reveal a promising achievement of the technique presented in terms of average sensitivity and precision (i.e., 88.04% and 91.30% for Variant-1; 86.19% and 95.00% for Variant-2), when compared with an existing method (86.06% and 90.11%), originally developed for analyzing C. elegans images.

Cell Migration in Tissues: Explant Culture and Live Imaging.

PubMed

Staneva, Ralitza; Barbazan, Jorge; Simon, Anthony; Vignjevic, Danijela Matic; Krndija, Denis

2018-01-01

Cell migration is a process that ensures correct cell localization and function in development and homeostasis. In disease such as cancer, cells acquire an upregulated migratory capacity that leads to their dissemination throughout the body. Live imaging of cell migration allows for better understanding of cell behaviors in development, adult tissue homeostasis and disease. We have optimized live imaging procedures to track cell migration in adult murine tissue explants derived from: (1) healthy gut; (2) primary intestinal carcinoma; and (3) the liver, a common metastatic site. To track epithelial cell migration in the gut, we generated an inducible fluorescent reporter mouse, enabling us to visualize and track individual cells in unperturbed gut epithelium. To image intratumoral cancer cells, we use a spontaneous intestinal cancer model based on the activation of Notch1 and deletion of p53 in the mouse intestinal epithelium, which gives rise to aggressive carcinoma. Interaction of cancer cells with a metastatic niche, the mouse liver, is addressed using a liver colonization model. In summary, we describe a method for long-term 3D imaging of tissue explants by two-photon excitation microscopy. Explant culturing and imaging can help understand dynamic behavior of cells in homeostasis and disease, and would be applicable to various tissues.

A method for prolonged imaging of motile lymphocytes.

PubMed

Day, Daniel; Pham, Kim; Ludford-Menting, Mandy J; Oliaro, Jane; Izon, David; Russell, Sarah M; Gu, Min

2009-02-01

With new imaging technologies and fluorescent probes, live imaging of cells in vitro has revolutionized many aspects of cell biology. A key goal now is to develop systems to optimize in vitro imaging, which do not compromise the physiological relevance of the study. We have developed a methodology that contains non-adherent cells within the field of view. 'Cell paddocks' are created by generating an array of microgrids using polydimethylsiloxane. Each microgrid is up to 250 x 250 microm(2) with a height of 60 microm. Overlayed cells settle into the grids and the walls restrict their lateral movement, but a contiguous supply of medium between neighboring microgrids facilitates the exchange of cytokines and growth factors. This allows culture over at least 6 days with no impact upon viability and proliferation. Adaptations of the microgrids have enabled imaging and tracking of lymphocyte division through multiple generations of long-term interactions between T lymphocytes and dendritic cells, and of thymocyte-stromal cell interactions.

A ganglion-cell-based primary image representation method and its contribution to object recognition

NASA Astrophysics Data System (ADS)

Wei, Hui; Dai, Zhi-Long; Zuo, Qing-Song

2016-10-01

A visual stimulus is represented by the biological visual system at several levels: in the order from low to high levels, they are: photoreceptor cells, ganglion cells (GCs), lateral geniculate nucleus cells and visual cortical neurons. Retinal GCs at the early level need to represent raw data only once, but meet a wide number of diverse requests from different vision-based tasks. This means the information representation at this level is general and not task-specific. Neurobiological findings have attributed this universal adaptation to GCs' receptive field (RF) mechanisms. For the purposes of developing a highly efficient image representation method that can facilitate information processing and interpretation at later stages, here we design a computational model to simulate the GC's non-classical RF. This new image presentation method can extract major structural features from raw data, and is consistent with other statistical measures of the image. Based on the new representation, the performances of other state-of-the-art algorithms in contour detection and segmentation can be upgraded remarkably. This work concludes that applying sophisticated representation schema at early state is an efficient and promising strategy in visual information processing.

Automated image analysis for quantitative fluorescence in situ hybridization with environmental samples.

PubMed

Zhou, Zhi; Pons, Marie Noëlle; Raskin, Lutgarde; Zilles, Julie L

2007-05-01

When fluorescence in situ hybridization (FISH) analyses are performed with complex environmental samples, difficulties related to the presence of microbial cell aggregates and nonuniform background fluorescence are often encountered. The objective of this study was to develop a robust and automated quantitative FISH method for complex environmental samples, such as manure and soil. The method and duration of sample dispersion were optimized to reduce the interference of cell aggregates. An automated image analysis program that detects cells from 4',6'-diamidino-2-phenylindole (DAPI) micrographs and extracts the maximum and mean fluorescence intensities for each cell from corresponding FISH images was developed with the software Visilog. Intensity thresholds were not consistent even for duplicate analyses, so alternative ways of classifying signals were investigated. In the resulting method, the intensity data were divided into clusters using fuzzy c-means clustering, and the resulting clusters were classified as target (positive) or nontarget (negative). A manual quality control confirmed this classification. With this method, 50.4, 72.1, and 64.9% of the cells in two swine manure samples and one soil sample, respectively, were positive as determined with a 16S rRNA-targeted bacterial probe (S-D-Bact-0338-a-A-18). Manual counting resulted in corresponding values of 52.3, 70.6, and 61.5%, respectively. In two swine manure samples and one soil sample 21.6, 12.3, and 2.5% of the cells were positive with an archaeal probe (S-D-Arch-0915-a-A-20), respectively. Manual counting resulted in corresponding values of 22.4, 14.0, and 2.9%, respectively. This automated method should facilitate quantitative analysis of FISH images for a variety of complex environmental samples.

Imaging elemental distribution and ion transport in cultured cells with ion microscopy.

PubMed

Chandra, S; Morrison, G H

1985-06-28

Both elemental distribution and ion transport in cultured cells have been imaged by ion microscopy. Morphological and chemical information was obtained with a spatial resolution of approximately 0.5 micron for sodium, potassium, calcium, and magnesium in freeze-fixed, cryofractured, and freeze-dried normal rat kidney cells and Chinese hamster ovary cells. Ion transport was successfully demonstrated by imaging Na+-K+ fluxes after the inhibition of Na+- and K+ -dependent adenosine triphosphatase with ouabain. This method allows measurements of elemental (isotopic) distribution to be related to cell morphology, thereby providing the means for studying ion distribution and ion transport under different physiological, pathological, and toxicological conditions in cell culture systems.

Force-activatable coating enables high-resolution cellular force imaging directly on regular cell culture surfaces.

PubMed

Sarkar, Anwesha; Zhao, Yuanchang; Wang, Yongliang; Wang, Xuefeng

2018-06-25

Integrin-transmitted cellular forces are crucial mechanical signals regulating a vast range of cell functions. Although various methods have been developed to visualize and quantify cellular forces at the cell-matrix interface, a method with high performance and low technical barrier is still in demand. Here we developed a force-activatable coating (FAC), which can be simply coated on regular cell culture apparatus' surfaces by physical adsorption, and turn these surfaces to force reporting platforms that enable cellular force mapping directly by fluorescence imaging. The FAC molecule consists of an adhesive domain for surface coating and a force-reporting domain which can be activated to fluoresce by integrin molecular tension. The tension threshold required for FAC activation is tunable in 10-60 piconewton (pN), allowing the selective imaging of cellular force contributed by integrin tension at different force levels. We tested the performance of two FACs with tension thresholds of 12 and 54 pN (nominal values), respectively, on both glass and polystyrene surfaces. Cellular forces were successfully mapped by fluorescence imaging on all the surfaces. FAC-coated surfaces also enable co-imaging of cellular forces and cell structures in both live cells and immunostained cells, therefore opening a new avenue for the study of the interplay of force and structure. We demonstrated the co-imaging of integrin tension and talin clustering in live cells, and concluded that talin clustering always occurs before the generation of integrin tension above 54 pN, reinforcing the notion that talin is an important adaptor protein for integrin tension transmission. Overall, FAC provides a highly convenient approach that is accessible to general biological laboratories for the study of cellular forces with high sensitivity and resolution, thus holding the potential to greatly boost the research of cell mechanobiology.

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

RECOVERY ACT: MULTIMODAL IMAGING FOR SOLAR CELL MICROCRACK DETECTION

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

Janice Hudgings; Lawrence Domash

2012-02-08

Undetected microcracks in solar cells are a principal cause of failure in service due to subsequent weather exposure, mechanical flexing or diurnal temperature cycles. Existing methods have not been able to detect cracks early enough in the production cycle to prevent inadvertent shipment to customers. This program, sponsored under the DOE Photovoltaic Supply Chain and Cross-Cutting Technologies program, studied the feasibility of quantifying surface micro-discontinuities by use of a novel technique, thermoreflectance imaging, to detect surface temperature gradients with very high spatial resolution, in combination with a suite of conventional imaging methods such as electroluminescence. The project carried out laboratorymore » tests together with computational image analyses using sample solar cells with known defects supplied by industry sources or DOE National Labs. Quantitative comparisons between the effectiveness of the new technique and conventional methods were determined in terms of the smallest detectable crack. Also the robustness of the new technique for reliable microcrack detection was determined at various stages of processing such as before and after antireflectance treatments. An overall assessment is that the new technique compares favorably with existing methods such as lock-in thermography or ultrasonics. The project was 100% completed in Sept, 2010. A detailed report of key findings from this program was published as: Q.Zhou, X.Hu, K.Al-Hemyari, K.McCarthy, L.Domash and J.Hudgings, High spatial resolution characterization of silicon solar cells using thermoreflectance imaging, J. Appl. Phys, 110, 053108 (2011).« less

A semi-Markov model for mitosis segmentation in time-lapse phase contrast microscopy image sequences of stem cell populations.

PubMed

Liu, An-An; Li, Kang; Kanade, Takeo

2012-02-01

We propose a semi-Markov model trained in a max-margin learning framework for mitosis event segmentation in large-scale time-lapse phase contrast microscopy image sequences of stem cell populations. Our method consists of three steps. First, we apply a constrained optimization based microscopy image segmentation method that exploits phase contrast optics to extract candidate subsequences in the input image sequence that contains mitosis events. Then, we apply a max-margin hidden conditional random field (MM-HCRF) classifier learned from human-annotated mitotic and nonmitotic sequences to classify each candidate subsequence as a mitosis or not. Finally, a max-margin semi-Markov model (MM-SMM) trained on manually-segmented mitotic sequences is utilized to reinforce the mitosis classification results, and to further segment each mitosis into four predefined temporal stages. The proposed method outperforms the event-detection CRF model recently reported by Huh as well as several other competing methods in very challenging image sequences of multipolar-shaped C3H10T1/2 mesenchymal stem cells. For mitosis detection, an overall precision of 95.8% and a recall of 88.1% were achieved. For mitosis segmentation, the mean and standard deviation for the localization errors of the start and end points of all mitosis stages were well below 1 and 2 frames, respectively. In particular, an overall temporal location error of 0.73 ± 1.29 frames was achieved for locating daughter cell birth events.

Programmable oligonucleotide probes design and applications for in situ and in vivo RNA imaging in cells

NASA Astrophysics Data System (ADS)

Cheglakov, Zoya

Unequal spreading of mRNA is a frequent experience observed in varied cell lines. The study of cellular processes dynamics and precise localization of mRNAs offers a vital toolbox to target specific proteins in precise cytoplasmic areas and provides a convenient instrument to uncover their mechanisms and functions. Latest methodological innovations have allowed imaging of a single mRNA molecule in situ and in vivo. Today, Fluorescent In Situ Hybridization (FISH) methods allow the studying of mRNA expression and offer a vital toolbox for accurate biological models. Studies enable analysis of the dynamics of an individual mRNA, have uncovered the multiplex RNA transport systems. With all current approaches, a single mRNA tracking in the mammalian cells is still challenging. This thesis describes mRNA detection methods based on programmable fluorophore-labeled DNA structures complimentary to native targets providing an accurate mRNA imaging in mammalian cells. First method represents beta-actin (ACTB) transcripts in situ detection in human cells, the technique strategy is based on programmable DNA probes, amplified by rolling circle amplification (RCA). The method reports precise localization of molecule of interest with an accuracy of a single-cell. Visualization and localization of specific endogenous mRNA molecules in real-time in vivo has the promising to innovate cellular biology studies, medical analysis and to provide a vital toolbox in drugs invention area. Second method described in this thesis represents miR-21 miRNA detection within a single live-cell resolution. The method using fluorophore-labeled short synthetic DNAs probes forming a stem-loop shape and generating Fluorescent Resonance Energy Transfer (FRET) as a result of target-probes hybridization. Catalytic nucleic acid (DNAzymes) probes are cooperative tool for precise detection of different mRNA targets. With assistance of a complementary fluorophore-quencher labeled substrate, the DNAzymes provide a beneficial strategy for simultaneous tracking readily accomplished by multicolor imaging with diverse fluorescent tags. The third method in this thesis will demonstrate the advantage of DNAzymes probes amplification, and offers potential strategy to monitor miRNAs in mammalian live cells.

High-speed stimulated Raman scattering microscopy for studying the metabolic diversity of motile Euglena gracilis

NASA Astrophysics Data System (ADS)

Suzuki, Y.; Wakisaka, Y.; Iwata, O.; Nakashima, A.; Ito, T.; Hirose, M.; Domon, R.; Sugawara, M.; Tsumura, N.; Watarai, H.; Shimobaba, T.; Suzuki, K.; Goda, K.; Ozeki, Y.

2017-02-01

Microalgae have been receiving great attention for their ability to produce biomaterials that are applicable for food supplements, drugs, biodegradable plastics, and biofuels. Among such microalgae, Euglena gracilis has become a popular species by virtue of its capability of accumulating useful metabolites including paramylon and lipids. In order to maximize the production of desired metabolites, it is essential to find ideal culturing conditions and to develop efficient methods for genetic transformation. To achieve this, understanding and controlling cell-to-cell variations in response to external stress is essential, with chemically specific analysis of microalgal cells including E. gracilis. However, conventional analytical tools such as fluorescence microscopy and spontaneous Raman scattering are not suitable for evaluation of diverse populations of motile microalgae, being restricted either by the requirement for fluorescent labels or a limited imaging speed, respectively. Here we demonstrate video-rate label-free metabolite imaging of live E. gracilis using stimulated Raman scattering (SRS) - an optical spectroscopic method for probing the vibrational signatures of molecules with orders of magnitude higher sensitivity than spontaneous Raman scattering. Our SRS's highspeed image acquisition (27 metabolite images per second) allows for population analysis of live E. gracilis cells cultured under nitrogen-deficiency - a technique for promoting the accumulation of paramylon and lipids within the cell body. Thus, our SRS system's fast imaging capability enables quantification and analysis of previously unresolvable cell-to-cell variations in the metabolite accumulation of large motile E. gracilis cell populations.

New Tools for Comparing Microscopy Images: Quantitative Analysis of Cell Types in Bacillus subtilis

PubMed Central

van Gestel, Jordi; Vlamakis, Hera

2014-01-01

Fluorescence microscopy is a method commonly used to examine individual differences between bacterial cells, yet many studies still lack a quantitative analysis of fluorescence microscopy data. Here we introduce some simple tools that microbiologists can use to analyze and compare their microscopy images. We show how image data can be converted to distribution data. These data can be subjected to a cluster analysis that makes it possible to objectively compare microscopy images. The distribution data can further be analyzed using distribution fitting. We illustrate our methods by scrutinizing two independently acquired data sets, each containing microscopy images of a doubly labeled Bacillus subtilis strain. For the first data set, we examined the expression of srfA and tapA, two genes which are expressed in surfactin-producing and matrix-producing cells, respectively. For the second data set, we examined the expression of eps and tapA; these genes are expressed in matrix-producing cells. We show that srfA is expressed by all cells in the population, a finding which contrasts with a previously reported bimodal distribution of srfA expression. In addition, we show that eps and tapA do not always have the same expression profiles, despite being expressed in the same cell type: both operons are expressed in cell chains, while single cells mainly express eps. These findings exemplify that the quantification and comparison of microscopy data can yield insights that otherwise would go unnoticed. PMID:25448819

New tools for comparing microscopy images: quantitative analysis of cell types in Bacillus subtilis.

PubMed

van Gestel, Jordi; Vlamakis, Hera; Kolter, Roberto

2015-02-15

Fluorescence microscopy is a method commonly used to examine individual differences between bacterial cells, yet many studies still lack a quantitative analysis of fluorescence microscopy data. Here we introduce some simple tools that microbiologists can use to analyze and compare their microscopy images. We show how image data can be converted to distribution data. These data can be subjected to a cluster analysis that makes it possible to objectively compare microscopy images. The distribution data can further be analyzed using distribution fitting. We illustrate our methods by scrutinizing two independently acquired data sets, each containing microscopy images of a doubly labeled Bacillus subtilis strain. For the first data set, we examined the expression of srfA and tapA, two genes which are expressed in surfactin-producing and matrix-producing cells, respectively. For the second data set, we examined the expression of eps and tapA; these genes are expressed in matrix-producing cells. We show that srfA is expressed by all cells in the population, a finding which contrasts with a previously reported bimodal distribution of srfA expression. In addition, we show that eps and tapA do not always have the same expression profiles, despite being expressed in the same cell type: both operons are expressed in cell chains, while single cells mainly express eps. These findings exemplify that the quantification and comparison of microscopy data can yield insights that otherwise would go unnoticed. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Comparison of segmentation algorithms for fluorescence microscopy images of cells.

PubMed

Dima, Alden A; Elliott, John T; Filliben, James J; Halter, Michael; Peskin, Adele; Bernal, Javier; Kociolek, Marcin; Brady, Mary C; Tang, Hai C; Plant, Anne L

2011-07-01

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc.

Accurate measurement of peripheral blood mononuclear cell concentration using image cytometry to eliminate RBC-induced counting error.

PubMed

Chan, Leo Li-Ying; Laverty, Daniel J; Smith, Tim; Nejad, Parham; Hei, Hillary; Gandhi, Roopali; Kuksin, Dmitry; Qiu, Jean

2013-02-28

Peripheral blood mononuclear cells (PBMCs) have been widely researched in the fields of immunology, infectious disease, oncology, transplantation, hematological malignancy, and vaccine development. Specifically, in immunology research, PBMCs have been utilized to monitor concentration, viability, proliferation, and cytokine production from immune cells, which are critical for both clinical trials and biomedical research. The viability and concentration of isolated PBMCs are traditionally measured by manual counting with trypan blue (TB) using a hemacytometer. One of the common issues of PBMC isolation is red blood cell (RBC) contamination. The RBC contamination can be dependent on the donor sample and/or technical skill level of the operator. RBC contamination in a PBMC sample can introduce error to the measured concentration, which can pass down to future experimental assays performed on these cells. To resolve this issue, RBC lysing protocol can be used to eliminate potential error caused by RBC contamination. In the recent years, a rapid fluorescence-based image cytometry system has been utilized for bright-field and fluorescence imaging analysis of cellular characteristics (Nexcelom Bioscience LLC, Lawrence, MA). The Cellometer image cytometry system has demonstrated the capability of automated concentration and viability detection in disposable counting chambers of unpurified mouse splenocytes and PBMCs stained with acridine orange (AO) and propidium iodide (PI) under fluorescence detection. In this work, we demonstrate the ability of Cellometer image cytometry system to accurately measure PBMC concentration, despite RBC contamination, by comparison of five different total PBMC counting methods: (1) manual counting of trypan blue-stained PBMCs in hemacytometer, (2) manual counting of PBMCs in bright-field images, (3) manual counting of acetic acid lysing of RBCs with TB-stained PBMCs, (4) automated counting of acetic acid lysing of RBCs with PI-stained PBMCs, and (5) AO/PI dual staining method. The results show comparable total PBMC counting among all five methods, which validate the AO/PI staining method for PBMC measurement in the image cytometry method. Copyright © 2012 Elsevier B.V. All rights reserved.

Fuzzy entropy thresholding and multi-scale morphological approach for microscopic image enhancement

NASA Astrophysics Data System (ADS)

Zhou, Jiancan; Li, Yuexiang; Shen, Linlin

2017-07-01

Microscopic images provide lots of useful information for modern diagnosis and biological research. However, due to the unstable lighting condition during image capturing, two main problems, i.e., high-level noises and low image contrast, occurred in the generated cell images. In this paper, a simple but efficient enhancement framework is proposed to address the problems. The framework removes image noises using a hybrid method based on wavelet transform and fuzzy-entropy, and enhances the image contrast with an adaptive morphological approach. Experiments on real cell dataset were made to assess the performance of proposed framework. The experimental results demonstrate that our proposed enhancement framework increases the cell tracking accuracy to an average of 74.49%, which outperforms the benchmark algorithm, i.e., 46.18%.

In Vivo Myeloperoxidase Imaging and Flow Cytometry Analysis of Intestinal Myeloid Cells.

PubMed

Hülsdünker, Jan; Zeiser, Robert

2016-01-01

Myeloperoxidase (MPO) imaging is a non-invasive method to detect cells that produce the enzyme MPO that is most abundant in neutrophils, macrophages, and inflammatory monocytes. While lacking specificity for any of these three cell types, MPO imaging can provide guidance for further flow cytometry-based analysis of tissues where these cell types reside. Isolation of leukocytes from the intestinal tract is an error-prone procedure. Here, we describe a protocol for intestinal leukocyte isolation that works reliable in our hands and allows for flow cytometry-based analysis, in particular of neutrophils.

Community detection for fluorescent lifetime microscopy image segmentation

NASA Astrophysics Data System (ADS)

Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Achilefu, Samuel; Nussinov, Zohar

2014-03-01

Multiresolution community detection (CD) method has been suggested in a recent work as an efficient method for performing unsupervised segmentation of fluorescence lifetime (FLT) images of live cell images containing fluorescent molecular probes.1 In the current paper, we further explore this method in FLT images of ex vivo tissue slices. The image processing problem is framed as identifying clusters with respective average FLTs against a background or "solvent" in FLT imaging microscopy (FLIM) images derived using NIR fluorescent dyes. We have identified significant multiresolution structures using replica correlations in these images, where such correlations are manifested by information theoretic overlaps of the independent solutions ("replicas") attained using the multiresolution CD method from different starting points. In this paper, our method is found to be more efficient than a current state-of-the-art image segmentation method based on mixture of Gaussian distributions. It offers more than 1:25 times diversity based on Shannon index than the latter method, in selecting clusters with distinct average FLTs in NIR FLIM images.

An analytical tool that quantifies cellular morphology changes from three-dimensional fluorescence images.

PubMed

Haass-Koffler, Carolina L; Naeemuddin, Mohammad; Bartlett, Selena E

2012-08-31

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology even in complex tissue sections. Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells, however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgenössische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.

Automated processing of label-free Raman microscope images of macrophage cells with standardized regression for high-throughput analysis.

PubMed

Milewski, Robert J; Kumagai, Yutaro; Fujita, Katsumasa; Standley, Daron M; Smith, Nicholas I

2010-11-19

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively. We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest. The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.

Nanoscale Imaging of Whole Cells Using a Liquid Enclosure and a Scanning Transmission Electron Microscope

PubMed Central

Peckys, Diana B.; Veith, Gabriel M.; Joy, David C.; de Jonge, Niels

2009-01-01

Nanoscale imaging techniques are needed to investigate cellular function at the level of individual proteins and to study the interaction of nanomaterials with biological systems. We imaged whole fixed cells in liquid state with a scanning transmission electron microscope (STEM) using a micrometer-sized liquid enclosure with electron transparent windows providing a wet specimen environment. Wet-STEM images were obtained of fixed E. coli bacteria labeled with gold nanoparticles attached to surface membrane proteins. Mammalian cells (COS7) were incubated with gold-tagged epidermal growth factor and fixed. STEM imaging of these cells resulted in a resolution of 3 nm for the gold nanoparticles. The wet-STEM method has several advantages over conventional imaging techniques. Most important is the capability to image whole fixed cells in a wet environment with nanometer resolution, which can be used, e.g., to map individual protein distributions in/on whole cells. The sample preparation is compatible with that used for fluorescent microscopy on fixed cells for experiments involving nanoparticles. Thirdly, the system is rather simple and involves only minimal new equipment in an electron microscopy (EM) laboratory. PMID:20020038

New approaches for the analysis of confluent cell layers with quantitative phase digital holographic microscopy

NASA Astrophysics Data System (ADS)

Pohl, L.; Kaiser, M.; Ketelhut, S.; Pereira, S.; Goycoolea, F.; Kemper, Björn

2016-03-01

Digital holographic microscopy (DHM) enables high resolution non-destructive inspection of technical surfaces and minimally-invasive label-free live cell imaging. However, the analysis of confluent cell layers represents a challenge as quantitative DHM phase images in this case do not provide sufficient information for image segmentation, determination of the cellular dry mass or calculation of the cell thickness. We present novel strategies for the analysis of confluent cell layers with quantitative DHM phase contrast utilizing a histogram based-evaluation procedure. The applicability of our approach is illustrated by quantification of drug induced cell morphology changes and it is shown that the method is capable to quantify reliable global morphology changes of confluent cell layers.

CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging.

PubMed

Held, Michael; Schmitz, Michael H A; Fischer, Bernd; Walter, Thomas; Neumann, Beate; Olma, Michael H; Peter, Matthias; Ellenberg, Jan; Gerlich, Daniel W

2010-09-01

Fluorescence time-lapse imaging has become a powerful tool to investigate complex dynamic processes such as cell division or intracellular trafficking. Automated microscopes generate time-resolved imaging data at high throughput, yet tools for quantification of large-scale movie data are largely missing. Here we present CellCognition, a computational framework to annotate complex cellular dynamics. We developed a machine-learning method that combines state-of-the-art classification with hidden Markov modeling for annotation of the progression through morphologically distinct biological states. Incorporation of time information into the annotation scheme was essential to suppress classification noise at state transitions and confusion between different functional states with similar morphology. We demonstrate generic applicability in different assays and perturbation conditions, including a candidate-based RNA interference screen for regulators of mitotic exit in human cells. CellCognition is published as open source software, enabling live-cell imaging-based screening with assays that directly score cellular dynamics.

Advantages of indium-tin oxide-coated glass slides in correlative scanning electron microscopy applications of uncoated cultured cells.

PubMed

Pluk, H; Stokes, D J; Lich, B; Wieringa, B; Fransen, J

2009-03-01

A method of direct visualization by correlative scanning electron microscopy (SEM) and fluorescence light microscopy of cell structures of tissue cultured cells grown on conductive glass slides is described. We show that by growing cells on indium-tin oxide (ITO)-coated glass slides, secondary electron (SE) and backscatter electron (BSE) images of uncoated cells can be obtained in high-vacuum SEM without charging artefacts. Interestingly, we observed that BSE imaging is influenced by both accelerating voltage and ITO coating thickness. By combining SE and BSE imaging with fluorescence light microscopy imaging, we were able to reveal detailed features of actin cytoskeletal and mitochondrial structures in mouse embryonic fibroblasts. We propose that the application of ITO glass as a substrate for cell culture can easily be extended and offers new opportunities for correlative light and electron microscopy studies of adherently growing cells.

Deconstructing autofluorescence: non-invasive detection and monitoring of biochemistry in cells and tissues (Conference Presentation)

NASA Astrophysics Data System (ADS)

Goldys, Ewa M.; Gosnell, Martin E.; Anwer, Ayad G.; Cassano, Juan C.; Sue, Carolyn M.; Mahbub, Saabah B.; Pernichery, Sandeep M.; Inglis, David W.; Adhikary, Partho P.; Jazayeri, Jalal A.; Cahill, Michael A.; Saad, Sonia; Pollock, Carol; Sutton-Mcdowall, Melanie L.; Thompson, Jeremy G.

2016-03-01

Automated and unbiased methods of non-invasive cell monitoring able to deal with complex biological heterogeneity are fundamentally important for biology and medicine. Label-free cell imaging provides information about endogenous fluorescent metabolites, enzymes and cofactors in cells. However extracting high content information from imaging of native fluorescence has been hitherto impossible. Here, we quantitatively characterise cell populations in different tissue types, live or fixed, by using novel image processing and a simple multispectral upgrade of a wide-field fluorescence microscope. Multispectral intrinsic fluorescence imaging was applied to patient olfactory neurosphere-derived cells, cell model of a human metabolic disease MELAS (mitochondrial myopathy, encephalomyopathy, lactic acidosis, stroke-like syndrome). By using an endogenous source of contrast, subtle metabolic variations have been detected between living cells in their full morphological context which made it possible to distinguish healthy from diseased cells before and after therapy. Cellular maps of native fluorophores, flavins, bound and free NADH and retinoids unveiled subtle metabolic signatures and helped uncover significant cell subpopulations, in particular a subpopulation with compromised mitochondrial function. The versatility of our method is further illustrated by detecting genetic mutations in cancer, non-invasive monitoring of CD90 expression, label-free tracking of stem cell differentiation, identifying stem cell subpopulations with varying functional characteristics, tissue diagnostics in diabetes, and assessing the condition of preimplantation embryos. Our optimal discrimination approach enables statistical hypothesis testing and intuitive visualisations where previously undetectable differences become clearly apparent.

Reconstruction of incomplete cell paths through a 3D-2D level set segmentation

NASA Astrophysics Data System (ADS)

Hariri, Maia; Wan, Justin W. L.

2012-02-01

Segmentation of fluorescent cell images has been a popular technique for tracking live cells. One challenge of segmenting cells from fluorescence microscopy is that cells in fluorescent images frequently disappear. When the images are stacked together to form a 3D image volume, the disappearance of the cells leads to broken cell paths. In this paper, we present a segmentation method that can reconstruct incomplete cell paths. The key idea of this model is to perform 2D segmentation in a 3D framework. The 2D segmentation captures the cells that appear in the image slices while the 3D segmentation connects the broken cell paths. The formulation is similar to the Chan-Vese level set segmentation which detects edges by comparing the intensity value at each voxel with the mean intensity values inside and outside of the level set surface. Our model, however, performs the comparison on each 2D slice with the means calculated by the 2D projected contour. The resulting effect is to segment the cells on each image slice. Unlike segmentation on each image frame individually, these 2D contours together form the 3D level set function. By enforcing minimum mean curvature on the level set surface, our segmentation model is able to extend the cell contours right before (and after) the cell disappears (and reappears) into the gaps, eventually connecting the broken paths. We will present segmentation results of C2C12 cells in fluorescent images to illustrate the effectiveness of our model qualitatively and quantitatively by different numerical examples.

Diagnosis of basal cell carcinoma by two photon excited fluorescence combined with lifetime imaging

NASA Astrophysics Data System (ADS)

Fan, Shunping; Peng, Xiao; Liu, Lixin; Liu, Shaoxiong; Lu, Yuan; Qu, Junle

2014-02-01

Basal cell carcinoma (BCC) is the most common type of human skin cancer. The traditional diagnostic procedure of BCC is histological examination with haematoxylin and eosin staining of the tissue biopsy. In order to reduce complexity of the diagnosis procedure, a number of noninvasive optical methods have been applied in skin examination, for example, multiphoton tomography (MPT) and fluorescence lifetime imaging microscopy (FLIM). In this study, we explored two-photon optical tomography of human skin specimens using two-photon excited autofluorescence imaging and FLIM. There are a number of naturally endogenous fluorophores in skin sample, such as keratin, melanin, collagen, elastin, flavin and porphyrin. Confocal microscopy was used to obtain structures of the sample. Properties of epidermic and cancer cells were characterized by fluorescence emission spectra, as well as fluorescence lifetime imaging. Our results show that two-photon autofluorescence lifetime imaging can provide accurate optical biopsies with subcellular resolution and is potentially a quantitative optical diagnostic method in skin cancer diagnosis.

A hybrid scanning mode for fast scanning ion conductance microscopy (SICM) imaging

PubMed Central

Zhukov, Alex; Richards, Owen; Ostanin, Victor; Korchev, Yuri; Klenerman, David

2012-01-01

We have developed a new method of controlling the pipette for scanning ion conductance microscopy to obtain high-resolution images faster. The method keeps the pipette close to the surface during a single line scan but does not follow the exact surface topography, which is calculated by using the ion current. Using an FPGA platform we demonstrate this new method on model test samples and then on live cells. This method will be particularly useful to follow changes occurring on relatively flat regions of the cell surface at high spatial and temporal resolutions. PMID:22902298

Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours

PubMed Central

Molnar, Csaba; Jermyn, Ian H.; Kato, Zoltan; Rahkama, Vesa; Östling, Päivi; Mikkonen, Piia; Pietiäinen, Vilja; Horvath, Peter

2016-01-01

The identification of fluorescently stained cell nuclei is the basis of cell detection, segmentation, and feature extraction in high content microscopy experiments. The nuclear morphology of single cells is also one of the essential indicators of phenotypic variation. However, the cells used in experiments can lose their contact inhibition, and can therefore pile up on top of each other, making the detection of single cells extremely challenging using current segmentation methods. The model we present here can detect cell nuclei and their morphology even in high-confluency cell cultures with many overlapping cell nuclei. We combine the “gas of near circles” active contour model, which favors circular shapes but allows slight variations around them, with a new data model. This captures a common property of many microscopic imaging techniques: the intensities from superposed nuclei are additive, so that two overlapping nuclei, for example, have a total intensity that is approximately double the intensity of a single nucleus. We demonstrate the power of our method on microscopic images of cells, comparing the results with those obtained from a widely used approach, and with manual image segmentations by experts. PMID:27561654

A study of glasses-type color CGH using a color filter considering reduction of blurring

NASA Astrophysics Data System (ADS)

Iwami, Saki; Sakamoto, Yuji

2009-02-01

We have developed a glasses-type color computer generated hologram (CGH) by using a color filter. The proposed glasses consist of two "lenses" made of overlapping holograms and color filters. The holograms, which are calculated to reconstruct images in each primary color, are divided to small areas, which we called cells, and superimposed on one hologram. In the same way, colors of the filter correspond to the hologram cells. We can configure it very simply without a complex optical system, and the configuration yields a small and light weight system suitable for glasses. When the cell is small enough, the colors are mixed and reconstructed color images are observed. In addition, color expression of reconstruction images improves, too. However, using small cells blurrs reconstructed images because of the following reasons: (1) interference between cells because of the correlation with the cells, and (2) reduction of resolution caused by the size of the cell hologram. We are investigating in order to make a hologram that has high resolution reconstructed color images without ghost images. In this paper, we discuss (1) the details of the proposed glasses-type color CGH, (2) appropriate cell size for an eye system, (3) effects of cell shape on the reconstructed images, and (4) a new method to reduce the blurring of the images.

A plasmid-based reporter system for live cell imaging of dengue virus infected cells.

PubMed

Medin, Carey L; Valois, Sierra; Patkar, Chinmay G; Rothman, Alan L

2015-01-01

Cell culture models are used widely to study the effects of dengue virus (DENV) on host cell function. Current methods of identification of cells infected with an unmodified DENV requires fixation and permeablization of cells to allow DENV-specific antibody staining. This method does not permit imaging of viable cells over time. In this report, a plasmid-based reporter was developed to allow non-destructive identification of DENV-infected cells. The plasmid-based reporter was demonstrated to be broadly applicable to the four DENV serotypes, including low-passaged strains, and was specifically cleaved by the viral protease with minimal interference on viral production. This study reveals the potential for this novel reporter system to advance the studies of virus-host interactions during DENV infection. Copyright © 2014 Elsevier B.V. All rights reserved.

Segmentation of Vasculature from Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images.

PubMed

Bates, Russell; Irving, Benjamin; Markelc, Bostjan; Kaeppler, Jakob; Brown, Graham; Muschel, Ruth J; Brady, Sir Michael; Grau, Vicente; Schnabel, Julia A

2017-08-09

Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods, however, particularly in the study of tumors it is of particular interest to be able to visualize both perfused and non-perfused vasculature. Imaging vasculature by highlighting the endothelium provides a way to separate the morphology of vasculature from the potentially confounding factor of perfusion. Here we present a method for the segmentation of tumor vasculature in 3D fluorescence microscopy images using signals from the endothelial and surrounding cells. We show that our method can provide complete and semantically meaningful segmentations of complex vasculature using a supervoxel-Markov Random Field approach. We show that in terms of extracting meaningful segmentations of the vasculature, our method out-performs both a state-ofthe- art method, specific to these data, as well as more classical vasculature segmentation methods.

Hyperspectral microscope for in vivo imaging of microstructures and cells in tissues

DOEpatents

Demos,; Stavros, G [Livermore, CA

2011-05-17

An optical hyperspectral/multimodal imaging method and apparatus is utilized to provide high signal sensitivity for implementation of various optical imaging approaches. Such a system utilizes long working distance microscope objectives so as to enable off-axis illumination of predetermined tissue thereby allowing for excitation at any optical wavelength, simplifies design, reduces required optical elements, significantly reduces spectral noise from the optical elements and allows for fast image acquisition enabling high quality imaging in-vivo. Such a technology provides a means of detecting disease at the single cell level such as cancer, precancer, ischemic, traumatic or other type of injury, infection, or other diseases or conditions causing alterations in cells and tissue micro structures.

Live-cell imaging of mammalian RNAs with Spinach2.

PubMed

Strack, Rita L; Jaffrey, Samie R

2015-01-01

The ability to monitor RNAs of interest in living cells is crucial to understanding the function, dynamics, and regulation of this important class of molecules. In recent years, numerous strategies have been developed with the goal of imaging individual RNAs of interest in living cells, each with their own advantages and limitations. This chapter provides an overview of current methods of live-cell RNA imaging, including a detailed discussion of genetically encoded strategies for labeling RNAs in mammalian cells. This chapter then focuses on the development and use of "RNA mimics of GFP" or Spinach technology for tagging mammalian RNAs and includes a detailed protocol for imaging 5S and CGG60 RNA with the recently described Spinach2 tag. © 2015 Elsevier Inc. All rights reserved.

[Watching dance of the molecules - CARS microscopy].

PubMed

Korczyński, Jaroslaw; Kubiak, Katarzyna; Węgłowska, Edyta

2017-01-01

CARS (Coherent Anti-Stokes Raman Scattering) microscopy is an imaging method for living cells visualization as well as for food or cosmetics material analysis without the need for staining. The near infrared laser source generates the CARS signal - the characteristic intrinsic vibrational contrast of the molecules in a sample which is no longer caused by staining, but by the molecules themselves. It provides the benefit of a non-toxic, non-destructive and almost noninvasive method for sample imaging. CARS can easily be combined with fluorescence confocal microscopy so it is an excellent complementary imaging method. In this article we showed some of the applications for this technology: imaging of lipid droplets inside human HaCaT cells and analysis of the composition of cosmetic products. Moreover we believe, that soon new fields of application become accessible for this rapidly developing branch of microscopy.

RNA imaging: tracking in real-time RNA transport in neurons using molecular beacons and confocal microscopy.

PubMed

Zepeda, Angélica; Arias, Clorinda; Flores-Jasso, Fabian; Vaca, Luis

2013-01-01

RNAs are present within eukaryotic cells and are involved in several biological processes. RNA transport within cell compartments is important for proper cell function. To understand in depth the cellular processes in which RNA is involved requires a method that reveals RNA localization in real time in a sub-cellular context in living cells. In this protocol we describe a method for imaging RNA in living cells and in particular in neuronal cultures based on cell microinjection of molecular beacons in conjunction with confocal microscopy. This methodology overcomes some of the main obstacles for imaging RNA in live cells since microinjection allows the delivery of the probe to a desired cellular compartment and MBs bind with high specificity to its target RNA without inhibiting its function. The proper design of the MBs is essential to obtain RNA-MB association at the temperature of the cell cytosol. MBs design with other purposes in mind (such as PCR experiments) have a design that facilitates association to its target at high temperatures, rendering them unsuitable for live cell imaging. Using the methodology described in this chapter allows the study of RNA transport to different regions of neurons and may be combined with the tagging of proteins of interest to measure co-transport of the protein and the RNA to different cellular regions. Copyright © 2013 Elsevier Inc. All rights reserved.

In vivo cell tracking and quantification method in adult zebrafish

NASA Astrophysics Data System (ADS)

Zhang, Li; Alt, Clemens; Li, Pulin; White, Richard M.; Zon, Leonard I.; Wei, Xunbin; Lin, Charles P.

2012-03-01

Zebrafish have become a powerful vertebrate model organism for drug discovery, cancer and stem cell research. A recently developed transparent adult zebrafish using double pigmentation mutant, called casper, provide unparalleled imaging power in in vivo longitudinal analysis of biological processes at an anatomic resolution not readily achievable in murine or other systems. In this paper we introduce an optical method for simultaneous visualization and cell quantification, which combines the laser scanning confocal microscopy (LSCM) and the in vivo flow cytometry (IVFC). The system is designed specifically for non-invasive tracking of both stationary and circulating cells in adult zebrafish casper, under physiological conditions in the same fish over time. The confocal imaging part in this system serves the dual purposes of imaging fish tissue microstructure and a 3D navigation tool to locate a suitable vessel for circulating cell counting. The multi-color, multi-channel instrument allows the detection of multiple cell populations or different tissues or organs simultaneously. We demonstrate initial testing of this novel instrument by imaging vasculature and tracking circulating cells in CD41: GFP/Gata1: DsRed transgenic casper fish whose thrombocytes/erythrocytes express the green and red fluorescent proteins. Circulating fluorescent cell incidents were recorded and counted repeatedly over time and in different types of vessels. Great application opportunities in cancer and stem cell researches are discussed.

Time-resolved, single-cell analysis of induced and programmed cell death via non-invasive propidium iodide and counterstain perfusion.

PubMed

Krämer, Christina E M; Wiechert, Wolfgang; Kohlheyer, Dietrich

2016-09-01

Conventional propidium iodide (PI) staining requires the execution of multiple steps prior to analysis, potentially affecting assay results as well as cell vitality. In this study, this multistep analysis method has been transformed into a single-step, non-toxic, real-time method via live-cell imaging during perfusion with 0.1 μM PI inside a microfluidic cultivation device. Dynamic PI staining was an effective live/dead analytical tool and demonstrated consistent results for single-cell death initiated by direct or indirect triggers. Application of this method for the first time revealed the apparent antibiotic tolerance of wild-type Corynebacterium glutamicum cells, as indicated by the conversion of violet fluorogenic calcein acetoxymethyl ester (CvAM). Additional implementation of this method provided insight into the induced cell lysis of Escherichia coli cells expressing a lytic toxin-antitoxin module, providing evidence for non-lytic cell death and cell resistance to toxin production. Finally, our dynamic PI staining method distinguished necrotic-like and apoptotic-like cell death phenotypes in Saccharomyces cerevisiae among predisposed descendants of nutrient-deprived ancestor cells using PO-PRO-1 or green fluorogenic calcein acetoxymethyl ester (CgAM) as counterstains. The combination of single-cell cultivation, fluorescent time-lapse imaging, and PI perfusion facilitates spatiotemporally resolved observations that deliver new insights into the dynamics of cellular behaviour.

Time-lapse imaging assay using the BioStation CT: A sensitive drug-screening method for three-dimensional cell culture

PubMed Central

Sakamoto, Ruriko; Rahman, M Mamunur; Shimomura, Manami; Itoh, Manabu; Nakatsura, Tetsuya

2015-01-01

Three-dimensional (3D) cell culture is beneficial for physiological studies of tumor cells, due to its potential to deliver a high quantity of cell culture information that is representative of the cancer microenvironment and predictive of drug responses in vivo. Currently, gel-associated or matrix-associated 3D cell culture is comprised of intricate procedures that often result in experimental complexity. Therefore, we developed an innovative anti-cancer drug sensitivity screening technique for 3D cell culture on NanoCulture Plates (NCP) by employing the imaging device BioStation CT. Here, we showed that the human breast cancer cell lines BT474 and T47D form multicellular spheroids on NCP plates and compared their sensitivity to the anti-cancer drugs trastuzumab and paclitaxel using the BioStation CT. The anticancer drugs reduced spheroid migration velocity and suppressed spheroid fusion. In addition, primary cells derived from the human breast cancer tissues B58 and B61 grown on NCP plates also exhibited similar drug sensitivity. These results were in good agreement with the conventional assay method using ATP quantification. We confirmed the antitumor effects of the drugs on cells seeded in 96-well plates using the BioStation CT imaging technique. We expect this method to be useful in research for new antitumor agents and for drug sensitivity tests in individually-tailored cancer treatments. PMID:25865675

Segmentation of whole cells and cell nuclei from 3-D optical microscope images using dynamic programming.

PubMed

McCullough, D P; Gudla, P R; Harris, B S; Collins, J A; Meaburn, K J; Nakaya, M A; Yamaguchi, T P; Misteli, T; Lockett, S J

2008-05-01

Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived errors can be interactively corrected. Segmentation accuracy is not significantly affected by intermittent labeling of object surfaces, diffuse surfaces, or spurious signals away from surfaces. The unique strength of the segmentation method was demonstrated on a variety of biological tissue samples where all cells, including irregularly shaped cells, were accurately segmented based on visual inspection.

A MULTICORE BASED PARALLEL IMAGE REGISTRATION METHOD

PubMed Central

Yang, Lin; Gong, Leiguang; Zhang, Hong; Nosher, John L.; Foran, David J.

2012-01-01

Image registration is a crucial step for many image-assisted clinical applications such as surgery planning and treatment evaluation. In this paper we proposed a landmark based nonlinear image registration algorithm for matching 2D image pairs. The algorithm was shown to be effective and robust under conditions of large deformations. In landmark based registration, the most important step is establishing the correspondence among the selected landmark points. This usually requires an extensive search which is often computationally expensive. We introduced a nonregular data partition algorithm using the K-means clustering algorithm to group the landmarks based on the number of available processing cores. The step optimizes the memory usage and data transfer. We have tested our method using IBM Cell Broadband Engine (Cell/B.E.) platform. PMID:19964921

A novel method for measuring cellular antibody uptake using imaging flow cytometry reveals distinct uptake rates for two different monoclonal antibodies targeting L1.

PubMed

Hazin, John; Moldenhauer, Gerhard; Altevogt, Peter; Brady, Nathan R

2015-08-01

Monoclonal antibodies (mAbs) have emerged as a promising tool for cancer therapy. Differing approaches utilize mAbs to either deliver a drug to the tumor cells or to modulate the host's immune system to mediate tumor kill. The rate by which a therapeutic antibody is being internalized by tumor cells is a decisive feature for choosing the appropriate treatment strategy. We herein present a novel method to effectively quantitate antibody uptake of tumor cells by using image-based flow cytometry, which combines image analysis with high throughput of sample numbers and sample size. The use of this method is established by determining uptake rate of an anti-EpCAM antibody (HEA125), from single cell measurements of plasma membrane versus internalized antibody, in conjunction with inhibitors of endocytosis. The method is then applied to two mAbs (L1-9.3, L1-OV52.24) targeting the neural cell adhesion molecule L1 (L1CAM) at two different epitopes. Based on median cell population responses, we find that mAb L1-OV52.24 is rapidly internalized by the ovarian carcinoma cell line SKOV3ip while L1 mAb 9.3 is mainly retained at the cell surface. These findings suggest the L1 mAb OV52.24 as a candidate to be further developed for drug-delivery to cancer cells, while L1-9.3 may be optimized to tag the tumor cells and stimulate immunogenic cancer cell killing. Furthermore, when analyzing cell-to-cell variability, we observed L1 mAb OV52.24 rapidly transition into a subpopulation with high-internalization capacity. In summary, this novel high-content method for measuring antibody internalization rate provides a high level of accuracy and sensitivity for cell population measurements and reveals further biologically relevant information when taking into account cellular heterogeneity. Copyright © 2015 Elsevier B.V. All rights reserved.

Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study

NASA Astrophysics Data System (ADS)

Niazi, M. Khalid Khan; Pennell, Michael; Elkins, Camille; Hemminger, Jessica; Jin, Ming; Kirby, Sean; Kurt, Habibe; Miller, Barrie; Plocharczyk, Elizabeth; Roth, Rachel; Ziegler, Rebecca; Shana'ah, Arwa; Racke, Fred; Lozanski, Gerard; Gurcan, Metin N.

2013-03-01

Presence of Ki-67, a nuclear protein, is typically used to measure cell proliferation. The quantification of the Ki-67 proliferation index is performed visually by the pathologist; however, this is subject to inter- and intra-reader variability. Automated techniques utilizing digital image analysis by computers have emerged. The large variations in specimen preparation, staining, and imaging as well as true biological heterogeneity of tumor tissue often results in variable intensities in Ki-67 stained images. These variations affect the performance of currently developed methods. To optimize the segmentation of Ki-67 stained cells, one should define a data dependent transformation that will account for these color variations instead of defining a fixed linear transformation to separate different hues. To address these issues in images of tissue stained with Ki-67, we propose a methodology that exploits the intrinsic properties of CIE L∗a∗b∗ color space to translate this complex problem into an automatic entropy based thresholding problem. The developed method was evaluated through two reader studies with pathology residents and expert hematopathologists. Agreement between the proposed method and the expert pathologists was good (CCC = 0.80).

Tracing cell lineages in videos of lens-free microscopy.

PubMed

Rempfler, Markus; Stierle, Valentin; Ditzel, Konstantin; Kumar, Sanjeev; Paulitschke, Philipp; Andres, Bjoern; Menze, Bjoern H

2018-06-05

In vitro experiments with cultured cells are essential for studying their growth and migration pattern and thus, for gaining a better understanding of cancer progression and its treatment. Recent progress in lens-free microscopy (LFM) has rendered it an inexpensive tool for label-free, continuous live cell imaging, yet there is only little work on analysing such time-lapse image sequences. We propose (1) a cell detector for LFM images based on fully convolutional networks and residual learning, and (2) a probabilistic model based on moral lineage tracing that explicitly handles multiple detections and temporal successor hypotheses by clustering and tracking simultaneously. (3) We benchmark our method in terms of detection and tracking scores on a dataset of three annotated sequences of several hours of LFM, where we demonstrate our method to produce high quality lineages. (4) We evaluate its performance on a somewhat more challenging problem: estimating cell lineages from the LFM sequence as would be possible from a corresponding fluorescence microscopy sequence. We present experiments on 16 LFM sequences for which we acquired fluorescence microscopy in parallel and generated annotations from them. Finally, (5) we showcase our methods effectiveness for quantifying cell dynamics in an experiment with skin cancer cells. Copyright © 2018 Elsevier B.V. All rights reserved.

Magnetomotive imaging of iron oxide nanoparticles as cellular contrast agents for optical coherence tomography

NASA Astrophysics Data System (ADS)

Cimalla, Peter; Werner, Theresa; Gaertner, Maria; Mueller, Claudia; Walther, Julia; Wittig, Dierk; Ader, Marius; Karl, Mike; Koch, Edmund

2013-06-01

Recent studies in animal models provided proof-of-principle evidence for cell transplantation as a potential future therapeutic approach for retinal pathologies in humans such as Retinitis pigmentosa or age-related macular degeneration. In this case, donor cells are injected into the eye in order to protect or replace degenerating photoreceptors or retinal pigment epithelium. However, currently there is no three-dimensional imaging technique available that allows tracking of cell migration and integration into the host tissue under in vivo conditions. Therefore, we investigate about magnetomotive optical coherence tomography (OCT) of substances labeled with iron oxide nanoparticles as a potential method for noninvasive, three-dimensional cell tracking in the retina. We use a self-developed spectral domain OCT system for high-resolution imaging in the 800 nm-wavelength region. A suitable AC magnetic field for magnetomotive imaging was generated using two different setups, which consist of an electrically driven solenoid in combination with a permanent magnet, and a mechanically driven all-permanent magnet configuration. In the sample region the maximum magnetic flux density was 100 mT for both setups, with a field gradient of 9 T/m and 13 T/m for the solenoid and the allpermanent magnet setup, respectively. Magnetomotive OCT imaging was performed in elastic tissue phantoms and single cells labeled with iron oxide nanoparticles. Particle-induced sub-resolution movement of the elastic samples and the single cells could successfully be detected and visualized by means of phase-resolved Doppler OCT analysis. Therefore, this method is a potential technique to enhance image contrast of specific cells in OCT.

Unsupervised sputum color image segmentation for lung cancer diagnosis based on a Hopfield neural network

NASA Astrophysics Data System (ADS)

Sammouda, Rachid; Niki, Noboru; Nishitani, Hiroshi; Nakamura, S.; Mori, Shinichiro

1997-04-01

The paper presents a method for automatic segmentation of sputum cells with color images, to develop an efficient algorithm for lung cancer diagnosis based on a Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of squared errors, and the second term a temporary noise added to the network as an excitation to escape certain local minima with the result of being closer to the global minimum. To increase the accuracy in segmenting the regions of interest, a preclassification technique is used to extract the sputum cell regions within the color image and remove those of the debris cells. The former is then given with the raw image to the input of Hopfield neural network to make a crisp segmentation by assigning each pixel to label such as background, cytoplasm, and nucleus. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.

Passive synthetic aperture radar imaging of ground moving targets

NASA Astrophysics Data System (ADS)

Wacks, Steven; Yazici, Birsen

2012-05-01

In this paper we present a method for imaging ground moving targets using passive synthetic aperture radar. A passive radar imaging system uses small, mobile receivers that do not radiate any energy. For these reasons, passive imaging systems result in signicant cost, manufacturing, and stealth advantages. The received signals are obtained by multiple airborne receivers collecting scattered waves due to illuminating sources of opportunity such as commercial television, radio, and cell phone towers. We describe a novel forward model and a corresponding ltered-backprojection type image reconstruction method combined with entropy optimization. Our method determines the location and velocity of multiple targets moving at dierent velocities. Furthermore, it can accommodate arbitrary imaging geometries. we present numerical simulations to verify the imaging method.

Fluorometric imaging methods for palladium and platinum and the use of palladium for imaging biomolecules.

PubMed

Tracey, Matthew P; Pham, Dianne; Koide, Kazunori

2015-07-21

Neither palladium nor platinum is an endogenous biological metal. Imaging palladium in biological samples, however, is becoming increasingly important because bioorthogonal organometallic chemistry involves palladium catalysis. In addition to being an imaging target, palladium has been used to fluorometrically image biomolecules. In these cases, palladium species are used as imaging-enabling reagents. This review article discusses these fluorometric methods. Platinum-based drugs are widely used as anticancer drugs, yet their mechanism of action remains largely unknown. We discuss fluorometric methods for imaging or quantifying platinum in cells or biofluids. These methods include the use of chemosensors to directly detect platinum, fluorescently tagging platinum-based drugs, and utilizing post-labeling to elucidate distribution and mode of action.

A Fusion Algorithm for GFP Image and Phase Contrast Image of Arabidopsis Cell Based on SFL-Contourlet Transform

PubMed Central

Feng, Peng; Wang, Jing; Wei, Biao; Mi, Deling

2013-01-01

A hybrid multiscale and multilevel image fusion algorithm for green fluorescent protein (GFP) image and phase contrast image of Arabidopsis cell is proposed in this paper. Combining intensity-hue-saturation (IHS) transform and sharp frequency localization Contourlet transform (SFL-CT), this algorithm uses different fusion strategies for different detailed subbands, which include neighborhood consistency measurement (NCM) that can adaptively find balance between color background and gray structure. Also two kinds of neighborhood classes based on empirical model are taken into consideration. Visual information fidelity (VIF) as an objective criterion is introduced to evaluate the fusion image. The experimental results of 117 groups of Arabidopsis cell image from John Innes Center show that the new algorithm cannot only make the details of original images well preserved but also improve the visibility of the fusion image, which shows the superiority of the novel method to traditional ones. PMID:23476716

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

Cell nuclei and cytoplasm joint segmentation using the sliding band filter.

PubMed

Quelhas, Pedro; Marcuzzo, Monica; Mendonça, Ana Maria; Campilho, Aurélio

2010-08-01

Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells' locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter's design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells' nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95%/69% and 82%/90% for nuclei and cytoplasm detection respectively and an overall accuracy of 76%.

Bacterial cell identification in differential interference contrast microscopy images.

PubMed

Obara, Boguslaw; Roberts, Mark A J; Armitage, Judith P; Grau, Vicente

2013-04-23

Microscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding. We have developed and evaluated a high-throughput image analysis and processing approach to detect and characterize bacterial cells and chemotaxis proteins. Its performance was evaluated using differential interference contrast and fluorescence microscopy images of Rhodobacter sphaeroides. Results demonstrate that the proposed approach provides a fast and robust method for detection and analysis of spatial relationship between bacterial cells and their chemotaxis proteins.

Segmentation of nuclear images in automated cervical cancer screening

NASA Astrophysics Data System (ADS)

Dadeshidze, Vladimir; Olsson, Lars J.; Domanik, Richard A.

1995-08-01

This paper describes an efficient method of segmenting cell nuclei from complex scenes based upon the use of adaptive region growing in conjuction with nucleus-specific filters. Results of segmenting potentially abnormal (cancer or neoplastic) cell nuclei in Papanicolaou smears from 0.8 square micrometers resolution images are also presented.

Analytical validation of quantitative immunohistochemical assays of tumor infiltrating lymphocyte biomarkers.

PubMed

Singh, U; Cui, Y; Dimaano, N; Mehta, S; Pruitt, S K; Yearley, J; Laterza, O F; Juco, J W; Dogdas, B

2018-06-04

Tumor infiltrating lymphocytes (TIL), especially T-cells, have both prognostic and therapeutic applications. The presence of CD8+ effector T-cells and the ratio of CD8+ cells to FOXP3+ regulatory T-cells have been used as biomarkers of disease prognosis to predict response to various immunotherapies. Blocking the interaction between inhibitory receptors on T-cells and their ligands with therapeutic antibodies including atezolizumab, nivolumab, pembrolizumab and tremelimumab increases the immune response against cancer cells and has shown significant improvement in clinical benefits and survival in several different tumor types. The improved clinical outcome is presumed to be associated with a higher tumor infiltration; therefore, it is thought that more accurate methods for measuring the amount of TIL could assist prognosis and predict treatment response. We have developed and validated quantitative immunohistochemistry (IHC) assays for CD3, CD8 and FOXP3 for immunophenotyping T-lymphocytes in tumor tissue. Various types of formalin fixed, paraffin embedded (FFPE) tumor tissues were immunolabeled with anti-CD3, anti-CD8 and anti-FOXP3 antibodies using an IHC autostainer. The tumor area of stained tissues, including the invasive margin of the tumor, was scored by a pathologist (visual scoring) and by computer-based quantitative image analysis. Two image analysis scores were obtained for the staining of each biomarker: the percent positive cells in the tumor area and positive cells/mm 2 tumor area. Comparison of visual vs. image analysis scoring methods using regression analysis showed high correlation and indicated that quantitative image analysis can be used to score the number of positive cells in IHC stained slides. To demonstrate that the IHC assays produce consistent results in normal daily testing, we evaluated the specificity, sensitivity and reproducibility of the IHC assays using both visual and image analysis scoring methods. We found that CD3, CD8 and FOXP3 IHC assays met the fit-for-purpose analytical acceptance validation criteria and that they can be used to support clinical studies.

Quantitative phase imaging of biological cells and tissues using singleshot white light interference microscopy and phase subtraction method for extended range of measurement

NASA Astrophysics Data System (ADS)

Mehta, Dalip Singh; Sharma, Anuradha; Dubey, Vishesh; Singh, Veena; Ahmad, Azeem

2016-03-01

We present a single-shot white light interference microscopy for the quantitative phase imaging (QPI) of biological cells and tissues. A common path white light interference microscope is developed and colorful white light interferogram is recorded by three-chip color CCD camera. The recorded white light interferogram is decomposed into the red, green and blue color wavelength component interferograms and processed it to find out the RI for different color wavelengths. The decomposed interferograms are analyzed using local model fitting (LMF)" algorithm developed for reconstructing the phase map from single interferogram. LMF is slightly off-axis interferometric QPI method which is a single-shot method that employs only a single image, so it is fast and accurate. The present method is very useful for dynamic process where path-length changes at millisecond level. From the single interferogram a wavelength-dependent quantitative phase imaging of human red blood cells (RBCs) are reconstructed and refractive index is determined. The LMF algorithm is simple to implement and is efficient in computation. The results are compared with the conventional phase shifting interferometry and Hilbert transform techniques.

Where in the Cell Are You? Probing HIV-1 Host Interactions through Advanced Imaging Techniques

PubMed Central

Dirk, Brennan S.; Van Nynatten, Logan R.; Dikeakos, Jimmy D.

2016-01-01

Viruses must continuously evolve to hijack the host cell machinery in order to successfully replicate and orchestrate key interactions that support their persistence. The type-1 human immunodeficiency virus (HIV-1) is a prime example of viral persistence within the host, having plagued the human population for decades. In recent years, advances in cellular imaging and molecular biology have aided the elucidation of key steps mediating the HIV-1 lifecycle and viral pathogenesis. Super-resolution imaging techniques such as stimulated emission depletion (STED) and photoactivation and localization microscopy (PALM) have been instrumental in studying viral assembly and release through both cell–cell transmission and cell–free viral transmission. Moreover, powerful methods such as Forster resonance energy transfer (FRET) and bimolecular fluorescence complementation (BiFC) have shed light on the protein-protein interactions HIV-1 engages within the host to hijack the cellular machinery. Specific advancements in live cell imaging in combination with the use of multicolor viral particles have become indispensable to unravelling the dynamic nature of these virus-host interactions. In the current review, we outline novel imaging methods that have been used to study the HIV-1 lifecycle and highlight advancements in the cell culture models developed to enhance our understanding of the HIV-1 lifecycle. PMID:27775563

Cell invasion in the spheroid sprouting assay: a spatial organisation analysis adaptable to cell behaviour.

PubMed

Blacher, Silvia; Erpicum, Charlotte; Lenoir, Bénédicte; Paupert, Jenny; Moraes, Gustavo; Ormenese, Sandra; Bullinger, Eric; Noel, Agnès

2014-01-01

The endothelial cell spheroid assay provides a suitable in vitro model to study (lymph) angiogenesis and test pro- and anti-(lymph) angiogenic factors or drugs. Usually, the extent of cell invasion, observed through optical microscopy, is measured. The present study proposes the spatial distribution of migrated cells as a new descriptor of the (lymph) angiogenic response. The utility of this novel method rests with its capacity to locally characterise spheroid structure, allowing not only the investigation of single and collective cell invasion but also the evolution of the spheroid core itself. Moreover, the proposed method can be applied to 2D-projected spheroid images obtained by optical microscopy, as well as to 3D images acquired by confocal microscopy. To validate the proposed methodology, endothelial cell invasion was evaluated under different experimental conditions. The results were compared with widely used global parameters. The comparison shows that our method prevents local spheroid modifications from being overlooked and leading to the possible misinterpretation of results.

Classification of Normal and Apoptotic Cells from Fluorescence Microscopy Images Using Generalized Polynomial Chaos and Level Set Function.

PubMed

Du, Yuncheng; Budman, Hector M; Duever, Thomas A

2016-06-01

Accurate automated quantitative analysis of living cells based on fluorescence microscopy images can be very useful for fast evaluation of experimental outcomes and cell culture protocols. In this work, an algorithm is developed for fast differentiation of normal and apoptotic viable Chinese hamster ovary (CHO) cells. For effective segmentation of cell images, a stochastic segmentation algorithm is developed by combining a generalized polynomial chaos expansion with a level set function-based segmentation algorithm. This approach provides a probabilistic description of the segmented cellular regions along the boundary, from which it is possible to calculate morphological changes related to apoptosis, i.e., the curvature and length of a cell's boundary. These features are then used as inputs to a support vector machine (SVM) classifier that is trained to distinguish between normal and apoptotic viable states of CHO cell images. The use of morphological features obtained from the stochastic level set segmentation of cell images in combination with the trained SVM classifier is more efficient in terms of differentiation accuracy as compared with the original deterministic level set method.

Use of an optical trap for study of host-pathogen interactions for dynamic live cell imaging.

PubMed

Tam, Jenny M; Castro, Carlos E; Heath, Robert J W; Mansour, Michael K; Cardenas, Michael L; Xavier, Ramnik J; Lang, Matthew J; Vyas, Jatin M

2011-07-28

Dynamic live cell imaging allows direct visualization of real-time interactions between cells of the immune system(1, 2); however, the lack of spatial and temporal control between the phagocytic cell and microbe has rendered focused observations into the initial interactions of host response to pathogens difficult. Historically, intercellular contact events such as phagocytosis(3) have been imaged by mixing two cell types, and then continuously scanning the field-of-view to find serendipitous intercellular contacts at the appropriate stage of interaction. The stochastic nature of these events renders this process tedious, and it is difficult to observe early or fleeting events in cell-cell contact by this approach. This method requires finding cell pairs that are on the verge of contact, and observing them until they consummate their contact, or do not. To address these limitations, we use optical trapping as a non-invasive, non-destructive, but fast and effective method to position cells in culture. Optical traps, or optical tweezers, are increasingly utilized in biological research to capture and physically manipulate cells and other micron-sized particles in three dimensions(4). Radiation pressure was first observed and applied to optical tweezer systems in 1970(5, 6), and was first used to control biological specimens in 1987(7). Since then, optical tweezers have matured into a technology to probe a variety of biological phenomena(8-13). We describe a method(14) that advances live cell imaging by integrating an optical trap with spinning disk confocal microscopy with temperature and humidity control to provide exquisite spatial and temporal control of pathogenic organisms in a physiological environment to facilitate interactions with host cells, as determined by the operator. Live, pathogenic organisms like Candida albicans and Aspergillus fumigatus, which can cause potentially lethal, invasive infections in immunocompromised individuals(15, 16) (e.g. AIDS, chemotherapy, and organ transplantation patients), were optically trapped using non-destructive laser intensities and moved adjacent to macrophages, which can phagocytose the pathogen. High resolution, transmitted light and fluorescence-based movies established the ability to observe early events of phagocytosis in living cells. To demonstrate the broad applicability in immunology, primary T-cells were also trapped and manipulated to form synapses with anti-CD3 coated microspheres in vivo, and time-lapse imaging of synapse formation was also obtained. By providing a method to exert fine spatial control of live pathogens with respect to immune cells, cellular interactions can be captured by fluorescence microscopy with minimal perturbation to cells and can yield powerful insight into early responses of innate and adaptive immunity.

Molecular-genetic imaging based on reporter gene expression.

PubMed

Kang, Joo Hyun; Chung, June-Key

2008-06-01

Molecular imaging includes proteomic, metabolic, cellular biologic process, and genetic imaging. In a narrow sense, molecular imaging means genetic imaging and can be called molecular-genetic imaging. Imaging reporter genes play a leading role in molecular-genetic imaging. There are 3 major methods of molecular-genetic imaging, based on optical, MRI, and nuclear medicine modalities. For each of these modalities, various reporter genes and probes have been developed, and these have resulted in successful transitions from bench to bedside applications. Each of these imaging modalities has its unique advantages and disadvantages. Fluorescent and bioluminescent optical imaging modalities are simple, less expensive, more convenient, and more user friendly than other imaging modalities. Another advantage, especially of bioluminescence imaging, is its ability to detect low levels of gene expression. MRI has the advantage of high spatial resolution, whereas nuclear medicine methods are highly sensitive and allow data from small-animal imaging studies to be translated to clinical practice. Moreover, multimodality imaging reporter genes will allow us to choose the imaging technologies that are most appropriate for the biologic problem at hand and facilitate the clinical application of reporter gene technologies. Reporter genes can be used to visualize the levels of expression of particular exogenous and endogenous genes and several intracellular biologic phenomena, including specific signal transduction pathways, nuclear receptor activities, and protein-protein interactions. This technique provides a straightforward means of monitoring tumor mass and can visualize the in vivo distributions of target cells, such as immune cells and stem cells. Molecular imaging has gradually evolved into an important tool for drug discovery and development, and transgenic mice with an imaging reporter gene can be useful during drug and stem cell therapy development. Moreover, instrumentation improvements, the identification of novel targets and genes, and imaging probe developments suggest that molecular-genetic imaging is likely to play an increasingly important role in the diagnosis and therapy of cancer.

Quantitative Analysis of Rat Dorsal Root Ganglion Neurons Cultured on Microelectrode Arrays Based on Fluorescence Microscopy Image Processing.

PubMed

Mari, João Fernando; Saito, José Hiroki; Neves, Amanda Ferreira; Lotufo, Celina Monteiro da Cruz; Destro-Filho, João-Batista; Nicoletti, Maria do Carmo

2015-12-01

Microelectrode Arrays (MEA) are devices for long term electrophysiological recording of extracellular spontaneous or evocated activities on in vitro neuron culture. This work proposes and develops a framework for quantitative and morphological analysis of neuron cultures on MEAs, by processing their corresponding images, acquired by fluorescence microscopy. The neurons are segmented from the fluorescence channel images using a combination of segmentation by thresholding, watershed transform, and object classification. The positioning of microelectrodes is obtained from the transmitted light channel images using the circular Hough transform. The proposed method was applied to images of dissociated culture of rat dorsal root ganglion (DRG) neuronal cells. The morphological and topological quantitative analysis carried out produced information regarding the state of culture, such as population count, neuron-to-neuron and neuron-to-microelectrode distances, soma morphologies, neuron sizes, neuron and microelectrode spatial distributions. Most of the analysis of microscopy images taken from neuronal cultures on MEA only consider simple qualitative analysis. Also, the proposed framework aims to standardize the image processing and to compute quantitative useful measures for integrated image-signal studies and further computational simulations. As results show, the implemented microelectrode identification method is robust and so are the implemented neuron segmentation and classification one (with a correct segmentation rate up to 84%). The quantitative information retrieved by the method is highly relevant to assist the integrated signal-image study of recorded electrophysiological signals as well as the physical aspects of the neuron culture on MEA. Although the experiments deal with DRG cell images, cortical and hippocampal cell images could also be processed with small adjustments in the image processing parameter estimation.

Three-Dimensional, Live-Cell Imaging of Chromatin Dynamics in Plant Nuclei Using Chromatin Tagging Systems.

PubMed

Hirakawa, Takeshi; Matsunaga, Sachihiro

2016-01-01

In plants, chromatin dynamics spatiotemporally change in response to various environmental stimuli. However, little is known about chromatin dynamics in the nuclei of plants. Here, we introduce a three-dimensional, live-cell imaging method that can monitor chromatin dynamics in nuclei via a chromatin tagging system that can visualize specific genomic loci in living plant cells. The chromatin tagging system is based on a bacterial operator/repressor system in which the repressor is fused to fluorescent proteins. A recent refinement of promoters for the system solved the problem of gene silencing and abnormal pairing frequencies between operators. Using this system, we can detect the spatiotemporal dynamics of two homologous loci as two fluorescent signals within a nucleus and monitor the distance between homologous loci. These live-cell imaging methods will provide new insights into genome organization, development processes, and subnuclear responses to environmental stimuli in plants.

High-speed video capillaroscopy method for imaging and evaluation of moving red blood cells

NASA Astrophysics Data System (ADS)

Gurov, Igor; Volkov, Mikhail; Margaryants, Nikita; Pimenov, Aleksei; Potemkin, Andrey

2018-05-01

The video capillaroscopy system with high image recording rate to resolve moving red blood cells with velocity up to 5 mm/s into a capillary is considered. Proposed procedures of the recorded video sequence processing allow evaluating spatial capillary area, capillary diameter and central line with high accuracy and reliability independently on properties of individual capillary. Two-dimensional inter frame procedure is applied to find lateral shift of neighbor images in the blood flow area with moving red blood cells and to measure directly the blood flow velocity along a capillary central line. The developed method opens new opportunities for biomedical diagnostics, particularly, due to long-time continuous monitoring of red blood cells velocity into capillary. Spatio-temporal representation of capillary blood flow is considered. Experimental results of direct measurement of blood flow velocity into separate capillary as well as capillary net are presented and discussed.

Effects of Image Compression on Automatic Count of Immunohistochemically Stained Nuclei in Digital Images

PubMed Central

López, Carlos; Lejeune, Marylène; Escrivà, Patricia; Bosch, Ramón; Salvadó, Maria Teresa; Pons, Lluis E.; Baucells, Jordi; Cugat, Xavier; Álvaro, Tomás; Jaén, Joaquín

2008-01-01

This study investigates the effects of digital image compression on automatic quantification of immunohistochemical nuclear markers. We examined 188 images with a previously validated computer-assisted analysis system. A first group was composed of 47 images captured in TIFF format, and other three contained the same images converted from TIFF to JPEG format with 3×, 23× and 46× compression. Counts of TIFF format images were compared with the other three groups. Overall, differences in the count of the images increased with the percentage of compression. Low-complexity images (≤100 cells/field, without clusters or with small-area clusters) had small differences (<5 cells/field in 95–100% of cases) and high-complexity images showed substantial differences (<35–50 cells/field in 95–100% of cases). Compression does not compromise the accuracy of immunohistochemical nuclear marker counts obtained by computer-assisted analysis systems for digital images with low complexity and could be an efficient method for storing these images. PMID:18755997

Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes

PubMed Central

Manning, Cerys; Rattray, Magnus

2017-01-01

Multiple biological processes are driven by oscillatory gene expression at different time scales. Pulsatile dynamics are thought to be widespread, and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in expression dynamics, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. This underlies a new challenge to the experimentalist because neither intuition nor pre-existing methods work well for identifying oscillatory activity in noisy biological time series. Thus, there is an acute need for an objective statistical method for classifying whether an experimentally derived noisy time series is periodic. Here, we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of non-parametric regression with Gaussian processes. Our method can distinguish oscillatory gene expression from random fluctuations of non-oscillatory expression in single-cell time series, despite peak-to-peak variability in period and amplitude of single-cell oscillations. We show that our method outperforms the Lomb-Scargle periodogram in successfully classifying cells as oscillatory or non-oscillatory in data simulated from a simple genetic oscillator model and in experimental data. Analysis of bioluminescent live-cell imaging shows a significantly greater number of oscillatory cells when luciferase is driven by a Hes1 promoter (10/19), which has previously been reported to oscillate, than the constitutive MoMuLV 5’ LTR (MMLV) promoter (0/25). The method can be applied to data from any gene network to both quantify the proportion of oscillating cells within a population and to measure the period and quality of oscillations. It is publicly available as a MATLAB package. PMID:28493880

A simple method for multiday imaging of slice cultures.

PubMed

Seidl, Armin H; Rubel, Edwin W

2010-01-01

The organotypic slice culture (Stoppini et al. A simple method for organotypic cultures of nervous tissue. 1991;37:173-182) has become the method of choice to answer a variety of questions in neuroscience. For many experiments, however, it would be beneficial to image or manipulate a slice culture repeatedly, for example, over the course of many days. We prepared organotypic slice cultures of the auditory brainstem of P3 and P4 mice and kept them in vitro for up to 4 weeks. Single cells in the auditory brainstem were transfected with plasmids expressing fluorescent proteins by way of electroporation (Haas et al. Single-cell electroporation for gene transfer in vivo. 2001;29:583-591). The culture was then placed in a chamber perfused with oxygenated ACSF and the labeled cell imaged with an inverted wide-field microscope repeatedly for multiple days, recording several time-points per day, before returning the slice to the incubator. We describe a simple method to image a slice culture preparation during the course of multiple days and over many continuous hours, without noticeable damage to the tissue or photobleaching. Our method uses a simple, inexpensive custom-built insulator constructed around the microscope to maintain controlled temperature and uses a perfusion chamber as used for in vitro slice recordings. (c) 2009 Wiley-Liss, Inc.

Imaging human retinal pigment epithelium cells using adaptive optics optical coherence tomography

NASA Astrophysics Data System (ADS)

Liu, Zhuolin; Kocaoglu, Omer P.; Turner, Timothy L.; Miller, Donald T.

2016-03-01

Retinal pigment epithelium (RPE) cells are vital to health of the outer retina, but are often compromised in ageing and major ocular diseases that lead to blindness. Early manifestation of RPE disruption occurs at the cellular level, and while biomarkers at this scale hold considerable promise, RPE cells have proven extremely challenging to image in the living human eye. We present a novel method based on optical coherence tomography (OCT) equipped with adaptive optics (AO) that overcomes the associated technical obstacles. The method takes advantage of the 3D resolution of AO-OCT, but more critically sub-cellular segmentation and registration that permit organelle motility to be used as a novel contrast mechanism. With this method, we successfully visualized RPE cells and characterized their 3D reflectance profile in every subject and retinal location (3° and 7° temporal to the fovea) imaged to date. We have quantified RPE packing geometry in terms of cell density, cone-to-RPE ratio, and number of nearest neighbors using Voronoi and power spectra analyses. RPE cell density (cells/mm2) showed no significant difference between 3° (4,892+/-691) and 7° (4,780+/-354). In contrast, cone-to- RPE ratio was significantly higher at 3° (3.88+/-0.52:1) than 7° (2.31+/- 0.23:1). Voronoi analysis also showed most RPE cells have six nearest neighbors, which was significantly larger than the next two most prevalent associations: five and seven. Averaged across the five subjects, prevalence of cells with six neighbors was 51.4+/-3.58% at 3°, and 54.58+/-3.01% at 7°. These results are consistent with histology and in vivo studies using other imaging modalities.

Target-cancer cell specific activatable fluorescence imaging Probes: Rational Design and in vivo Applications

PubMed Central

Kobayashi, Hisataka; Choyke, Peter L.

2010-01-01

CONSPECTUS Conventional imaging methods, such as angiography, computed tomography, magnetic resonance imaging and radionuclide imaging, rely on contrast agents (iodine, gadolinium, radioisotopes) that are “always on”. While these agents have proven clinically useful, they are not sufficiently sensitive because of the inadequate target to background ratio. A unique aspect of optical imaging is that fluorescence probes can be designed to be activatable, i.e. only “turned on” under certain conditions. These probes can be designed to emit signal only after binding a target tissue, greatly increasing sensitivity and specificity in the detection of disease. There are two basic types of activatable fluorescence probes; 1) conventional enzymatically activatable probes, which exist in the quenched state until activated by enzymatic cleavage mostly outside of the cells, and 2) newly designed target-cell specific activatable probes, which are quenched until activated in targeted cells by endolysosomal processing that results when the probe binds specific cell-surface receptors and is subsequently internalized. Herein, we present a review of the rational design and in vivo applications of target-cell specific activatable probes. Designing these probes based on their photo-chemical (e.g. activation strategy), pharmacological (e.g. biodistribution), and biological (e.g. target specificity) properties has recently allowed the rational design and synthesis of target-cell specific activatable fluorescence imaging probes, which can be conjugated to a wide variety of targeting molecules. Several different photo-chemical mechanisms have been utilized, each of which offers a unique capability for probe design. These include: self-quenching, homo- and hetero-fluorescence resonance energy transfer (FRET), H-dimer formation and photon-induced electron transfer (PeT). In addition, the repertoire is further expanded by the option for reversibility or irreversibility of the signal emitted using the aforementioned mechanisms. Given the wide range of photochemical mechanisms and properties, target-cell specific activatable probes possess considerable flexibility and can be adapted to specific diagnostic needs. Herein, we summarize the chemical, pharmacological, and biological basis of target-cell specific activatable imaging probes and discuss methods to successfully design such target-cell specific activatable probes for in vivo cancer imaging. PMID:21062101

Human induced pluripotent stem cells labeled with fluorescent magnetic nanoparticles for targeted imaging and hyperthermia therapy for gastric cancer.

PubMed

Li, Chao; Ruan, Jing; Yang, Meng; Pan, Fei; Gao, Guo; Qu, Su; Shen, You-Lan; Dang, Yong-Jun; Wang, Kan; Jin, Wei-Lin; Cui, Da-Xiang

2015-09-01

Human induced pluripotent stem (iPS) cells exhibit great potential for generating functional human cells for medical therapies. In this paper, we report for use of human iPS cells labeled with fluorescent magnetic nanoparticles (FMNPs) for targeted imaging and synergistic therapy of gastric cancer cells in vivo. Human iPS cells were prepared and cultured for 72 h. The culture medium was collected, and then was co-incubated with MGC803 cells. Cell viability was analyzed by the MTT method. FMNP-labeled human iPS cells were prepared and injected into gastric cancer-bearing nude mice. The mouse model was observed using a small-animal imaging system. The nude mice were irradiated under an external alternating magnetic field and evaluated using an infrared thermal mapping instrument. Tumor sizes were measured weekly. iPS cells and the collected culture medium inhibited the growth of MGC803 cells. FMNP-labeled human iPS cells targeted and imaged gastric cancer cells in vivo, as well as inhibited cancer growth in vivo through the external magnetic field. FMNP-labeled human iPS cells exhibit considerable potential in applications such as targeted dual-mode imaging and synergistic therapy for early gastric cancer.

Quantitative Chemical Imaging and Unsupervised Analysis Using Hyperspectral Coherent Anti-Stokes Raman Scattering Microscopy

PubMed Central

2013-01-01

In this work, we report a method to acquire and analyze hyperspectral coherent anti-Stokes Raman scattering (CARS) microscopy images of organic materials and biological samples resulting in an unbiased quantitative chemical analysis. The method employs singular value decomposition on the square root of the CARS intensity, providing an automatic determination of the components above noise, which are retained. Complex CARS susceptibility spectra, which are linear in the chemical composition, are retrieved from the CARS intensity spectra using the causality of the susceptibility by two methods, and their performance is evaluated by comparison with Raman spectra. We use non-negative matrix factorization applied to the imaginary part and the nonresonant real part of the susceptibility with an additional concentration constraint to obtain absolute susceptibility spectra of independently varying chemical components and their absolute concentration. We demonstrate the ability of the method to provide quantitative chemical analysis on known lipid mixtures. We then show the relevance of the method by imaging lipid-rich stem-cell-derived mouse adipocytes as well as differentiated embryonic stem cells with a low density of lipids. We retrieve and visualize the most significant chemical components with spectra given by water, lipid, and proteins segmenting the image into the cell surrounding, lipid droplets, cytosol, and the nucleus, and we reveal the chemical structure of the cells, with details visualized by the projection of the chemical contrast into a few relevant channels. PMID:24099603

Methods on observation of fluorescence micro-imaging for microalgae

NASA Astrophysics Data System (ADS)

Ou, Lin; Zhuang, Hui-ru; Chen, Rong; Lei, Jin-pin; Liao, Xiao-hua; Lin, Wen-suo

2007-11-01

Objective: Auto-fluorescence micro-imaging of microalgae are observed by using of laser scanning confocal microscopy (LSCM) and fluorescence microscopy, so as to investigate the effect of auto fluorescence alteration on growth of irradiated microalgae irradiated, meanwhile, the method of microalgae cells stained also to be studied. Methods: Platymonas subcordiformis, Phaeodactylum tricormutum and Isochyrsis zhanjiangensis cells are stained with acridine orange, and observed by fluorescence microscopy; the three types microalgae mentioned above are irradiated by Nd:YAP laser with 10w at 1341nm, irradiating time:12s, 30s, 35s and 55s, than to be cultured 6 days, and the auto fluorescence images and fluorescence spectra of algae cells are obtained by LSCM on lambda scan mode, at excitation 488nm (Ar + laser). Results: It is showed that the shapes and the structural features of microalgae cells stained can be seen clearly, and the cytoplasm and nucleus also can be observed. The chloroplasts in cell is bigger on promoting effects, conversely, it is to be mutilated, deformation and shrink. Contrast to the CK, the peak positions of fluorescence of algae cells irradiated is similar to the whole while the peak light intensity alters. On irradiation of promoting dose, however, the auto fluorescence intensity is enhanced more than control. Conclusions: The method of cell stained can be used to observed genetic material in microalgae. There are obvious effects for laser irradiating to chloroplasts in cells, the bigger chloroplasts the greater fluorescence intensity. Physiological incentive effects of microalgae irradiated can be given expression on fluorescence characteristics and fluorescence intensity alteration of cells.

Revealing 3D Ultrastructure and Morphology of Stem Cell Spheroids by Electron Microscopy.

PubMed

Jaros, Josef; Petrov, Michal; Tesarova, Marketa; Hampl, Ales

2017-01-01

Cell culture methods have been developed in efforts to produce biologically relevant systems for developmental and disease modeling, and appropriate analytical tools are essential. Knowledge of ultrastructural characteristics represents the basis to reveal in situ the cellular morphology, cell-cell interactions, organelle distribution, niches in which cells reside, and many more. The traditional method for 3D visualization of ultrastructural components, serial sectioning using transmission electron microscopy (TEM), is very labor-intensive due to contentious TEM slice preparation and subsequent image processing of the whole collection. In this chapter, we present serial block-face scanning electron microscopy, together with complex methodology for spheroid formation, contrasting of cellular compartments, image processing, and 3D visualization. The described technique is effective for detailed morphological analysis of stem cell spheroids, organoids, as well as organotypic cell cultures.

Label-free imaging of metabolism and oxidative stress in human induced pluripotent stem cell-derived cardiomyocytes

PubMed Central

Datta, Rupsa; Heylman, Christopher; George, Steven C.; Gratton, Enrico

2016-01-01

In this work we demonstrate a label-free optical imaging technique to assess metabolic status and oxidative stress in human induced pluripotent stem cell-derived cardiomyocytes by two-photon fluorescence lifetime imaging of endogenous fluorophores. Our results show the sensitivity of this method to detect shifts in metabolism and oxidative stress in the cardiomyocytes upon pathological stimuli of hypoxia and cardiotoxic drugs. This non-invasive imaging technique could prove beneficial for drug development and screening, especially for in vitro cardiac models created from stem cell-derived cardiomyocytes and to study the pathogenesis of cardiac diseases and therapy. PMID:27231614

Methods for imaging Shewanella oneidensis MR-1 nanofilaments.

PubMed

Ray, R; Lizewski, S; Fitzgerald, L A; Little, B; Ringeisen, B R

2010-08-01

Nanofilament production by Shewanella oneidensis MR-1 was evaluated as a function of lifestyle (planktonic vs. sessile) under aerobic and anaerobic conditions using different sample preparation techniques prior to imaging with scanning electron microscopy. Nanofilaments could be imaged on MR-1 cells grown in biofilms or planktonically under both aerobic and anaerobic batch culture conditions after fixation, critical point drying and coating with a conductive metal. Critical point drying was a requirement for imaging nanofilaments attached to planktonically grown MR-1 cells, but not for cells grown in a biofilm. Techniques described in this paper cannot be used to differentiate nanowires from pili or flagella.

Multilevel Space-Time Aggregation for Bright Field Cell Microscopy Segmentation and Tracking

PubMed Central

Inglis, Tiffany; De Sterck, Hans; Sanders, Geoffrey; Djambazian, Haig; Sladek, Robert; Sundararajan, Saravanan; Hudson, Thomas J.

2010-01-01

A multilevel aggregation method is applied to the problem of segmenting live cell bright field microscope images. The method employed is a variant of the so-called “Segmentation by Weighted Aggregation” technique, which itself is based on Algebraic Multigrid methods. The variant of the method used is described in detail, and it is explained how it is tailored to the application at hand. In particular, a new scale-invariant “saliency measure” is proposed for deciding when aggregates of pixels constitute salient segments that should not be grouped further. It is shown how segmentation based on multilevel intensity similarity alone does not lead to satisfactory results for bright field cells. However, the addition of multilevel intensity variance (as a measure of texture) to the feature vector of each aggregate leads to correct cell segmentation. Preliminary results are presented for applying the multilevel aggregation algorithm in space time to temporal sequences of microscope images, with the goal of obtaining space-time segments (“object tunnels”) that track individual cells. The advantages and drawbacks of the space-time aggregation approach for segmentation and tracking of live cells in sequences of bright field microscope images are presented, along with a discussion on how this approach may be used in the future work as a building block in a complete and robust segmentation and tracking system. PMID:20467468

Multi-classification of cell deformation based on object alignment and run length statistic.

PubMed

Li, Heng; Liu, Zhiwen; An, Xing; Shi, Yonggang

2014-01-01

Cellular morphology is widely applied in digital pathology and is essential for improving our understanding of the basic physiological processes of organisms. One of the main issues of application is to develop efficient methods for cell deformation measurement. We propose an innovative indirect approach to analyze dynamic cell morphology in image sequences. The proposed approach considers both the cellular shape change and cytoplasm variation, and takes each frame in the image sequence into account. The cell deformation is measured by the minimum energy function of object alignment, which is invariant to object pose. Then an indirect analysis strategy is employed to overcome the limitation of gradual deformation by run length statistic. We demonstrate the power of the proposed approach with one application: multi-classification of cell deformation. Experimental results show that the proposed method is sensitive to the morphology variation and performs better than standard shape representation methods.

Extracting contours of oval-shaped objects by Hough transform and minimal path algorithms

NASA Astrophysics Data System (ADS)

Tleis, Mohamed; Verbeek, Fons J.

2014-04-01

Circular and oval-like objects are very common in cell and micro biology. These objects need to be analyzed, and to that end, digitized images from the microscope are used so as to come to an automated analysis pipeline. It is essential to detect all the objects in an image as well as to extract the exact contour of each individual object. In this manner it becomes possible to perform measurements on these objects, i.e. shape and texture features. Our measurement objective is achieved by probing contour detection through dynamic programming. In this paper we describe a method that uses Hough transform and two minimal path algorithms to detect contours of (ovoid-like) objects. These algorithms are based on an existing grey-weighted distance transform and a new algorithm to extract the circular shortest path in an image. The methods are tested on an artificial dataset of a 1000 images, with an F1-score of 0.972. In a case study with yeast cells, contours from our methods were compared with another solution using Pratt's figure of merit. Results indicate that our methods were more precise based on a comparison with a ground-truth dataset. As far as yeast cells are concerned, the segmentation and measurement results enable, in future work, to retrieve information from different developmental stages of the cell using complex features.

Murine aggregation chimeras and wholemount imaging in airway stem cell biology.

PubMed

Rosewell, Ian R; Giangreco, Adam

2012-01-01

Local tissue stem cells are known to exist in mammalian lungs but their role in epithelial maintenance remains unclear. We therefore developed murine aggregation chimera and wholemount imaging techniques to assess the contribution of these cells to lung homeostasis and repair. In this chapter we provide further details regarding the generation of murine aggregation chimera mice and their subsequent use in wholemount lung imaging. We also describe methods related to the interpretation of this data that allows for quantitative assessment of airway stem cell activation versus quiescence. Using these techniques, it is possible to compare the growth and differentiation capacity of various lung epithelial cells in normal, repairing, and diseased states.

A multiplexed method for kinetic measurements of apoptosis and proliferation using live-content imaging.

PubMed

Artymovich, Katherine; Appledorn, Daniel M

2015-01-01

In vitro cell proliferation and apoptosis assays are widely used to study cancer cell biology. Commonly used methodologies are however performed at a single, user-defined endpoint. We describe a kinetic multiplex assay incorporating the CellPlayer(TM) NucLight Red reagent to measure proliferation and the CellPlayer(TM) Caspase-3/7 reagent to measure apoptosis using the two-color, live-content imaging platform, IncuCyte(TM) ZOOM. High-definition phase-contrast images provide an additional qualitative validation of cell death based on morphological characteristics. The kinetic data generated using this strategy can be used to derive informed pharmacology measurements to screen potential cancer therapeutics.

Halo-free phase contrast microscopy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Nguyen, Tan H.; Kandel, Mikhail E.; Shakir, Haadi M.; Best, Catherine; Do, Minh N.; Popescu, Gabriel

2017-02-01

The phase contrast (PC) method is one of the most impactful developments in the four-century long history of microscopy. It allows for intrinsic, nondestructive contrast of transparent specimens, such as live cells. However, PC is plagued by the halo artifact, a result of insufficient spatial coherence in the illumination field, which limits its applicability. We present a new approach for retrieving halo-free phase contrast microscopy (hfPC) images by upgrading the conventional PC microscope with an external interferometric module, which generates sufficient data for reversing the halo artifact. Measuring four independent intensity images, our approach first measures haloed phase maps of the sample. We solve for the halo-free sample transmission function by using a physical model of the image formation under partial spatial coherence. Using this halo-free sample transmission, we can numerically generate artifact-free PC images. Furthermore, this transmission can be further used to obtain quantitative information about the sample, e.g., the thickness with known refractive indices, dry mass of live cells during their cycles. We tested our hfPC method on various control samples, e.g., beads, pillars and validated its potential for biological investigation by imaging live HeLa cells, red blood cells, and neurons.

Quantifying spontaneous metastasis in a syngeneic mouse melanoma model using real time PCR.

PubMed

Deng, Wentao; McLaughlin, Sarah L; Klinke, David J

2017-08-07

Modeling metastasis in vivo with animals is a priority for both revealing mechanisms of tumor dissemination and developing therapeutic methods. While conventional intravenous injection of tumor cells provides an efficient and consistent system for studying tumor cell extravasation and colonization, studying spontaneous metastasis derived from orthotopic tumor sites has the advantage of modeling more aspects of the metastatic cascade, but is challenging as it is difficult to detect small numbers of metastatic cells. In this work, we developed an approach for quantifying spontaneous metastasis in the syngeneic mouse B16 system using real time PCR. We first transduced B16 cells with lentivirus expressing firefly luciferase Luc2 gene for bioluminescence imaging. Next, we developed a real time quantitative PCR (qPCR) method for the detection of luciferase-expressing, metastatic tumor cells in mouse lungs and other organs. To illustrate the approach, we quantified lung metastasis in both spontaneous and experimental scenarios using B16F0 and B16F10 cells in C57BL/6Ncrl and NOD-Scid Gamma (NSG) mice. We tracked B16 melanoma metastasis with both bioluminescence imaging and qPCR, which were found to be self-consistent. Using this assay, we can quantitatively detect one Luc2 positive tumor cell out of 10 4 tissue cells, which corresponds to a metastatic burden of 1.8 × 10 4 metastatic cells per whole mouse lung. More importantly, the qPCR method was at least a factor of 10 more sensitive in detecting metastatic cell dissemination and should be combined with bioluminescence imaging as a high-resolution, end-point method for final metastatic cell quantitation. Given the rapid growth of primary tumors in many mouse models, assays with improved sensitivity can provide better insight into biological mechanisms that underpin tumor metastasis.

Fast methods for analysis of neurotransmitters from single cell and monitoring their releases in central nervous system by capillary electrophoresis, fluorescence microscopy and luminescence imaging

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

Wang, Ziqiang

1999-12-10

Fast methods for separation and detection of important neurotransmitters and the releases in central nervous system (CNS) were developed. Enzyme based immunoassay combined with capillary electrophoresis was used to analyze the contents of amino acid neurotransmitters from single neuron cells. The release of amino acid neurotransmitters from neuron cultures was monitored by laser induced fluorescence imaging method. The release and signal transduction of adenosine triphosphate (ATP) in CNS was studied with sensitive luminescence imaging method. A new dual-enzyme on-column reaction method combined with capillary electrophoresis has been developed for determining the glutamate content in single cells. Detection was based onmore » monitoring the laser-induced fluorescence of the reaction product NADH, and the measured fluorescence intensity was related to the concentration of glutamate in each cell. The detection limit of glutamate is down to 10 -8 M level, which is 1 order of magnitude lower than the previously reported detection limit based on similar detection methods. The mass detection limit of a few attomoles is far superior to that of any other reports. Selectivity for glutamate is excellent over most of amino acids. The glutamate content in single human erythrocyte and baby rat brain neurons were determined with this method and results agreed well with literature values.« less

Quantitative live-cell imaging of human immunodeficiency virus (HIV-1) assembly.

PubMed

Baumgärtel, Viola; Müller, Barbara; Lamb, Don C

2012-05-01

Advances in fluorescence methodologies make it possible to investigate biological systems in unprecedented detail. Over the last few years, quantitative live-cell imaging has increasingly been used to study the dynamic interactions of viruses with cells and is expected to become even more indispensable in the future. Here, we describe different fluorescence labeling strategies that have been used to label HIV-1 for live cell imaging and the fluorescence based methods used to visualize individual aspects of virus-cell interactions. This review presents an overview of experimental methods and recent experiments that have employed quantitative microscopy in order to elucidate the dynamics of late stages in the HIV-1 replication cycle. This includes cytosolic interactions of the main structural protein, Gag, with itself and the viral RNA genome, the recruitment of Gag and RNA to the plasma membrane, virion assembly at the membrane and the recruitment of cellular proteins involved in HIV-1 release to the nascent budding site.

Anti-epidermal growth factor receptor (anti-EGFR) antibody conjugated fluorescent nanoparticles probe for breast cancer imaging

NASA Astrophysics Data System (ADS)

Hun, Xu; Zhang, Zhujun

2009-10-01

Fluorescent nanoparticles (FNs) with unique optical properties may be useful as biosensors in living cancer cell imaging and cancer targeting. In this study, anti-EGFR antibody conjugated fluorescent nanoparticles (FNs) (anti-EGFR antibody conjugated FNs) probe was used to detect breast cancer cells. FNs with excellent character such as non-toxicity and photostability were first synthesized with a simple, cost-effective and environmentally friendly modified Stőber synthesis method, and then successfully modified with anti-EGFR antibody. This kind of fluorescence probe based on the anti-EGFR antibody conjugated FNs has been used to detect breast cancer cells with fluorescence microscopy imaging technology. The experimental results demonstrate that the anti-EGFR antibody conjugated FNs can effectively recognize breast cancer cells and exhibited good sensitivity and exceptional photostability, which would provide a novel way for the diagnosis and curative effect observation of breast cancer cells and offer a new method in detecting EGFR.

Quantitative Live-Cell Imaging of Human Immunodeficiency Virus (HIV-1) Assembly

PubMed Central

Baumgärtel, Viola; Müller, Barbara; Lamb, Don C.

2012-01-01

Advances in fluorescence methodologies make it possible to investigate biological systems in unprecedented detail. Over the last few years, quantitative live-cell imaging has increasingly been used to study the dynamic interactions of viruses with cells and is expected to become even more indispensable in the future. Here, we describe different fluorescence labeling strategies that have been used to label HIV-1 for live cell imaging and the fluorescence based methods used to visualize individual aspects of virus-cell interactions. This review presents an overview of experimental methods and recent experiments that have employed quantitative microscopy in order to elucidate the dynamics of late stages in the HIV-1 replication cycle. This includes cytosolic interactions of the main structural protein, Gag, with itself and the viral RNA genome, the recruitment of Gag and RNA to the plasma membrane, virion assembly at the membrane and the recruitment of cellular proteins involved in HIV-1 release to the nascent budding site. PMID:22754649

Parallel excitation-emission multiplexed fluorescence lifetime confocal microscopy for live cell imaging

PubMed Central

Zhao, Ming; Li, Yu; Peng, Leilei

2014-01-01

We present a novel excitation-emission multiplexed fluorescence lifetime microscopy (FLIM) method that surpasses current FLIM techniques in multiplexing capability. The method employs Fourier multiplexing to simultaneously acquire confocal fluorescence lifetime images of multiple excitation wavelength and emission color combinations at 44,000 pixels/sec. The system is built with low-cost CW laser sources and standard PMTs with versatile spectral configuration, which can be implemented as an add-on to commercial confocal microscopes. The Fourier lifetime confocal method allows fast multiplexed FLIM imaging, which makes it possible to monitor multiple biological processes in live cells. The low cost and compatibility with commercial systems could also make multiplexed FLIM more accessible to biological research community. PMID:24921725

Image-based red cell counting for wild animals blood.

PubMed

Mauricio, Claudio R M; Schneider, Fabio K; Dos Santos, Leonilda Correia

2010-01-01

An image-based red blood cell (RBC) automatic counting system is presented for wild animals blood analysis. Images with 2048×1536-pixel resolution acquired on an optical microscope using Neubauer chambers are used to evaluate RBC counting for three animal species (Leopardus pardalis, Cebus apella and Nasua nasua) and the error found using the proposed method is similar to that obtained for inter observer visual counting method, i.e., around 10%. Smaller errors (e.g., 3%) can be obtained in regions with less grid artifacts. These promising results allow the use of the proposed method either as a complete automatic counting tool in laboratories for wild animal's blood analysis or as a first counting stage in a semi-automatic counting tool.

Super-Resolution Imaging Strategies for Cell Biologists Using a Spinning Disk Microscope

PubMed Central

Hosny, Neveen A.; Song, Mingying; Connelly, John T.; Ameer-Beg, Simon; Knight, Martin M.; Wheeler, Ann P.

2013-01-01

In this study we use a spinning disk confocal microscope (SD) to generate super-resolution images of multiple cellular features from any plane in the cell. We obtain super-resolution images by using stochastic intensity fluctuations of biological probes, combining Photoactivation Light-Microscopy (PALM)/Stochastic Optical Reconstruction Microscopy (STORM) methodologies. We compared different image analysis algorithms for processing super-resolution data to identify the most suitable for analysis of particular cell structures. SOFI was chosen for X and Y and was able to achieve a resolution of ca. 80 nm; however higher resolution was possible >30 nm, dependant on the super-resolution image analysis algorithm used. Our method uses low laser power and fluorescent probes which are available either commercially or through the scientific community, and therefore it is gentle enough for biological imaging. Through comparative studies with structured illumination microscopy (SIM) and widefield epifluorescence imaging we identified that our methodology was advantageous for imaging cellular structures which are not immediately at the cell-substrate interface, which include the nuclear architecture and mitochondria. We have shown that it was possible to obtain two coloured images, which highlights the potential this technique has for high-content screening, imaging of multiple epitopes and live cell imaging. PMID:24130668

A Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images.

PubMed

Khan, Adnan Mujahid; Sirinukunwattana, Korsuk; Rajpoot, Nasir

2015-09-01

Nuclear atypia scoring is a diagnostic measure commonly used to assess tumor grade of various cancers, including breast cancer. It provides a quantitative measure of deviation in visual appearance of cell nuclei from those in normal epithelial cells. In this paper, we present a novel image-level descriptor for nuclear atypia scoring in breast cancer histopathology images. The method is based on the region covariance descriptor that has recently become a popular method in various computer vision applications. The descriptor in its original form is not suitable for classification of histopathology images as cancerous histopathology images tend to possess diversely heterogeneous regions in a single field of view. Our proposed image-level descriptor, which we term as the geodesic mean of region covariance descriptors, possesses all the attractive properties of covariance descriptors lending itself to tractable geodesic-distance-based k-nearest neighbor classification using efficient kernels. The experimental results suggest that the proposed image descriptor yields high classification accuracy compared to a variety of widely used image-level descriptors.

Imaging and characterizing cells using tomography

PubMed Central

Do, Myan; Isaacson, Samuel A.; McDermott, Gerry; Le Gros, Mark A.; Larabell, Carolyn A.

2015-01-01

We can learn much about cell function by imaging and quantifying sub-cellular structures, especially if this is done non-destructively without altering said structures. Soft x-ray tomography (SXT) is a high-resolution imaging technique for visualizing cells and their interior structure in 3D. A tomogram of the cell, reconstructed from a series of 2D projection images, can be easily segmented and analyzed. SXT has a very high specimen throughput compared to other high-resolution structure imaging modalities; for example, tomographic data for reconstructing an entire eukaryotic cell is acquired in a matter of minutes. SXT visualizes cells without the need for chemical fixation, dehydration, or staining of the specimen. As a result, the SXT reconstructions are close representations of cells in their native state. SXT is applicable to most cell types. The deep penetration of soft x-rays allows cells, even mammalian cells, to be imaged without being sectioned. Image contrast in SXT is generated by the differential attenuation soft x-ray illumination as it passes through the specimen. Accordingly, each voxel in the tomographic reconstruction has a measured linear absorption coefficient (LAC) value. LAC values are quantitative and give rise to each sub-cellular component having a characteristic LAC profile, allowing organelles to be identified and segmented from the milieu of other cell contents. In this chapter, we describe the fundamentals of SXT imaging and how this technique can answer real world questions in the study of the nucleus. We also describe the development of correlative methods for the localization of specific molecules in a SXT reconstruction. The combination of fluorescence and SXT data acquired from the same specimen produces composite 3D images, rich with detailed information on the inner workings of cells. PMID:25602704

Detection of Hydroxyapatite in Calcified Cardiovascular Tissues

PubMed Central

Lee, Jae Sam; Morrisett, Joel D.; Tung, Ching-Hsuan

2012-01-01

Objective The objective of this study is to develop a method for selective detection of the calcific (hydroxyapatite) component in human aortic smooth muscle cells in vitro and in calcified cardiovascular tissues ex vivo. This method uses a novel optical molecular imaging contrast dye, Cy-HABP-19, to target calcified cells and tissues. Methods A peptide that mimics the binding affinity of osteocalcin was used to label hydroxyapatite in vitro and ex vivo. Morphological changes in vascular smooth muscle cells were evaluated at an early stage of the mineralization process induced by extrinsic stimuli, osteogenic factors and a magnetic suspension cell culture. Hydroxyapatite components were detected in monolayers of these cells in the presence of osteogenic factors and a magnetic suspension environment. Results Atherosclerotic plaque contains multiple components including lipidic, fibrotic, thrombotic, and calcific materials. Using optical imaging and the Cy-HABP-19 molecular imaging probe, we demonstrated that hydroxyapatite components could be selectively distinguished from various calcium salts in human aortic smooth muscle cells in vitro and in calcified cardiovascular tissues, carotid endarterectomy samples and aortic valves, ex vivo. Conclusion Hydroxyapatite deposits in cardiovascular tissues were selectively detected in the early stage of the calcification process using our Cy-HABP-19 probe. This new probe makes it possible to study the earliest events associated with vascular hydroxyapatite deposition at the cellular and molecular levels. This target-selective molecular imaging probe approach holds high potential for revealing early pathophysiological changes, leading to progression, regression, or stabilization of cardiovascular diseases. PMID:22877867

An evaluation of image quality and accuracy of eye bank measurement of donor cornea endothelial cell density in the Specular Microscopy Ancillary Study.

PubMed

Lass, Jonathan H; Gal, Robin L; Ruedy, Katrina J; Benetz, Beth Ann; Beck, Roy W; Baratz, Keith H; Holland, Edward J; Kalajian, Andrea; Kollman, Craig; Manning, Francis J; Mannis, Mark J; McCoy, Kristen; Montoya, Monty; Stulting, Doyle; Xing, Dongyuan

2005-03-01

The Specular Microscopy Ancillary Study was designed to examine donor corneal endothelial specular image quality, compare the central endothelial cell density determined by eye banks with the endothelial cell density determined by a central specular microscopy reading center, and evaluate donor factors that may have an impact on specular image quality and endothelial cell density accuracy. Nonrandomized comparative trial. Endothelial specular images of donor corneas assigned in the Cornea Donor Study. Certified readers assessed donor image quality (analyzable from fair to excellent vs. unanalyzable) and determined the central endothelial cell density. Independent adjudication was performed if there was a difference in the quality of grading or if the endothelial cell density varied by > or =5.0% between readers. Average reading center-determined endothelial cell density was compared with the endothelial cell density determined by each eye bank. Evaluation of image quality and accuracy of endothelial cell density. Of 688 donor endothelial images submitted by 23 eye banks, 663 (96%) were analyzable (excellent, 40 [6%]; good, 302 [44%]; fair, 321 [47%]), and 25 (4%) were unanalyzable by reading center standards. In situ retrieval and greater epithelial exposure correlated with a higher image quality grading. The eye bank-determined endothelial cell density of 434 of the 663 (65%) analyzable images were within 10% of the endothelial cell density determined by the reading center, whereas 185 (28%) were more than 10% higher and 44 (7%) were more than 10% lower. Greater variation in endothelial cell density between the eye banks and the reading center was observed with shorter time of death to preservation, presence of an epithelial defect, folds in Descemet's membrane, lower image quality, and the use of fixed-frame or center method endothelial cell density analysis. Overall, donor endothelial specular image quality and accuracy of endothelial cell density determination were good. However, the data suggest that factors that may affect image quality and contribute to variation in interpretation of the endothelial cell density should be addressed, because the donor endothelial cell density is an important parameter for assessing long-term corneal graft survival.

Quantifying phosphoric acid in high-temperature polymer electrolyte fuel cell components by X-ray tomographic microscopy.

PubMed

Eberhardt, S H; Marone, F; Stampanoni, M; Büchi, F N; Schmidt, T J

2014-11-01

Synchrotron-based X-ray tomographic microscopy is investigated for imaging the local distribution and concentration of phosphoric acid in high-temperature polymer electrolyte fuel cells. Phosphoric acid fills the pores of the macro- and microporous fuel cell components. Its concentration in the fuel cell varies over a wide range (40-100 wt% H3PO4). This renders the quantification and concentration determination challenging. The problem is solved by using propagation-based phase contrast imaging and a referencing method. Fuel cell components with known acid concentrations were used to correlate greyscale values and acid concentrations. Thus calibration curves were established for the gas diffusion layer, catalyst layer and membrane in a non-operating fuel cell. The non-destructive imaging methodology was verified by comparing image-based values for acid content and concentration in the gas diffusion layer with those from chemical analysis.

Fast Metabolic Response to Drug Intervention through Analysis on a Miniaturized, Highly Integrated Molecular Imaging System

PubMed Central

Wang, Jun; Hwang, Kiwook; Braas, Daniel; Dooraghi, Alex; Nathanson, David; Campbell, Dean O.; Gu, Yuchao; Sandberg, Troy; Mischel, Paul; Radu, Caius; Chatziioannou, Arion F.; Phelps, Michael E.; Christofk, Heather; Heath, James R.

2014-01-01

We report on a radiopharmaceutical imaging platform designed to capture the kinetics of cellular responses to drugs. Methods A portable in vitro molecular imaging system, comprised of a microchip and a beta-particle imaging camera, permits routine cell-based radioassays on small number of either suspension or adherent cells. We investigate the response kinetics of model lymphoma and glioblastoma cancer cell lines to [18F]fluorodeoxyglucose ([18F]FDG) uptake following drug exposure. Those responses are correlated with kinetic changes in the cell cycle, or with changes in receptor-tyrosine kinase signaling. Results The platform enables radioassays directly on multiple cell types, and yields results comparable to conventional approaches, but uses smaller sample sizes, permits a higher level of quantitation, and doesn’t require cell lysis. Conclusion The kinetic analysis enabled by the platform provides a rapid (~1 hour) drug screening assay. PMID:23978446

FlowCam: Quantification and Classification of Phytoplankton by Imaging Flow Cytometry.

PubMed

Poulton, Nicole J

2016-01-01

The ability to enumerate, classify, and determine biomass of phytoplankton from environmental samples is essential for determining ecosystem function and their role in the aquatic community and microbial food web. Traditional micro-phytoplankton quantification methods using microscopic techniques require preservation and are slow, tedious and very laborious. The availability of more automated imaging microscopy platforms has revolutionized the way particles and cells are detected within their natural environment. The ability to examine cells unaltered and without preservation is key to providing more accurate cell concentration estimates and overall phytoplankton biomass. The FlowCam(®) is an imaging cytometry tool that was originally developed for use in aquatic sciences and provides a more rapid and unbiased method for enumerating and classifying phytoplankton within diverse aquatic environments.

Three-dimensional label-free imaging and quantification of lipid droplets in live hepatocytes

NASA Astrophysics Data System (ADS)

Kim, Kyoohyun; Lee, Seoeun; Yoon, Jonghee; Heo, Jihan; Choi, Chulhee; Park, Yongkeun

2016-11-01

Lipid droplets (LDs) are subcellular organelles with important roles in lipid storage and metabolism and involved in various diseases including cancer, obesity, and diabetes. Conventional methods, however, have limited ability to provide quantitative information on individual LDs and have limited capability for three-dimensional (3-D) imaging of LDs in live cells especially for fast acquisition of 3-D dynamics. Here, we present an optical method based on 3-D quantitative phase imaging to measure the 3-D structural distribution and biochemical parameters (concentration and dry mass) of individual LDs in live cells without using exogenous labelling agents. The biochemical change of LDs under oleic acid treatment was quantitatively investigated, and 4-D tracking of the fast dynamics of LDs revealed the intracellular transport of LDs in live cells.

Positive contrast of SPIO-labeled cells by off-resonant reconstruction of 3D radial half-echo bSSFP.

PubMed

Diwoky, Clemens; Liebmann, Daniel; Neumayer, Bernhard; Reinisch, Andreas; Knoll, Florian; Strunk, Dirk; Stollberger, Rudolf

2015-01-01

This article describes a new acquisition and reconstruction concept for positive contrast imaging of cells labeled with superparamagnetic iron oxides (SPIOs). Overcoming the limitations of a negative contrast representation as gained with gradient echo and fully balanced steady state (bSSFP), the proposed method delivers a spatially localized contrast with high cellular sensitivity not accomplished by other positive contrast methods. Employing a 3D radial bSSFP pulse sequence with half-echo sampling, positive cellular contrast is gained by adding artificial global frequency offsets to each half-echo before image reconstruction. The new contrast regime is highlighted with numerical intravoxel simulations including the point-spread function for 3D half-echo acquisitions. Furthermore, the new method is validated on the basis of in vitro cell phantom measurements on a clinical MRI platform, where the measured contrast-to-noise ratio (CNR) of the new approach exceeds even the negative contrast of bSSFP. Finally, an in vivo proof of principle study based on a mouse model with a clear depiction of labeled cells within a subcutaneous cell islet containing a cell density as low as 7 cells/mm(3) is presented. The resultant isotropic images show robustness to motion and a high CNR, in addition to an enhanced specificity due to the positive contrast of SPIO-labeled cells. Copyright © 2014 John Wiley & Sons, Ltd.

Light microscopic image analysis system to quantify immunoreactive terminal area apposed to nerve cells.

PubMed

Wu, L C; D'Amelio, F; Fox, R A; Polyakov, I; Daunton, N G

1997-06-06

The present report describes a desktop computer-based method for the quantitative assessment of the area occupied by immunoreactive terminals in close apposition to nerve cells in relation to the perimeter of the cell soma. This method is based on Fast Fourier Transform (FFT) routines incorporated in NIH-Image public domain software. Pyramidal cells of layer V of the somatosensory cortex outlined by GABA immunolabeled terminals were chosen for our analysis. A Leitz Diaplan light microscope was employed for the visualization of the sections. A Sierra Scientific Model 4030 CCD camera was used to capture the images into a Macintosh Centris 650 computer. After preprocessing, filtering was performed on the power spectrum in the frequency domain produced by the FFT operation. An inverse FFT with filter procedure was employed to restore the images to the spatial domain. Pasting of the original image to the transformed one using a Boolean logic operation called 'AND'ing produced an image with the terminals enhanced. This procedure allowed the creation of a binary image using a well-defined threshold of 128. Thus, the terminal area appears in black against a white background. This methodology provides an objective means of measurement of area by counting the total number of pixels occupied by immunoreactive terminals in light microscopic sections in which the difficulties of labeling intensity, size, shape and numerical density of terminals are avoided.

Light microscopic image analysis system to quantify immunoreactive terminal area apposed to nerve cells

NASA Technical Reports Server (NTRS)

Wu, L. C.; D'Amelio, F.; Fox, R. A.; Polyakov, I.; Daunton, N. G.

1997-01-01

The present report describes a desktop computer-based method for the quantitative assessment of the area occupied by immunoreactive terminals in close apposition to nerve cells in relation to the perimeter of the cell soma. This method is based on Fast Fourier Transform (FFT) routines incorporated in NIH-Image public domain software. Pyramidal cells of layer V of the somatosensory cortex outlined by GABA immunolabeled terminals were chosen for our analysis. A Leitz Diaplan light microscope was employed for the visualization of the sections. A Sierra Scientific Model 4030 CCD camera was used to capture the images into a Macintosh Centris 650 computer. After preprocessing, filtering was performed on the power spectrum in the frequency domain produced by the FFT operation. An inverse FFT with filter procedure was employed to restore the images to the spatial domain. Pasting of the original image to the transformed one using a Boolean logic operation called 'AND'ing produced an image with the terminals enhanced. This procedure allowed the creation of a binary image using a well-defined threshold of 128. Thus, the terminal area appears in black against a white background. This methodology provides an objective means of measurement of area by counting the total number of pixels occupied by immunoreactive terminals in light microscopic sections in which the difficulties of labeling intensity, size, shape and numerical density of terminals are avoided.

Labeling and Imaging Mesenchymal Stem Cells with Quantum Dots

EPA Science Inventory

Mesenchymal stem cells (MSCs) are multipotent cells with the potential to differentiate into bone, cartilage, adipose and muscle cells. Adult derived MSCs are being actively investigated because of their potential to be utilized for therapeutic cell-based transplantation. Methods...

Accurate cytogenetic biodosimetry through automated dicentric chromosome curation and metaphase cell selection

PubMed Central

Wilkins, Ruth; Flegal, Farrah; Knoll, Joan H.M.; Rogan, Peter K.

2017-01-01

Accurate digital image analysis of abnormal microscopic structures relies on high quality images and on minimizing the rates of false positive (FP) and negative objects in images. Cytogenetic biodosimetry detects dicentric chromosomes (DCs) that arise from exposure to ionizing radiation, and determines radiation dose received based on DC frequency. Improvements in automated DC recognition increase the accuracy of dose estimates by reclassifying FP DCs as monocentric chromosomes or chromosome fragments. We also present image segmentation methods to rank high quality digital metaphase images and eliminate suboptimal metaphase cells. A set of chromosome morphology segmentation methods selectively filtered out FP DCs arising primarily from sister chromatid separation, chromosome fragmentation, and cellular debris. This reduced FPs by an average of 55% and was highly specific to these abnormal structures (≥97.7%) in three samples. Additional filters selectively removed images with incomplete, highly overlapped, or missing metaphase cells, or with poor overall chromosome morphologies that increased FP rates. Image selection is optimized and FP DCs are minimized by combining multiple feature based segmentation filters and a novel image sorting procedure based on the known distribution of chromosome lengths. Applying the same image segmentation filtering procedures to both calibration and test samples reduced the average dose estimation error from 0.4 Gy to <0.2 Gy, obviating the need to first manually review these images. This reliable and scalable solution enables batch processing for multiple samples of unknown dose, and meets current requirements for triage radiation biodosimetry of high quality metaphase cell preparations. PMID:29026522

Morphological feature extraction for the classification of digital images of cancerous tissues.

PubMed

Thiran, J P; Macq, B

1996-10-01

This paper presents a new method for automatic recognition of cancerous tissues from an image of a microscopic section. Based on the shape and the size analysis of the observed cells, this method provides the physician with nonsubjective numerical values for four criteria of malignancy. This automatic approach is based on mathematical morphology, and more specifically on the use of Geodesy. This technique is used first to remove the background noise from the image and then to operate a segmentation of the nuclei of the cells and an analysis of their shape, their size, and their texture. From the values of the extracted criteria, an automatic classification of the image (cancerous or not) is finally operated.

3D fluorescence anisotropy imaging using selective plane illumination microscopy.

PubMed

Hedde, Per Niklas; Ranjit, Suman; Gratton, Enrico

2015-08-24

Fluorescence anisotropy imaging is a popular method to visualize changes in organization and conformation of biomolecules within cells and tissues. In such an experiment, depolarization effects resulting from differences in orientation, proximity and rotational mobility of fluorescently labeled molecules are probed with high spatial resolution. Fluorescence anisotropy is typically imaged using laser scanning and epifluorescence-based approaches. Unfortunately, those techniques are limited in either axial resolution, image acquisition speed, or by photobleaching. In the last decade, however, selective plane illumination microscopy has emerged as the preferred choice for three-dimensional time lapse imaging combining axial sectioning capability with fast, camera-based image acquisition, and minimal light exposure. We demonstrate how selective plane illumination microscopy can be utilized for three-dimensional fluorescence anisotropy imaging of live cells. We further examined the formation of focal adhesions by three-dimensional time lapse anisotropy imaging of CHO-K1 cells expressing an EGFP-paxillin fusion protein.

Development of an ultralow-light-level luminescence image analysis system for dynamic measurements of transcriptional activity in living and migrating cells.

PubMed

Maire, E; Lelièvre, E; Brau, D; Lyons, A; Woodward, M; Fafeur, V; Vandenbunder, B

2000-04-10

We have developed an approach to study in single living epithelial cells both cell migration and transcriptional activation, which was evidenced by the detection of luminescence emission from cells transfected with luciferase reporter vectors. The image acquisition chain consists of an epifluorescence inverted microscope, connected to an ultralow-light-level photon-counting camera and an image-acquisition card associated to specialized image analysis software running on a PC computer. Using a simple method based on a thin calibrated light source, the image acquisition chain has been optimized following comparisons of the performance of microscopy objectives and photon-counting cameras designed to observe luminescence. This setup allows us to measure by image analysis the luminescent light emitted by individual cells stably expressing a luciferase reporter vector. The sensitivity of the camera was adjusted to a high value, which required the use of a segmentation algorithm to eliminate the background noise. Following mathematical morphology treatments, kinetic changes of luminescent sources were analyzed and then correlated with the distance and speed of migration. Our results highlight the usefulness of our image acquisition chain and mathematical morphology software to quantify the kinetics of luminescence changes in migrating cells.

Atomic force microscopy as a tool for the investigation of living cells.

PubMed

Morkvėnaitė-Vilkončienė, Inga; Ramanavičienė, Almira; Ramanavičius, Arūnas

2013-01-01

Atomic force microscopy is a valuable and useful tool for the imaging and investigation of living cells in their natural environment at high resolution. Procedures applied to living cell preparation before measurements should be adapted individually for different kinds of cells and for the desired measurement technique. Different ways of cell immobilization, such as chemical fixation on the surface, entrapment in the pores of a membrane, or growing them directly on glass cover slips or on plastic substrates, result in the distortion or appearance of artifacts in atomic force microscopy images. Cell fixation allows the multiple use of samples and storage for a prolonged period; it also increases the resolution of imaging. Different atomic force microscopy modes are used for the imaging and analysis of living cells. The contact mode is the best for cell imaging because of high resolution, but it is usually based on the following: (i) image formation at low interaction force, (ii) low scanning speed, and (iii) usage of "soft," low resolution cantilevers. The tapping mode allows a cell to behave like a very solid material, and destructive shear forces are minimized, but imaging in liquid is difficult. The force spectroscopy mode is used for measuring the mechanical properties of cells; however, obtained results strongly depend on the cell fixation method. In this paper, the application of 3 atomic force microscopy modes including (i) contact, (ii) tapping, and (iii) force spectroscopy for the investigation of cells is described. The possibilities of cell preparation for the measurements, imaging, and determination of mechanical properties of cells are provided. The applicability of atomic force microscopy to diagnostics and other biomedical purposes is discussed.

Nondestructive chemical imaging of wood at the micro-scale: advanced technology to complement macro-scale evaluations

Treesearch

Barbara L. Illman; Julia Sedlmair; Miriam Unger; Carol Hirschmugl

2013-01-01

Chemical images help understanding of wood properties, durability, and cell wall deconstruction for conversion of lignocellulose to biofuels, nanocellulose and other value added chemicals in forest biorefineries. We describe here a new method for nondestructive chemical imaging of wood and wood-based materials at the micro-scale to complement macro-scale methods based...

Segmentation and analysis of mouse pituitary cells with graphic user interface (GUI)

NASA Astrophysics Data System (ADS)

González, Erika; Medina, Lucía.; Hautefeuille, Mathieu; Fiordelisio, Tatiana

2018-02-01

In this work we present a method to perform pituitary cell segmentation in image stacks acquired by fluorescence microscopy from pituitary slice preparations. Although there exist many procedures developed to achieve cell segmentation tasks, they are generally based on the edge detection and require high resolution images. However in the biological preparations that we worked on, the cells are not well defined as experts identify their intracellular calcium activity due to fluorescence intensity changes in different regions over time. This intensity changes were associated with time series over regions, and because they present a particular behavior they were used into a classification procedure in order to perform cell segmentation. Two logistic regression classifiers were implemented for the time series classification task using as features the area under the curve and skewness in the first classifier and skewness and kurtosis in the second classifier. Once we have found both decision boundaries in two different feature spaces by training using 120 time series, the decision boundaries were tested over 12 image stacks through a python graphical user interface (GUI), generating binary images where white pixels correspond to cells and the black ones to background. Results show that area-skewness classifier reduces the time an expert dedicates in locating cells by up to 75% in some stacks versus a 92% for the kurtosis-skewness classifier, this evaluated on the number of regions the method found. Due to the promising results, we expect that this method will be improved adding more relevant features to the classifier.

Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning.

PubMed

Feizi, Alborz; Zhang, Yibo; Greenbaum, Alon; Guziak, Alex; Luong, Michelle; Chan, Raymond Yan Lok; Berg, Brandon; Ozkan, Haydar; Luo, Wei; Wu, Michael; Wu, Yichen; Ozcan, Aydogan

2016-11-01

Monitoring yeast cell viability and concentration is important in brewing, baking and biofuel production. However, existing methods of measuring viability and concentration are relatively bulky, tedious and expensive. Here we demonstrate a compact and cost-effective automatic yeast analysis platform (AYAP), which can rapidly measure cell concentration and viability. AYAP is based on digital in-line holography and on-chip microscopy and rapidly images a large field-of-view of 22.5 mm 2 . This lens-free microscope weighs 70 g and utilizes a partially-coherent illumination source and an opto-electronic image sensor chip. A touch-screen user interface based on a tablet-PC is developed to reconstruct the holographic shadows captured by the image sensor chip and use a support vector machine (SVM) model to automatically classify live and dead cells in a yeast sample stained with methylene blue. In order to quantify its accuracy, we varied the viability and concentration of the cells and compared AYAP's performance with a fluorescence exclusion staining based gold-standard using regression analysis. The results agree very well with this gold-standard method and no significant difference was observed between the two methods within a concentration range of 1.4 × 10 5 to 1.4 × 10 6 cells per mL, providing a dynamic range suitable for various applications. This lensfree computational imaging technology that is coupled with machine learning algorithms would be useful for cost-effective and rapid quantification of cell viability and density even in field and resource-poor settings.

Multispectral Imaging Broadens Cellular Analysis

NASA Technical Reports Server (NTRS)

2007-01-01

Amnis Corporation, a Seattle-based biotechnology company, developed ImageStream to produce sensitive fluorescence images of cells in flow. The company responded to an SBIR solicitation from Ames Research Center, and proposed to evaluate several methods of extending the depth of field for its ImageStream system and implement the best as an upgrade to its commercial products. This would allow users to view whole cells at the same time, rather than just one section of each cell. Through Phase I and II SBIR contracts, Ames provided Amnis the funding the company needed to develop this extended functionality. For NASA, the resulting high-speed image flow cytometry process made its way into Medusa, a life-detection instrument built to collect, store, and analyze sample organisms from erupting hydrothermal vents, and has the potential to benefit space flight health monitoring. On the commercial end, Amnis has implemented the process in ImageStream, combining high-resolution microscopy and flow cytometry in a single instrument, giving researchers the power to conduct quantitative analyses of individual cells and cell populations at the same time, in the same experiment. ImageStream is also built for many other applications, including cell signaling and pathway analysis; classification and characterization of peripheral blood mononuclear cell populations; quantitative morphology; apoptosis (cell death) assays; gene expression analysis; analysis of cell conjugates; molecular distribution; and receptor mapping and distribution.

Reprint of Solution of Ambrosio-Tortorelli model for image segmentation by generalized relaxation method

NASA Astrophysics Data System (ADS)

D'Ambra, Pasqua; Tartaglione, Gaetano

2015-04-01

Image segmentation addresses the problem to partition a given image into its constituent objects and then to identify the boundaries of the objects. This problem can be formulated in terms of a variational model aimed to find optimal approximations of a bounded function by piecewise-smooth functions, minimizing a given functional. The corresponding Euler-Lagrange equations are a set of two coupled elliptic partial differential equations with varying coefficients. Numerical solution of the above system often relies on alternating minimization techniques involving descent methods coupled with explicit or semi-implicit finite-difference discretization schemes, which are slowly convergent and poorly scalable with respect to image size. In this work we focus on generalized relaxation methods also coupled with multigrid linear solvers, when a finite-difference discretization is applied to the Euler-Lagrange equations of Ambrosio-Tortorelli model. We show that non-linear Gauss-Seidel, accelerated by inner linear iterations, is an effective method for large-scale image analysis as those arising from high-throughput screening platforms for stem cells targeted differentiation, where one of the main goal is segmentation of thousand of images to analyze cell colonies morphology.

Solution of Ambrosio-Tortorelli model for image segmentation by generalized relaxation method

NASA Astrophysics Data System (ADS)

D'Ambra, Pasqua; Tartaglione, Gaetano

2015-03-01

Image segmentation addresses the problem to partition a given image into its constituent objects and then to identify the boundaries of the objects. This problem can be formulated in terms of a variational model aimed to find optimal approximations of a bounded function by piecewise-smooth functions, minimizing a given functional. The corresponding Euler-Lagrange equations are a set of two coupled elliptic partial differential equations with varying coefficients. Numerical solution of the above system often relies on alternating minimization techniques involving descent methods coupled with explicit or semi-implicit finite-difference discretization schemes, which are slowly convergent and poorly scalable with respect to image size. In this work we focus on generalized relaxation methods also coupled with multigrid linear solvers, when a finite-difference discretization is applied to the Euler-Lagrange equations of Ambrosio-Tortorelli model. We show that non-linear Gauss-Seidel, accelerated by inner linear iterations, is an effective method for large-scale image analysis as those arising from high-throughput screening platforms for stem cells targeted differentiation, where one of the main goal is segmentation of thousand of images to analyze cell colonies morphology.

A semi-automated technique for labeling and counting of apoptosing retinal cells

PubMed Central

2014-01-01

Background Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer’s disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability. Results A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast “gain control”, a “blob analysis” step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined ‘size’ and ‘aspect’ ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating ‘Gold-standard’ mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson’s correlation coefficient 0.978 (p < 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p < 0.001), and Cronbach’s alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: −5.53 to 6.10) was detected between the cell counts of the two methods. Conclusions The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly. PMID:24902592

Phenotypic feature quantification of patient derived 3D cancer spheroids in fluorescence microscopy image

NASA Astrophysics Data System (ADS)

Kang, Mi-Sun; Rhee, Seon-Min; Seo, Ji-Hyun; Kim, Myoung-Hee

2017-03-01

Patients' responses to a drug differ at the cellular level. Here, we present an image-based cell phenotypic feature quantification method for predicting the responses of patient-derived glioblastoma cells to a particular drug. We used high-content imaging to understand the features of patient-derived cancer cells. A 3D spheroid culture formation resembles the in vivo environment more closely than 2D adherent cultures do, and it allows for the observation of cellular aggregate characteristics. However, cell analysis at the individual level is more challenging. In this paper, we demonstrate image-based phenotypic screening of the nuclei of patient-derived cancer cells. We first stitched the images of each well of the 384-well plate with the same state. We then used intensity information to detect the colonies. The nuclear intensity and morphological characteristics were used for the segmentation of individual nuclei. Next, we calculated the position of each nucleus that is appeal of the spatial pattern of cells in the well environment. Finally, we compared the results obtained using 3D spheroid culture cells with those obtained using 2D adherent culture cells from the same patient being treated with the same drugs. This technique could be applied for image-based phenotypic screening of cells to determine the patient's response to the drug.

High-performance imaging of stem cells using single-photon emissions

NASA Astrophysics Data System (ADS)

Wagenaar, Douglas J.; Moats, Rex A.; Hartsough, Neal E.; Meier, Dirk; Hugg, James W.; Yang, Tang; Gazit, Dan; Pelled, Gadi; Patt, Bradley E.

2011-10-01

Radiolabeled cells have been imaged for decades in the field of autoradiography. Recent advances in detector and microelectronics technologies have enabled the new field of "digital autoradiography" which remains limited to ex vivo specimens of thin tissue slices. The 3D field-of-view (FOV) of single cell imaging can be extended to millimeters if the low energy (10-30 keV) photon emissions of radionuclides are used for single-photon nuclear imaging. This new microscope uses a coded aperture foil made of highly attenuating elements such as gold or platinum to form the image as a kind of "lens". The detectors used for single-photon emission microscopy are typically silicon detectors with a pixel pitch less than 60 μm. The goal of this work is to image radiolabeled mesenchymal stem cells in vivo in an animal model of tendon repair processes. Single-photon nuclear imaging is an attractive modality for translational medicine since the labeled cells can be imaged simultaneously with the reparative processes by using the dual-isotope imaging technique. The details our microscope's two-layer gold aperture and the operation of the energy-dispersive, pixellated silicon detector are presented along with the first demonstration of energy discrimination with a 57Co source. Cell labeling techniques have been augmented by genetic engineering with the sodium-iodide symporter, a type of reporter gene imaging method that enables in vivo uptake of free 99mTc or an iodine isotope at a time point days or weeks after the insertion of the genetically modified stem cells into the animal model. This microscopy work in animal research may expand to the imaging of reporter-enabled stem cells simultaneously with the expected biological repair process in human clinical trials of stem cell therapies.

Measurement of wood/plant cell or composite material attributes with computer assisted tomography

DOEpatents

West, Darrell C.; Paulus, Michael J.; Tuskan, Gerald A.; Wimmer, Rupert

2004-06-08

A method for obtaining wood-cell attributes from cellulose containing samples includes the steps of radiating a cellulose containing sample with a beam of radiation. Radiation attenuation information is collected from radiation which passes through the sample. The source is rotated relative to the sample and the radiation and collecting steps repeated. A projected image of the sample is formed from the collected radiation attenuation information, the projected image including resolvable features of the cellulose containing sample. Cell wall thickness, cell diameter (length) and cell vacoule diameter can be determined. A system for obtaining physical measures from cellulose containing samples includes a radiation source, a radiation detector, and structure for rotating the source relative to said sample. The system forms an image of the sample from the radiation attenuation information, the image including resolvable features of the sample.

Superpixel guided active contour segmentation of retinal layers in OCT volumes

NASA Astrophysics Data System (ADS)

Bai, Fangliang; Gibson, Stuart J.; Marques, Manuel J.; Podoleanu, Adrian

2018-03-01

Retinal OCT image segmentation is a precursor to subsequent medical diagnosis by a clinician or machine learning algorithm. In the last decade, many algorithms have been proposed to detect retinal layer boundaries and simplify the image representation. Inspired by the recent success of superpixel methods for pre-processing natural images, we present a novel framework for segmentation of retinal layers in OCT volume data. In our framework, the region of interest (e.g. the fovea) is located using an adaptive-curve method. The cell layer boundaries are then robustly detected firstly using 1D superpixels, applied to A-scans, and then fitting active contours in B-scan images. Thereafter the 3D cell layer surfaces are efficiently segmented from the volume data. The framework was tested on healthy eye data and we show that it is capable of segmenting up to 12 layers. The experimental results imply the effectiveness of proposed method and indicate its robustness to low image resolution and intrinsic speckle noise.

Non-Invasive Multiphoton Imaging of Islets Transplanted Into the Pinna of the NOD Mouse Ear Reveals the Immediate Effect of Anti-CD3 Treatment in Autoimmune Diabetes.

PubMed

Benson, Robert A; Garcon, Fabien; Recino, Asha; Ferdinand, John R; Clatworthy, Menna R; Waldmann, Herman; Brewer, James M; Okkenhaug, Klaus; Cooke, Anne; Garside, Paul; Wållberg, Maja

2018-01-01

We present a novel and readily accessible method facilitating cellular time-resolved imaging of transplanted pancreatic islets. Grafting of islets to the mouse ear pinna allows non-invasive, in vivo longitudinal imaging of events in the islets and enables improved acquisition of experimental data and use of fewer experimental animals than is possible using invasive techniques, as the same mouse can be assessed for the presence of islet infiltrating cells before and after immune intervention. We have applied this method to investigating therapeutic protection of beta cells through the well-established use of anti-CD3 injection, and have acquired unprecedented data on the nature and rapidity of the effect on the islet infiltrating T cells. We demonstrate that infusion of anti-CD3 antibody leads to immediate effects on islet infiltrating T cells in islet grafts in the pinna of the ear, and causes them to increase their speed and displacement within 20 min of infusion. This technique overcomes several technical challenges associated with intravital imaging of pancreatic immune responses and facilitates routine study of beta islet cell development, differentiation, and function in health and disease.

Design of a functional cyclic HSV1-TK reporter and its application to PET imaging of apoptosis

PubMed Central

Wang, Zhe; Wang, Fu; Hida, Naoki; Kiesewetter, Dale O; Tian, Jie; Niu, Gang; Chen, Xiaoyuan

2017-01-01

Positron emission tomography (PET) is a sensitive and noninvasive imaging method that is widely used to explore molecular events in living subjects. PET can precisely and quantitatively evaluate cellular apoptosis, which has a crucial role in various physiological and pathological processes. In this protocol, we describe the design and use of an engineered cyclic herpes simplex virus 1–thymidine kinase (HSV1-TK) PET reporter whose kinase activity is specifically switched on by apoptosis. The expression of cyclic TK (cTK) in healthy cells leads to inactive product, whereas the activation of apoptosis through the caspase-3 pathway cleaves cTK, thus restoring its activity and enabling PET imaging. In addition to detailing the design and construction of the cTK plasmid in this protocol, we include assays for evaluating the function and specificity of the cTK reporter in apoptotic cells, such as assays for measuring the cell uptake of PET tracer in apoptotic cells, correlating doxorubicin (Dox)-induced cell apoptosis to cTK function recovery, and in vivo PET imaging of cancer cell apoptosis, and we also include corresponding data acquisition methods. The time to build the entire cTK reporter is ~2–3 weeks. The selection of a stable cancer cell line takes ~4–6 weeks. The time to implement assays regarding cTK function in apoptotic cells and the in vivo imaging varies depending on the experiment. The cyclization strategy described in this protocol can also be adapted to create other reporter systems for broad biomedical applications. PMID:25927390

Application of image flow cytometry for the characterization of red blood cell morphology

NASA Astrophysics Data System (ADS)

Pinto, Ruben N.; Sebastian, Joseph A.; Parsons, Michael; Chang, Tim C.; Acker, Jason P.; Kolios, Michael C.

2017-02-01

Red blood cells (RBCs) stored in hypothermic environments for the purpose of transfusion have been documented to undergo structural and functional changes over time. One sign of the so-called RBC storage lesion is irreversible damage to the cell membrane. Consequently, RBCs undergo a morphological transformation from regular, deformable biconcave discocytes to rigid spheroechinocytes. The spherically shaped RBCs lack the deformability to efficiently enter microvasculature, thereby reducing the capacity of RBCs to oxygenate tissue. Blood banks currently rely on microscope techniques that include fixing, staining and cell counting in order to morphologically characterize RBC samples; these methods are labor intensive and highly subjective. This study presents a novel, high-throughput RBC morphology characterization technique using image flow cytometry (IFC). An image segmentation template was developed to process 100,000 images acquired from the IFC system and output the relative spheroechinocyte percentage. The technique was applied on samples extracted from two blood bags to monitor the morphological changes of the RBCs during in vitro hypothermic storage. The study found that, for a given sample of RBCs, the IFC method was twice as fast in data acquisition, and analyzed 250-350 times more RBCs than the conventional method. Over the lifespan of the blood bags, the mean spheroechinocyte population increased by 37%. Future work will focus on expanding the template to segregate RBC images into more subpopulations for the validation of the IFC method against conventional techniques; the expanded template will aid in establishing quantitative links between spheroechinocyte increase and other RBC storage lesion characteristics.

A review of novel optical imaging strategies of the stroke pathology and stem cell therapy in stroke

PubMed Central

Aswendt, Markus; Adamczak, Joanna; Tennstaedt, Annette

2014-01-01

Transplanted stem cells can induce and enhance functional recovery in experimental stroke. Invasive analysis has been extensively used to provide detailed cellular and molecular characterization of the stroke pathology and engrafted stem cells. But post mortem analysis is not appropriate to reveal the time scale of the dynamic interplay between the cell graft, the ischemic lesion and the endogenous repair mechanisms. This review describes non-invasive imaging techniques which have been developed to provide complementary in vivo information. Recent advances were made in analyzing simultaneously different aspects of the cell graft (e.g., number of cells, viability state, and cell fate), the ischemic lesion (e.g., blood–brain-barrier consistency, hypoxic, and necrotic areas) and the neuronal and vascular network. We focus on optical methods, which permit simple animal preparation, repetitive experimental conditions, relatively medium-cost instrumentation and are performed under mild anesthesia, thus nearly under physiological conditions. A selection of recent examples of optical intrinsic imaging, fluorescence imaging and bioluminescence imaging to characterize the stroke pathology and engrafted stem cells are discussed. Special attention is paid to novel optimal reporter genes/probes for genetic labeling and tracking of stem cells and appropriate transgenic animal models. Requirements, advantages and limitations of these imaging platforms are critically discussed and placed into the context of other non-invasive techniques, e.g., magnetic resonance imaging and positron emission tomography, which can be joined with optical imaging in multimodal approaches. PMID:25177269

Semi-automated segmentation of neuroblastoma nuclei using the gradient energy tensor: a user driven approach

NASA Astrophysics Data System (ADS)

Kromp, Florian; Taschner-Mandl, Sabine; Schwarz, Magdalena; Blaha, Johanna; Weiss, Tamara; Ambros, Peter F.; Reiter, Michael

2015-02-01

We propose a user-driven method for the segmentation of neuroblastoma nuclei in microscopic fluorescence images involving the gradient energy tensor. Multispectral fluorescence images contain intensity and spatial information about antigene expression, fluorescence in situ hybridization (FISH) signals and nucleus morphology. The latter serves as basis for the detection of single cells and the calculation of shape features, which are used to validate the segmentation and to reject false detections. Accurate segmentation is difficult due to varying staining intensities and aggregated cells. It requires several (meta-) parameters, which have a strong influence on the segmentation results and have to be selected carefully for each sample (or group of similar samples) by user interactions. Because our method is designed for clinicians and biologists, who may have only limited image processing background, an interactive parameter selection step allows the implicit tuning of parameter values. With this simple but intuitive method, segmentation results with high precision for a large number of cells can be achieved by minimal user interaction. The strategy was validated on handsegmented datasets of three neuroblastoma cell lines.

A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix.

PubMed

Saito, Akira; Numata, Yasushi; Hamada, Takuya; Horisawa, Tomoyoshi; Cosatto, Eric; Graf, Hans-Peter; Kuroda, Masahiko; Yamamoto, Yoichiro

2016-01-01

Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features. CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.

Quantitative analysis of cell columns in the cerebral cortex.

PubMed

Buxhoeveden, D P; Switala, A E; Roy, E; Casanova, M F

2000-04-01

We present a quantified imaging method that describes the cell column in mammalian cortex. The minicolumn is an ideal template with which to examine cortical organization because it is a basic unit of function, complete in itself, which interacts with adjacent and distance columns to form more complex levels of organization. The subtle details of columnar anatomy should reflect physiological changes that have occurred in evolution as well as those that might be caused by pathologies in the brain. In this semiautomatic method, images of Nissl-stained tissue are digitized or scanned into a computer imaging system. The software detects the presence of cell columns and describes details of their morphology and of the surrounding space. Columns are detected automatically on the basis of cell-poor and cell-rich areas using a Gaussian distribution. A line is fit to the cell centers by least squares analysis. The line becomes the center of the column from which the precise location of every cell can be measured. On this basis several algorithms describe the distribution of cells from the center line and in relation to the available surrounding space. Other algorithms use cluster analyses to determine the spatial orientation of every column.

Segmentation of fluorescence microscopy cell images using unsupervised mining.

PubMed

Du, Xian; Dua, Sumeet

2010-05-28

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu's threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu's threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications.

The use of fluorescence microscopy and image analysis for rapid detection of non-producing revertant cells of Synechocystis sp. PCC6803 and Synechococcus sp. PCC7002.

PubMed

Schulze, Katja; Lang, Imke; Enke, Heike; Grohme, Diana; Frohme, Marcus

2015-04-17

Ethanol production via genetically engineered cyanobacteria is a promising solution for the production of biofuels. Through the introduction of a pyruvate decarboxylase and alcohol dehydrogenase direct ethanol production becomes possible within the cells. However, during cultivation genetic instability can lead to mutations and thus loss of ethanol production. Cells then revert back to the wild type phenotype. A method for a rapid and simple detection of these non-producing revertant cells in an ethanol producing cell population is an important quality control measure in order to predict genetic stability and the longevity of a producing culture. Several comparable cultivation experiments revealed a difference in the pigmentation for non-producing and producing cells: the accessory pigment phycocyanin (PC) is reduced in case of the ethanol producer, resulting in a yellowish appearance of the culture. Microarray and western blot studies of Synechocystis sp. PCC6803 and Synechococcus sp. PCC7002 confirmed this PC reduction on the level of RNA and protein. Based on these findings we developed a method for fluorescence microscopy in order to distinguish producing and non-producing cells with respect to their pigmentation phenotype. By applying a specific filter set the emitted fluorescence of a producer cell with a reduced PC content appeared orange. The emitted fluorescence of a non-producing cell with a wt pigmentation phenotype was detected in red, and dead cells in green. In an automated process multiple images of each sample were taken and analyzed with a plugin for the image analysis software ImageJ to identify dead (green), non-producing (red) and producing (orange) cells. The results of the presented validation experiments revealed a good identification with 98 % red cells in the wt sample and 90 % orange cells in the producer sample. The detected wt pigmentation phenotype (red cells) in the producer sample were either not fully induced yet (in 48 h induced cultures) or already reverted to a non-producing cells (in long-term photobioreactor cultivations), emphasizing the sensitivity and resolution of the method. The fluorescence microscopy method displays a useful technique for a rapid detection of non-producing single cells in an ethanol producing cell population.

FRET Imaging in Three-dimensional Hydrogels

PubMed Central

Taboas, Juan M.

2016-01-01

Imaging of Förster resonance energy transfer (FRET) is a powerful tool for examining cell biology in real-time. Studies utilizing FRET commonly employ two-dimensional (2D) culture, which does not mimic the three-dimensional (3D) cellular microenvironment. A method to perform quenched emission FRET imaging using conventional widefield epifluorescence microscopy of cells within a 3D hydrogel environment is presented. Here an analysis method for ratiometric FRET probes that yields linear ratios over the probe activation range is described. Measurement of intracellular cyclic adenosine monophosphate (cAMP) levels is demonstrated in chondrocytes under forskolin stimulation using a probe for EPAC1 activation (ICUE1) and the ability to detect differences in cAMP signaling dependent on hydrogel material type, herein a photocrosslinking hydrogel (PC-gel, polyethylene glycol dimethacrylate) and a thermoresponsive hydrogel (TR-gel). Compared with 2D FRET methods, this method requires little additional work. Laboratories already utilizing FRET imaging in 2D can easily adopt this method to perform cellular studies in a 3D microenvironment. It can further be applied to high throughput drug screening in engineered 3D microtissues. Additionally, it is compatible with other forms of FRET imaging, such as anisotropy measurement and fluorescence lifetime imaging (FLIM), and with advanced microscopy platforms using confocal, pulsed, or modulated illumination. PMID:27500354

Nondestructive Analysis of Tumor-Associated Membrane Protein Integrating Imaging and Amplified Detection in situ Based on Dual-Labeled DNAzyme.

PubMed

Chen, Xiaoxia; Zhao, Jing; Chen, Tianshu; Gao, Tao; Zhu, Xiaoli; Li, Genxi

2018-01-01

Comprehensive analysis of the expression level and location of tumor-associated membrane proteins (TMPs) is of vital importance for the profiling of tumor cells. Currently, two kinds of independent techniques, i.e. ex situ detection and in situ imaging, are usually required for the quantification and localization of TMPs respectively, resulting in some inevitable problems. Methods: Herein, based on a well-designed and fluorophore-labeled DNAzyme, we develop an integrated and facile method, in which imaging and quantification of TMPs in situ are achieved simultaneously in a single system. The labeled DNAzyme not only produces localized fluorescence for the visualization of TMPs but also catalyzes the cleavage of a substrate to produce quantitative fluorescent signals that can be collected from solution for the sensitive detection of TMPs. Results: Results from the DNAzyme-based in situ imaging and quantification of TMPs match well with traditional immunofluorescence and western blotting. In addition to the advantage of two-in-one, the DNAzyme-based method is highly sensitivity, allowing the detection of TMPs in only 100 cells. Moreover, the method is nondestructive. Cells after analysis could retain their physiological activity and could be cultured for other applications. Conclusion: The integrated system provides solid results for both imaging and quantification of TMPs, making it a competitive method over some traditional techniques for the analysis of TMPs, which offers potential application as a toolbox in the future.

Novel Peptide Sequence (“IQ-tag”) with High Affinity for NIR Fluorochromes Allows Protein and Cell Specific Labeling for In Vivo Imaging

PubMed Central

McCarthy, Jason R.; Weissleder, Ralph

2007-01-01

Background Probes that allow site-specific protein labeling have become critical tools for visualizing biological processes. Methods Here we used phage display to identify a novel peptide sequence with nanomolar affinity for near infrared (NIR) (benz)indolium fluorochromes. The developed peptide sequence (“IQ-tag”) allows detection of NIR dyes in a wide range of assays including ELISA, flow cytometry, high throughput screens, microscopy, and optical in vivo imaging. Significance The described method is expected to have broad utility in numerous applications, namely site-specific protein imaging, target identification, cell tracking, and drug development. PMID:17653285

Building cell models and simulations from microscope images.

PubMed

Murphy, Robert F

2016-03-01

The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge. Copyright © 2015 Elsevier Inc. All rights reserved.

Image analysis of the blood cells for cytomorphodiagnostics and control of the effectiveness treatment

NASA Astrophysics Data System (ADS)

Zhukotsky, Alexander V.; Kogan, Emmanuil M.; Kopylov, Victor F.; Marchenko, Oleg V.; Lomakin, O. A.

1994-07-01

A new method for morphodensitometric analysis of blood cells was applied for medically screening some ecological influence and infection pathologies. A complex algorithm of computational image processing was created for supra molecular restructurings of interphase chromatin of lymphocytes research. It includes specific methods of staining and unifies different quantitative analysis methods. Our experience with the use of a television image analyzer in cytological and immunological studies made it possible to carry out some research in morphometric analysis of chromatin structure in interphase lymphocyte nuclei in genetic and virus pathologies. In our study to characterize lymphocytes as an image-forming system by a rigorous mathematical description we used an approach involving contaminant evaluation of the topography of chromatin network intact and victims' lymphocytes. It is also possible to digitize data, which revealed significant distinctions between control and experiment. The method allows us to observe the minute structural changes in chromatin, especially eu- and hetero-chromatin that were previously studied by genetics only in chromosomes.

Extended Field Laser Confocal Microscopy (EFLCM): Combining automated Gigapixel image capture with in silico virtual microscopy

PubMed Central

Flaberg, Emilie; Sabelström, Per; Strandh, Christer; Szekely, Laszlo

2008-01-01

Background Confocal laser scanning microscopy has revolutionized cell biology. However, the technique has major limitations in speed and sensitivity due to the fact that a single laser beam scans the sample, allowing only a few microseconds signal collection for each pixel. This limitation has been overcome by the introduction of parallel beam illumination techniques in combination with cold CCD camera based image capture. Methods Using the combination of microlens enhanced Nipkow spinning disc confocal illumination together with fully automated image capture and large scale in silico image processing we have developed a system allowing the acquisition, presentation and analysis of maximum resolution confocal panorama images of several Gigapixel size. We call the method Extended Field Laser Confocal Microscopy (EFLCM). Results We show using the EFLCM technique that it is possible to create a continuous confocal multi-colour mosaic from thousands of individually captured images. EFLCM can digitize and analyze histological slides, sections of entire rodent organ and full size embryos. It can also record hundreds of thousands cultured cells at multiple wavelength in single event or time-lapse fashion on fixed slides, in live cell imaging chambers or microtiter plates. Conclusion The observer independent image capture of EFLCM allows quantitative measurements of fluorescence intensities and morphological parameters on a large number of cells. EFLCM therefore bridges the gap between the mainly illustrative fluorescence microscopy and purely quantitative flow cytometry. EFLCM can also be used as high content analysis (HCA) instrument for automated screening processes. PMID:18627634

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

High-speed autofocusing of a cell using diffraction pattern

NASA Astrophysics Data System (ADS)

Oku, Hiromasa; Ishikawa, Masatoshi; Theodorus; Hashimoto, Koichi

2006-05-01

This paper proposes a new autofocusing method for observing cells under a transmission illumination. The focusing method uses a quick and simple focus estimation technique termed “depth from diffraction,” which is based on a diffraction pattern in a defocused image of a biological specimen. Since this method can estimate the focal position of the specimen from only a single defocused image, it can easily realize high-speed autofocusing. To demonstrate the method, it was applied to continuous focus tracking of a swimming paramecium, in combination with two-dimensional position tracking. Three-dimensional tracking of the paramecium for 70 s was successfully demonstrated.

Accurate Region-of-Interest Recovery Improves the Measurement of the Cell Migration Rate in the In Vitro Wound Healing Assay.

PubMed

Bedoya, Cesar; Cardona, Andrés; Galeano, July; Cortés-Mancera, Fabián; Sandoz, Patrick; Zarzycki, Artur

2017-12-01

The wound healing assay is widely used for the quantitative analysis of highly regulated cellular events. In this essay, a wound is voluntarily produced on a confluent cell monolayer, and then the rate of wound reduction (WR) is characterized by processing images of the same regions of interest (ROIs) recorded at different time intervals. In this method, sharp-image ROI recovery is indispensable to compensate for displacements of the cell cultures due either to the exploration of multiple sites of the same culture or to transfers from the microscope stage to a cell incubator. ROI recovery is usually done manually and, despite a low-magnification microscope objective is generally used (10x), repositioning imperfections constitute a major source of errors detrimental to the WR measurement accuracy. We address this ROI recovery issue by using pseudoperiodic patterns fixed onto the cell culture dishes, allowing the easy localization of ROIs and the accurate quantification of positioning errors. The method is applied to a tumor-derived cell line, and the WR rates are measured by means of two different image processing software. Sharp ROI recovery based on the proposed method is found to improve significantly the accuracy of the WR measurement and the positioning under the microscope.

Method of fabricating an imaging X-ray spectrometer

NASA Technical Reports Server (NTRS)

Alcorn, G. E. (Inventor); Burgess, A. S. (Inventor)

1986-01-01

A process for fabricating an X-ray spectrometer having imaging and energy resolution of X-ray sources is discussed. The spectrometer has an array of adjoinging rectangularly shaped detector cells formed in a silicon body. The walls of the cells are created by laser drilling holes completely through the silicon body and diffusing n(+) phosphorous doping material therethrough. A thermally migrated aluminum electrode is formed centrally through each of the cells.

Validation of Biomarkers for Prostate Cancer Prognosis

DTIC Science & Technology

2017-06-01

such as the innate immune response to the malignancy, interactions of the malignant cells with the sur- rounding stroma, or stochastic factors that are...it is inadequate for automatic imaging reading. The main reason is that it still requires pathologists to sketch the boundary for cancer cell region...and merely requires a method (imaging, cell collection, measurement of a bioanalyte) that correlates with a disease state, followed by the application

Computer-assisted analysis of the vascular endothelial cell motile response to injury.

PubMed

Askey, D B; Herman, I M

1988-12-01

We have developed an automated, user-friendly method to track vascular endothelial cell migration in vitro using an IBM PC/XT with MS DOS. Analog phase-contrast images of the bovine aortic endothelial cells are converted into digital images (8 bit, 250 x 240 pixel resolution) using a Tecmar Video VanGogh A/D board. Digitized images are stored at selected time points following mechanical injury in vitro. FORTRAN and assembly language subroutines have been implemented to automatically detect the wound edge and the edge of each cell nucleus in the phase-contrast, light-microscope field. Detection of the wound edge is accomplished by intensity thresholding following noise reduction in the image and subsequent sampling of the wound. After the range of wound intensities is determined, the entire image is sampled and a histogram of intensities is formed. The histogram peak corresponding to the wound intensities is subtracted, leaving a histogram peak that gives the range of intensities corresponding to the cell nuclei. Rates of cell migration, as well as cellular trajectories and cell surface areas, can be automatically quantitated and analyzed. This inexpensive, automated cell-tracking system should be widely applicable in a variety of cell biologic applications.

Automatic tracking of labeled red blood cells in microchannels.

PubMed

Pinho, Diana; Lima, Rui; Pereira, Ana I; Gayubo, Fernando

2013-09-01

The current study proposes an automatic method for the segmentation and tracking of red blood cells flowing through a 100- μm glass capillary. The original images were obtained by means of a confocal system and then processed in MATLAB using the Image Processing Toolbox. The measurements obtained with the proposed automatic method were compared with the results determined by a manual tracking method. The comparison was performed by using both linear regressions and Bland-Altman analysis. The results have shown a good agreement between the two methods. Therefore, the proposed automatic method is a powerful way to provide rapid and accurate measurements for in vitro blood experiments in microchannels. Copyright © 2012 John Wiley & Sons, Ltd.

Tomographic brain imaging with nucleolar detail and automatic cell counting

NASA Astrophysics Data System (ADS)

Hieber, Simone E.; Bikis, Christos; Khimchenko, Anna; Schweighauser, Gabriel; Hench, Jürgen; Chicherova, Natalia; Schulz, Georg; Müller, Bert

2016-09-01

Brain tissue evaluation is essential for gaining in-depth insight into its diseases and disorders. Imaging the human brain in three dimensions has always been a challenge on the cell level. In vivo methods lack spatial resolution, and optical microscopy has a limited penetration depth. Herein, we show that hard X-ray phase tomography can visualise a volume of up to 43 mm3 of human post mortem or biopsy brain samples, by demonstrating the method on the cerebellum. We automatically identified 5,000 Purkinje cells with an error of less than 5% at their layer and determined the local surface density to 165 cells per mm2 on average. Moreover, we highlight that three-dimensional data allows for the segmentation of sub-cellular structures, including dendritic tree and Purkinje cell nucleoli, without dedicated staining. The method suggests that automatic cell feature quantification of human tissues is feasible in phase tomograms obtained with isotropic resolution in a label-free manner.

Correlating Intravital Multi-Photon Microscopy to 3D Electron Microscopy of Invading Tumor Cells Using Anatomical Reference Points

PubMed Central

Karreman, Matthia A.; Mercier, Luc; Schieber, Nicole L.; Shibue, Tsukasa; Schwab, Yannick; Goetz, Jacky G.

2014-01-01

Correlative microscopy combines the advantages of both light and electron microscopy to enable imaging of rare and transient events at high resolution. Performing correlative microscopy in complex and bulky samples such as an entire living organism is a time-consuming and error-prone task. Here, we investigate correlative methods that rely on the use of artificial and endogenous structural features of the sample as reference points for correlating intravital fluorescence microscopy and electron microscopy. To investigate tumor cell behavior in vivo with ultrastructural accuracy, a reliable approach is needed to retrieve single tumor cells imaged deep within the tissue. For this purpose, fluorescently labeled tumor cells were subcutaneously injected into a mouse ear and imaged using two-photon-excitation microscopy. Using near-infrared branding, the position of the imaged area within the sample was labeled at the skin level, allowing for its precise recollection. Following sample preparation for electron microscopy, concerted usage of the artificial branding and anatomical landmarks enables targeting and approaching the cells of interest while serial sectioning through the specimen. We describe here three procedures showing how three-dimensional (3D) mapping of structural features in the tissue can be exploited to accurately correlate between the two imaging modalities, without having to rely on the use of artificially introduced markers of the region of interest. The methods employed here facilitate the link between intravital and nanoscale imaging of invasive tumor cells, enabling correlating function to structure in the study of tumor invasion and metastasis. PMID:25479106

Multifunctional ferromagnetic disks for modulating cell function

PubMed Central

Vitol, Elina A.; Novosad, Valentyn; Rozhkova, Elena A.

2013-01-01

In this work, we focus on the methods for controlling cell function with ferromagnetic disk-shaped particles. We will first review the history of magnetically assisted modulation of cell behavior and applications of magnetic particles for studying physical properties of a cell. Then, we consider the biological applications of the microdisks such as the method for induction of cancer cell apoptosis, controlled drug release, hyperthermia and MRI imaging. PMID:23766544

Optical computed tomography for spatially isotropic four-dimensional imaging of live single cells

PubMed Central

Kelbauskas, Laimonas; Shetty, Rishabh; Cao, Bin; Wang, Kuo-Chen; Smith, Dean; Wang, Hong; Chao, Shi-Hui; Gangaraju, Sandhya; Ashcroft, Brian; Kritzer, Margaret; Glenn, Honor; Johnson, Roger H.; Meldrum, Deirdre R.

2017-01-01

Quantitative three-dimensional (3D) computed tomography (CT) imaging of living single cells enables orientation-independent morphometric analysis of the intricacies of cellular physiology. Since its invention, x-ray CT has become indispensable in the clinic for diagnostic and prognostic purposes due to its quantitative absorption-based imaging in true 3D that allows objects of interest to be viewed and measured from any orientation. However, x-ray CT has not been useful at the level of single cells because there is insufficient contrast to form an image. Recently, optical CT has been developed successfully for fixed cells, but this technology called Cell-CT is incompatible with live-cell imaging due to the use of stains, such as hematoxylin, that are not compatible with cell viability. We present a novel development of optical CT for quantitative, multispectral functional 4D (three spatial + one spectral dimension) imaging of living single cells. The method applied to immune system cells offers truly isotropic 3D spatial resolution and enables time-resolved imaging studies of cells suspended in aqueous medium. Using live-cell optical CT, we found a heterogeneous response to mitochondrial fission inhibition in mouse macrophages and differential basal remodeling of small (0.1 to 1 fl) and large (1 to 20 fl) nuclear and mitochondrial structures on a 20- to 30-s time scale in human myelogenous leukemia cells. Because of its robust 3D measurement capabilities, live-cell optical CT represents a powerful new tool in the biomedical research field. PMID:29226240

Two-photon fluorescence anisotropy imaging

NASA Astrophysics Data System (ADS)

Li, Wei; Wang, Yi; Shao, Hanrong; He, Yonghong; Ma, Hui

2006-09-01

We have developed a novel method for imaging the fluorescence intensity and anisotropy by two-photon fluorescence microscopy and tested its capability in biological application. This method is applied to model sample including FITC and FITC-CD44 antibody solution and also FITC-CD44 stained cells. The fluorescence anisotropy (FA) of FITC-CD44ab solution is higher than the FITC solution with the same concentration. The fluorescence in cell sample has even higher FA than in solution because the rotation diffusion is restrained in membrane. The method is employed to study the effect of berberine a kind of Chinese medicine, on tumor metastasis. The results indicated that tumor cell membrane fluidity is decreasing with increasing the concentration of berberine in culture medium.

Quantification of Cardiomyocyte Alignment from Three-Dimensional (3D) Confocal Microscopy of Engineered Tissue.

PubMed

Kowalski, William J; Yuan, Fangping; Nakane, Takeichiro; Masumoto, Hidetoshi; Dwenger, Marc; Ye, Fei; Tinney, Joseph P; Keller, Bradley B

2017-08-01

Biological tissues have complex, three-dimensional (3D) organizations of cells and matrix factors that provide the architecture necessary to meet morphogenic and functional demands. Disordered cell alignment is associated with congenital heart disease, cardiomyopathy, and neurodegenerative diseases and repairing or replacing these tissues using engineered constructs may improve regenerative capacity. However, optimizing cell alignment within engineered tissues requires quantitative 3D data on cell orientations and both efficient and validated processing algorithms. We developed an automated method to measure local 3D orientations based on structure tensor analysis and incorporated an adaptive subregion size to account for multiple scales. Our method calculates the statistical concentration parameter, κ, to quantify alignment, as well as the traditional orientational order parameter. We validated our method using synthetic images and accurately measured principal axis and concentration. We then applied our method to confocal stacks of cleared, whole-mount engineered cardiac tissues generated from human-induced pluripotent stem cells or embryonic chick cardiac cells and quantified cardiomyocyte alignment. We found significant differences in alignment based on cellular composition and tissue geometry. These results from our synthetic images and confocal data demonstrate the efficiency and accuracy of our method to measure alignment in 3D tissues.

Enumerating Hematopoietic Stem and Progenitor Cells in Zebrafish Embryos.

PubMed

Esain, Virginie; Cortes, Mauricio; North, Trista E

2016-01-01

Over the past 20 years, zebrafish have proven to be a valuable model to dissect the signaling pathways involved in hematopoiesis, including Hematopoietic Stem and Progenitor Cell (HSPC) formation and homeostasis. Despite tremendous efforts to generate the tools necessary to characterize HSPCs in vitro and in vivo the zebrafish community still lacks standardized methods to quantify HSPCs across laboratories. Here, we describe three methods used routinely in our lab, and in others, to reliably enumerate HSPCs in zebrafish embryos: large-scale live imaging of transgenic reporter lines, Fluorescence-Activated Cell Sorting (FACS), and in vitro cell culture. While live imaging and FACS analysis allows enumeration of total or site-specific HSPCs, the cell culture assay provides the unique opportunity to test the functional potential of isolated HSPCs, similar to those employed in mammals.

Image Reconstruction Under Contact Impedance Effect in Micro Electrical Impedance Tomography Sensors.

PubMed

Liu, Xiayi; Yao, Jiafeng; Zhao, Tong; Obara, Hiromichi; Cui, Yahui; Takei, Masahiro

2018-06-01

Contact impedance has an important effect on micro electrical impedance tomography (EIT) sensors compared to conventional macro sensors. In the present work, a complex contact impedance effect ratio ξ is defined to quantitatively evaluate the effect of the contact impedance on the accuracy of the reconstructed images by micro EIT. Quality of the reconstructed image under various ξ is estimated by the phantom simulation to find the optimum algorithm. The generalized vector sampled pattern matching (GVSPM) method reveals the best image quality and the best tolerance to ξ. Moreover, the images of yeast cells sedimentary distribution in a multilayered microchannel are reconstructed by the GVSPM method under various mean magnitudes of contact impedance effect ratio |ξ|. The result shows that the best image quality that has the smallest voltage error U E = 0.581 is achieved with measurement frequency f = 1 MHz and mean magnitude |ξ| = 26. In addition, the reconstructed images of cells distribution become improper while f < 10 kHz and mean value of |ξ| > 2400.

Statistical image segmentation for the detection of skin lesion borders in UV fluorescence excitation

NASA Astrophysics Data System (ADS)

Ortega-Martinez, Antonio; Padilla-Martinez, Juan Pablo; Franco, Walfre

2016-04-01

The skin contains several fluorescent molecules or fluorophores that serve as markers of structure, function and composition. UV fluorescence excitation photography is a simple and effective way to image specific intrinsic fluorophores, such as the one ascribed to tryptophan which emits at a wavelength of 345 nm upon excitation at 295 nm, and is a marker of cellular proliferation. Earlier, we built a clinical UV photography system to image cellular proliferation. In some samples, the naturally low intensity of the fluorescence can make it difficult to separate the fluorescence of cells in higher proliferation states from background fluorescence and other imaging artifacts -- like electronic noise. In this work, we describe a statistical image segmentation method to separate the fluorescence of interest. Statistical image segmentation is based on image averaging, background subtraction and pixel statistics. This method allows to better quantify the intensity and surface distributions of fluorescence, which in turn simplify the detection of borders. Using this method we delineated the borders of highly-proliferative skin conditions and diseases, in particular, allergic contact dermatitis, psoriatic lesions and basal cell carcinoma. Segmented images clearly define lesion borders. UV fluorescence excitation photography along with statistical image segmentation may serve as a quick and simple diagnostic tool for clinicians.

A human visual based binarization technique for histological images

NASA Astrophysics Data System (ADS)

Shreyas, Kamath K. M.; Rajendran, Rahul; Panetta, Karen; Agaian, Sos

2017-05-01

In the field of vision-based systems for object detection and classification, thresholding is a key pre-processing step. Thresholding is a well-known technique for image segmentation. Segmentation of medical images, such as Computed Axial Tomography (CAT), Magnetic Resonance Imaging (MRI), X-Ray, Phase Contrast Microscopy, and Histological images, present problems like high variability in terms of the human anatomy and variation in modalities. Recent advances made in computer-aided diagnosis of histological images help facilitate detection and classification of diseases. Since most pathology diagnosis depends on the expertise and ability of the pathologist, there is clearly a need for an automated assessment system. Histological images are stained to a specific color to differentiate each component in the tissue. Segmentation and analysis of such images is problematic, as they present high variability in terms of color and cell clusters. This paper presents an adaptive thresholding technique that aims at segmenting cell structures from Haematoxylin and Eosin stained images. The thresholded result can further be used by pathologists to perform effective diagnosis. The effectiveness of the proposed method is analyzed by visually comparing the results to the state of art thresholding methods such as Otsu, Niblack, Sauvola, Bernsen, and Wolf. Computer simulations demonstrate the efficiency of the proposed method in segmenting critical information.

Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.

PubMed

Kusumoto, Dai; Lachmann, Mark; Kunihiro, Takeshi; Yuasa, Shinsuke; Kishino, Yoshikazu; Kimura, Mai; Katsuki, Toshiomi; Itoh, Shogo; Seki, Tomohisa; Fukuda, Keiichi

2018-06-05

Deep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem cells (iPSCs), without the need for immunostaining or lineage tracing. Networks were trained to predict whether phase-contrast images contain endothelial cells based on morphology only. Predictions were validated by comparison to immunofluorescence staining for CD31, a marker of endothelial cells. Method parameters were then automatically and iteratively optimized to increase prediction accuracy. We found that prediction accuracy was correlated with network depth and pixel size of images to be analyzed. Finally, K-fold cross-validation confirmed that optimized convolutional neural networks can identify endothelial cells with high performance, based only on morphology. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

New decision support tool for acute lymphoblastic leukemia classification

NASA Astrophysics Data System (ADS)

Madhukar, Monica; Agaian, Sos; Chronopoulos, Anthony T.

2012-03-01

In this paper, we build up a new decision support tool to improve treatment intensity choice in childhood ALL. The developed system includes different methods to accurately measure furthermore cell properties in microscope blood film images. The blood images are exposed to series of pre-processing steps which include color correlation, and contrast enhancement. By performing K-means clustering on the resultant images, the nuclei of the cells under consideration are obtained. Shape features and texture features are then extracted for classification. The system is further tested on the classification of spectra measured from the cell nuclei in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. The results show that the proposed system robustly segments and classifies acute lymphoblastic leukemia based on complete microscopic blood images.

Development and validation of TOF-SIMS and CLSM imaging method for cytotoxicity study of ZnO nanoparticles in HaCaT cells.

PubMed

Lee, Pei-Ling; Chen, Bo-Chia; Gollavelli, Ganesh; Shen, Sin-Yu; Yin, Yu-Sheng; Lei, Shiu-Ling; Jhang, Cian-Ling; Lee, Woan-Ruoh; Ling, Yong-Chien

2014-07-30

Zinc oxide nanoparticles (ZnO NPs) exhibit novel physiochemical properties and have found increasing use in sunscreen products and cosmetics. The potential toxicity is of increasing concern due to their close association with human skin. A time-of-flight secondary ion mass spectrometry (TOF-SIMS) and confocal laser scanning microscopy (CLSM) imaging method was developed and validated for rapid and sensitive cytotoxicity study of ZnO NPs using human skin equivalent HaCaT cells as a model system. Assorted material, chemical, and toxicological analysis methods were used to confirm their shape, size, crystalline structure, and aggregation properties as well as dissolution behavior and effect on HaCaT cell viability in the presence of various concentrations of ZnO NPs in aqueous media. Comparative and correlative analyses of aforementioned results with TOF-SIMS and CLSM imaging results exhibit reasonable and acceptable outcome. A marked drop in survival rate was observed with 50μg/ml ZnO NPs. The CLSM images reveal the absorption and localization of ZnO NPs in cytoplasm and nuclei. The TOF-SIMS images demonstrate elevated levels of intracellular ZnO concentration and associated Zn concentration-dependent (40)Ca/(39)K ratio, presumably caused by the dissolution behavior of ZnO NPs. Additional validation by using stable isotope-labeled (68)ZnO NPs as tracers under the same experimental conditions yields similar cytotoxicity effect. The imaging results demonstrate spatially-resolved cytotoxicity relationship between intracellular ZnO NPs, (40)Ca/(39)K ratio, phosphocholine fragments, and glutathione fragments. The trend of change in TOF-SIMS spectra and images of ZnO NPs treated HaCaT cells demonstrate the possible mode of actions by ZnO NP involves cell membrane disruption, cytotoxic response, and ROS mediated apoptosis. Copyright © 2014 Elsevier B.V. All rights reserved.

Live Cell Imaging of Alphaherpes Virus Anterograde Transport and Spread

PubMed Central

Taylor, Matthew P.; Kratchmarov, Radomir; Enquist, Lynn W.

2013-01-01

Advances in live cell fluorescence microscopy techniques, as well as the construction of recombinant viral strains that express fluorescent fusion proteins have enabled real-time visualization of transport and spread of alphaherpes virus infection of neurons. The utility of novel fluorescent fusion proteins to viral membrane, tegument, and capsids, in conjunction with live cell imaging, identified viral particle assemblies undergoing transport within axons. Similar tools have been successfully employed for analyses of cell-cell spread of viral particles to quantify the number and diversity of virions transmitted between cells. Importantly, the techniques of live cell imaging of anterograde transport and spread produce a wealth of information including particle transport velocities, distributions of particles, and temporal analyses of protein localization. Alongside classical viral genetic techniques, these methodologies have provided critical insights into important mechanistic questions. In this article we describe in detail the imaging methods that were developed to answer basic questions of alphaherpes virus transport and spread. PMID:23978901

Contraction of gut smooth muscle cells assessed by fluorescence imaging.

PubMed

Tokita, Yohei; Akiho, Hirotada; Nakamura, Kazuhiko; Ihara, Eikichi; Yamamoto, Masahiro

2015-03-01

Here we discuss the development of a novel cell imaging system for the evaluation of smooth muscle cell (SMC) contraction. SMCs were isolated from the circular and longitudinal muscular layers of mouse small intestine by enzymatic digestion. SMCs were stimulated by test agents, thereafter fixed in acrolein. Actin in fixed SMCs was stained with phalloidin and cell length was determined by measuring diameter at the large end of phalloidin-stained strings within the cells. The contractile response was taken as the decrease in the average length of a population of stimulated-SMCs. Various mediators and chemically identified compounds of daikenchuto (DKT), pharmaceutical-grade traditional Japanese prokinetics, were examined. Verification of the integrity of SMC morphology by phalloidin and DAPI staining and semi-automatic measurement of cell length using an imaging analyzer was a reliable method by which to quantify the contractile response. Serotonin, substance P, prostaglandin E2 and histamine induced SMC contraction in concentration-dependent manner. Two components of DKT, hydroxy-α-sanshool and hydroxy-β-sanshool, induced contraction of SMCs. We established a novel cell imaging technique to evaluate SMC contractility. This method may facilitate investigation into SMC activity and its role in gastrointestinal motility, and may assist in the discovery of new prokinetic agents. Copyright © 2015 Japanese Pharmacological Society. Production and hosting by Elsevier B.V. All rights reserved.

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

Intraoperative near-infrared fluorescence imaging and spectroscopy identifies residual tumor cells in wounds

NASA Astrophysics Data System (ADS)

Holt, David; Parthasarathy, Ashwin B.; Okusanya, Olugbenga; Keating, Jane; Venegas, Ollin; Deshpande, Charuhas; Karakousis, Giorgos; Madajewski, Brian; Durham, Amy; Nie, Shuming; Yodh, Arjun G.; Singhal, Sunil

2015-07-01

Surgery is the most effective method to cure patients with solid tumors, and 50% of all cancer patients undergo resection. Local recurrences are due to tumor cells remaining in the wound, thus we explore near-infrared (NIR) fluorescence spectroscopy and imaging to identify residual cancer cells after surgery. Fifteen canines and two human patients with spontaneously occurring sarcomas underwent intraoperative imaging. During the operation, the wounds were interrogated with NIR fluorescence imaging and spectroscopy. NIR monitoring identified the presence or absence of residual tumor cells after surgery in 14/15 canines with a mean fluorescence signal-to-background ratio (SBR) of ˜16. Ten animals showed no residual tumor cells in the wound bed (mean SBR<2, P<0.001). None had a local recurrence at >1-year follow-up. In five animals, the mean SBR of the wound was >15, and histopathology confirmed tumor cells in the postsurgical wound in four/five canines. In the human pilot study, neither patient had residual tumor cells in the wound bed, and both remain disease free at >1.5-year follow up. Intraoperative NIR fluorescence imaging and spectroscopy identifies residual tumor cells in surgical wounds. These observations suggest that NIR imaging techniques may improve tumor resection during cancer operations.

Intraoperative near-infrared fluorescence imaging and spectroscopy identifies residual tumor cells in wounds

PubMed Central

Holt, David; Parthasarathy, Ashwin B.; Okusanya, Olugbenga; Keating, Jane; Venegas, Ollin; Deshpande, Charuhas; Karakousis, Giorgos; Madajewski, Brian; Durham, Amy; Nie, Shuming; Yodh, Arjun G.; Singhal, Sunil

2015-01-01

Abstract. Surgery is the most effective method to cure patients with solid tumors, and 50% of all cancer patients undergo resection. Local recurrences are due to tumor cells remaining in the wound, thus we explore near-infrared (NIR) fluorescence spectroscopy and imaging to identify residual cancer cells after surgery. Fifteen canines and two human patients with spontaneously occurring sarcomas underwent intraoperative imaging. During the operation, the wounds were interrogated with NIR fluorescence imaging and spectroscopy. NIR monitoring identified the presence or absence of residual tumor cells after surgery in 14/15 canines with a mean fluorescence signal-to-background ratio (SBR) of ∼16. Ten animals showed no residual tumor cells in the wound bed (mean SBR<2, P<0.001). None had a local recurrence at >1-year follow-up. In five animals, the mean SBR of the wound was >15, and histopathology confirmed tumor cells in the postsurgical wound in four/five canines. In the human pilot study, neither patient had residual tumor cells in the wound bed, and both remain disease free at >1.5-year follow up. Intraoperative NIR fluorescence imaging and spectroscopy identifies residual tumor cells in surgical wounds. These observations suggest that NIR imaging techniques may improve tumor resection during cancer operations. PMID:26160347

Imaging the beating heart in the mouse using intravital microscopy techniques

PubMed Central

Vinegoni, Claudio; Aguirre, Aaron D; Lee, Sungon; Weissleder, Ralph

2017-01-01

Real-time microscopic imaging of moving organs at single-cell resolution represents a major challenge in studying complex biology in living systems. Motion of the tissue from the cardiac and respiratory cycles severely limits intravital microscopy by compromising ultimate spatial and temporal imaging resolution. However, significant recent advances have enabled single-cell resolution imaging to be achieved in vivo. In this protocol, we describe experimental procedures for intravital microscopy based on a combination of thoracic surgery, tissue stabilizers and acquisition gating methods, which enable imaging at the single-cell level in the beating heart in the mouse. Setup of the model is typically completed in 1 h, which allows 2 h or more of continuous cardiac imaging. This protocol can be readily adapted for the imaging of other moving organs, and it will therefore broadly facilitate in vivo high-resolution microscopy studies. PMID:26492138

PH-sensitive fluorescence detection by diffuse fluorescence tomography

NASA Astrophysics Data System (ADS)

Li, Jiao; Gao, Feng; Duan, Linjing; Wang, Xin; Zhang, Limin; Zhao, Huijuan

2012-03-01

The importance of cellular pH has been shown clearly in the study of cell activity, pathological feature, drug metabolism, etc. Monitoring pH changes of living cells and imaging the regions with abnormal pH values in vivo could provide the physiologic and pathologic information for the research of the cell biology, pharmacokinetics, diagnostics and therapeutics of certain diseases such as cancer. Thus, pH-sensitive fluorescence imaging of bulk tissues has been attracting great attention in the regime of near-infrared diffuse fluorescence tomography (DFT), an efficient small-animal imaging tool. In this paper, the feasibility of quantifying pH-sensitive fluorescence targets in turbid medium is investigated using both time-domain and steady-state DFT methods. By use of the specifically designed time-domain and continuous-wave systems and the previously proposed image reconstruction scheme, we validate the method through 2-dimensional imaging experiments on a small-animal-sized phantom with multiply targets of distinct pH values. The results show that the approach can localize the targets with reasonable accuracy and achieve quantitative reconstruction of the pH-sensitive fluorescent yield.

Tumor cell differentiation by label-free microscopy

NASA Astrophysics Data System (ADS)

Schneckenburger, Herbert; Weber, Petra; Wagner, Michael

2013-05-01

Autofluorescence and Raman measurements of U251-MG glioblastoma cells prior and subsequent to activation of tumor suppressor genes are compared. While phase contrast images and fluorescence intensity patterns of the tumor (control) cells and the less malignant cells are similar, differences can be deduced from fluorescence spectra and nanosecond decay times. In particular, upon excitation around 375nm, the fluorescence ratio of the protein bound and the free coenzyme NADH depends on the state of malignancy and reflects different cytoplasmic (including lysosomal) and mitochondrial contributions. Slight differences are also observed in the Raman spectra of these cell lines, mainly originating from small granules (lysosomes) surrounding the cell nucleus. While larger numbers of fluorescence and Raman spectra are evaluated by multivariate statistical methods, additional information is obtained from spectral images and fluorescence lifetime images (FLIM).

Noninvasive imaging of protein-protein interactions in living animals

NASA Astrophysics Data System (ADS)

Luker, Gary D.; Sharma, Vijay; Pica, Christina M.; Dahlheimer, Julie L.; Li, Wei; Ochesky, Joseph; Ryan, Christine E.; Piwnica-Worms, Helen; Piwnica-Worms, David

2002-05-01

Protein-protein interactions control transcription, cell division, and cell proliferation as well as mediate signal transduction, oncogenic transformation, and regulation of cell death. Although a variety of methods have been used to investigate protein interactions in vitro and in cultured cells, none can analyze these interactions in intact, living animals. To enable noninvasive molecular imaging of protein-protein interactions in vivo by positron-emission tomography and fluorescence imaging, we engineered a fusion reporter gene comprising a mutant herpes simplex virus 1 thymidine kinase and green fluorescent protein for readout of a tetracycline-inducible, two-hybrid system in vivo. By using micro-positron-emission tomography, interactions between p53 tumor suppressor and the large T antigen of simian virus 40 were visualized in tumor xenografts of HeLa cells stably transfected with the imaging constructs. Imaging protein-binding partners in vivo will enable functional proteomics in whole animals and provide a tool for screening compounds targeted to specific protein-protein interactions in living animals.

Long Term Ex Vivo Culture and Live Imaging of Drosophila Larval Imaginal Discs.

PubMed

Tsao, Chia-Kang; Ku, Hui-Yu; Lee, Yuan-Ming; Huang, Yu-Fen; Sun, Yi Henry

Continuous imaging of live tissues provides clear temporal sequence of biological events. The Drosophila imaginal discs have been popular experimental subjects for the study of a wide variety of biological phenomena, but long term culture that allows normal development has not been satisfactory. Here we report a culture method that can sustain normal development for 18 hours and allows live imaging. The method is validated in multiple discs and for cell proliferation, differentiation and migration. However, it does not support disc growth and cannot support cell proliferation for more than 7 to 12 hr. We monitored the cellular behavior of retinal basal glia in the developing eye disc and found that distinct glia type has distinct properties of proliferation and migration. The live imaging provided direct proof that wrapping glia differentiated from existing glia after migrating to the anterior front, and unexpectedly found that they undergo endoreplication before wrapping axons, and their nuclei migrate up and down along the axons. UV-induced specific labeling of a single carpet glia also showed that the two carpet glia membrane do not overlap and suggests a tiling or repulsion mechanism between the two cells. These findings demonstrated the usefulness of an ex vivo culture method and live imaging.

Protocol for Biomarker Ratio Imaging Microscopy with Specific Application to Ductal Carcinoma In situ of the Breast

PubMed Central

Clark, Andrea J.; Petty, Howard R.

2016-01-01

This protocol describes the methods and steps involved in performing biomarker ratio imaging microscopy (BRIM) using formalin fixed paraffin-embedded (FFPE) samples of human breast tissue. The technique is based on the acquisition of two fluorescence images of the same microscopic field using two biomarkers and immunohistochemical tools. The biomarkers are selected such that one biomarker correlates with breast cancer aggressiveness while the second biomarker anti-correlates with aggressiveness. When the former image is divided by the latter image, a computed ratio image is formed that reflects the aggressiveness of tumor cells while increasing contrast and eliminating path-length and other artifacts from the image. For example, the aggressiveness of epithelial cells may be assessed by computing ratio images of N-cadherin and E-cadherin images or CD44 and CD24 images, which specifically reflect the mesenchymal or stem cell nature of the constituent cells, respectively. This methodology is illustrated for tissue samples of ductal carcinoma in situ (DCIS) and invasive breast cancer. This tool should be useful in tissue studies of experimental cancer as well as the management of cancer patients. PMID:27857940

Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features.

PubMed

Su, Hang; Yin, Zhaozheng; Huh, Seungil; Kanade, Takeo

2013-10-01

Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches. Copyright © 2013 Elsevier B.V. All rights reserved.

Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments

PubMed Central

Sachs, Christian Carsten; Grünberger, Alexander; Helfrich, Stefan; Probst, Christopher; Wiechert, Wolfgang; Kohlheyer, Dietrich; Nöh, Katharina

2016-01-01

Background Microfluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuous cultivation and observation of individual cells over many generations in a highly parallelized manner. To date, the lack of fully automated image analysis software limits the practical applicability of the MM as a phenotypic screening tool. Results We present an image analysis pipeline for the automated processing of MM time lapse image stacks. The pipeline supports all analysis steps, i.e., image registration, orientation correction, channel/cell detection, cell tracking, and result visualization. Tailored algorithms account for the specialized MM layout to enable a robust automated analysis. Image data generated in a two-day growth study (≈ 90 GB) is analyzed in ≈ 30 min with negligible differences in growth rate between automated and manual evaluation quality. The proposed methods are implemented in the software molyso (MOther machine AnaLYsis SOftware) that provides a new profiling tool to analyze unbiasedly hitherto inaccessible large-scale MM image stacks. Conclusion Presented is the software molyso, a ready-to-use open source software (BSD-licensed) for the unsupervised analysis of MM time-lapse image stacks. molyso source code and user manual are available at https://github.com/modsim/molyso. PMID:27661996

Electron microscopy of whole cells in liquid with nanometer resolution

PubMed Central

de Jonge, N.; Peckys, D. B.; Kremers, G. J.; Piston, D. W.

2009-01-01

Single gold-tagged epidermal growth factor (EGF) molecules bound to cellular EGF receptors of fixed fibroblast cells were imaged in liquid with a scanning transmission electron microscope (STEM). The cells were placed in buffer solution in a microfluidic device with electron transparent windows inside the vacuum of the electron microscope. A spatial resolution of 4 nm and a pixel dwell time of 20 μs were obtained. The liquid layer was sufficiently thick to contain the cells with a thickness of 7 ± 1 μm. The experimental findings are consistent with a theoretical calculation. Liquid STEM is a unique approach for imaging single molecules in whole cells with significantly improved resolution and imaging speed over existing methods. PMID:19164524