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1.  Quantitative Time-Lapse Fluorescence Microscopy in Single Cells 
The cloning of GFP 15 years ago revolutionized cell biology by permitting visualization of a wide range of molecular mechanisms within living cells. Though initially used to make largely qualitative assessments of protein levels and localizations, fluorescence microscopy has since evolved to become highly quantitative and high-throughput. Computational image analysis has catalyzed this evolution, enabling rapid and automated processing of large datasets. Here we review studies that combine time-lapse fluorescence microscopy and automated image analysis to investigate dynamic events at the single-cell level. We highlight examples where single-cell analysis provides unique mechanistic insights into cellular processes that cannot be otherwise resolved in bulk assays. Additionally, we discuss studies where quantitative microscopy facilitates the assembly of detailed 4D lineages in developing organisms. Finally, we describe recent advances in imaging technology, focusing especially on platforms that allow the simultaneous perturbation and quantitative monitoring of biological systems.
doi:10.1146/annurev.cellbio.042308.113408
PMCID: PMC3137897  PMID: 19575655
cell-to-cell variability; automated image analysis; lineage construction; microfluidic devices; fluorescent proteins
2.  Shedding Light on Filovirus Infection with High-Content Imaging 
Viruses  2012;4(8):1354-1371.
Microscopy has been instrumental in the discovery and characterization of microorganisms. Major advances in high-throughput fluorescence microscopy and automated, high-content image analysis tools are paving the way to the systematic and quantitative study of the molecular properties of cellular systems, both at the population and at the single-cell level. High-Content Imaging (HCI) has been used to characterize host-virus interactions in genome-wide reverse genetic screens and to identify novel cellular factors implicated in the binding, entry, replication and egress of several pathogenic viruses. Here we present an overview of the most significant applications of HCI in the context of the cell biology of filovirus infection. HCI assays have been recently implemented to quantitatively study filoviruses in cell culture, employing either infectious viruses in a BSL-4 environment or surrogate genetic systems in a BSL-2 environment. These assays are becoming instrumental for small molecule and siRNA screens aimed at the discovery of both cellular therapeutic targets and of compounds with anti-viral properties. We discuss the current practical constraints limiting the implementation of high-throughput biology in a BSL-4 environment, and propose possible solutions to safely perform high-content, high-throughput filovirus infection assays. Finally, we discuss possible novel applications of HCI in the context of filovirus research with particular emphasis on the identification of possible cellular biomarkers of virus infection.
doi:10.3390/v4081354
PMCID: PMC3446768  PMID: 23012631
filoviruses; High-Content Imaging; therapeutics; host-pathogen interactions; phenotype
3.  A Novel Multiplex Cell Viability Assay for High-Throughput RNAi Screening 
PLoS ONE  2011;6(12):e28338.
Cell-based high-throughput RNAi screening has become a powerful research tool in addressing a variety of biological questions. In RNAi screening, one of the most commonly applied assay system is measuring the fitness of cells that is usually quantified using fluorescence, luminescence and absorption-based readouts. These methods, typically implemented and scaled to large-scale screening format, however often only yield limited information on the cell fitness phenotype due to evaluation of a single and indirect physiological indicator. To address this problem, we have established a cell fitness multiplexing assay which combines a biochemical approach and two fluorescence-based assaying methods. We applied this assay in a large-scale RNAi screening experiment with siRNA pools targeting the human kinome in different modified HEK293 cell lines. Subsequent analysis of ranked fitness phenotypes assessed by the different assaying methods revealed average phenotype intersections of 50.7±2.3%–58.7±14.4% when two indicators were combined and 40–48% when a third indicator was taken into account. From these observations we conclude that combination of multiple fitness measures may decrease false-positive rates and increases confidence for hit selection. Our robust experimental and analytical method improves the classical approach in terms of time, data comprehensiveness and cost.
doi:10.1371/journal.pone.0028338
PMCID: PMC3230607  PMID: 22162763
4.  Workflow and metrics for image quality control in large-scale high-content screens 
Journal of biomolecular screening  2011;17(2):266-274.
Automated microscopes have enabled the unprecedented collection of images at a rate that precludes visual inspection. Automated image analysis is required to identify interesting samples and extract quantitative information for high content screening (HCS). However, researchers are impeded by the lack of metrics and software tools to identify image-based aberrations that pollute data, limiting an experiment's quality. We have developed and validated approaches to identify those image acquisition artifacts that prevent optimal extraction of knowledge from high-throughput microscopy experiments. We have implemented these as a versatile, open-source toolbox of algorithms and metrics readily usable by biologists to improve data quality in a wide variety of biological experiments.
doi:10.1177/1087057111420292
PMCID: PMC3593271  PMID: 21956170
5.  Unsupervised automated high throughput phenotyping of RNAi time-lapse movies 
BMC Bioinformatics  2013;14:292.
Background
Gene perturbation experiments in combination with fluorescence time-lapse cell imaging are a powerful tool in reverse genetics. High content applications require tools for the automated processing of the large amounts of data. These tools include in general several image processing steps, the extraction of morphological descriptors, and the grouping of cells into phenotype classes according to their descriptors. This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens.
Results
We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene’s function.
Conclusion
Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes.
doi:10.1186/1471-2105-14-292
PMCID: PMC3851277  PMID: 24090185
6.  An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy 
BMC Bioinformatics  2013;14:297.
Background
In recent years, high-throughput microscopy has emerged as a powerful tool to analyze cellular dynamics in an unprecedentedly high resolved manner. The amount of data that is generated, for example in long-term time-lapse microscopy experiments, requires automated methods for processing and analysis. Available software frameworks are well suited for high-throughput processing of fluorescence images, but they often do not perform well on bright field image data that varies considerably between laboratories, setups, and even single experiments.
Results
In this contribution, we present a fully automated image processing pipeline that is able to robustly segment and analyze cells with ellipsoid morphology from bright field microscopy in a high-throughput, yet time efficient manner. The pipeline comprises two steps: (i) Image acquisition is adjusted to obtain optimal bright field image quality for automatic processing. (ii) A concatenation of fast performing image processing algorithms robustly identifies single cells in each image. We applied the method to a time-lapse movie consisting of ∼315,000 images of differentiating hematopoietic stem cells over 6 days. We evaluated the accuracy of our method by comparing the number of identified cells with manual counts. Our method is able to segment images with varying cell density and different cell types without parameter adjustment and clearly outperforms a standard approach. By computing population doubling times, we were able to identify three growth phases in the stem cell population throughout the whole movie, and validated our result with cell cycle times from single cell tracking.
Conclusions
Our method allows fully automated processing and analysis of high-throughput bright field microscopy data. The robustness of cell detection and fast computation time will support the analysis of high-content screening experiments, on-line analysis of time-lapse experiments as well as development of methods to automatically track single-cell genealogies.
doi:10.1186/1471-2105-14-297
PMCID: PMC3850979  PMID: 24090363
7.  Automated Proteome-Wide Determination of Subcellular Location Using High Throughput Microscopy 
A major source of information for identifying subcellular location on a proteome-wide basis will be imaging of tagged proteins in living cells using fluorescence microscopy. We have previously developed automated systems to interpret images from such experiments and demonstrated that they can perform as well or better than visual inspection. Recent work demonstrates that these methods can be applied to large collections of images from sources as diverse as yeast expressing GFP-tagged proteins and human tissues imaged by immunocytochemistry. A distinct but related task is learning what location patterns exist. We have demonstrated clustering of mouse proteins into subcellular location families that share a statistically indistinguishable pattern. To communicate each pattern, we have developed approaches to learning generative models of subcellular patterns. Integration of high-throughput microscopy and automated model building with cell modeling systems will permit accurate, well-structured information on subcellular location to be incorporated into systems biology efforts.
doi:10.1109/ISBI.2008.4540994
PMCID: PMC2901541  PMID: 20622996
Location proteomics; tissue micro-array; pattern recognition; generative models; high throughput microscopy
8.  A high-throughput screening system for barley/powdery mildew interactions based on automated analysis of light micrographs 
BMC Plant Biology  2008;8:6.
Background
To find candidate genes that potentially influence the susceptibility or resistance of crop plants to powdery mildew fungi, an assay system based on transient-induced gene silencing (TIGS) as well as transient over-expression in single epidermal cells of barley has been developed. However, this system relies on quantitative microscopic analysis of the barley/powdery mildew interaction and will only become a high-throughput tool of phenomics upon automation of the most time-consuming steps.
Results
We have developed a high-throughput screening system based on a motorized microscope which evaluates the specimens fully automatically. A large-scale double-blind verification of the system showed an excellent agreement of manual and automated analysis and proved the system to work dependably. Furthermore, in a series of bombardment experiments an RNAi construct targeting the Mlo gene was included, which is expected to phenocopy resistance mediated by recessive loss-of-function alleles such as mlo5. In most cases, the automated analysis system recorded a shift towards resistance upon RNAi of Mlo, thus providing proof of concept for its usefulness in detecting gene-target effects.
Conclusion
Besides saving labor and enabling a screening of thousands of candidate genes, this system offers continuous operation of expensive laboratory equipment and provides a less subjective analysis as well as a complete and enduring documentation of the experimental raw data in terms of digital images. In general, it proves the concept of enabling available microscope hardware to handle challenging screening tasks fully automatically.
doi:10.1186/1471-2229-8-6
PMCID: PMC2262080  PMID: 18215267
9.  Bacterial cell identification in differential interference contrast microscopy images 
BMC Bioinformatics  2013;14:134.
Background
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.
Results
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.
Conclusions
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.
doi:10.1186/1471-2105-14-134
PMCID: PMC3734120  PMID: 23617824
10.  Image analysis in fluorescence microscopy: Bacterial dynamics as a case study 
Fluorescence microscopy is the primary tool for studying complex processes inside individual living cells. Technical advances in both molecular biology and microscopy have made it possible to image cells from many genetic and environmental backgrounds. These images contain a vast amount of information, but this information is often hidden behind various sources of noise, convoluted with other information, and stochastic in nature. Accessing the desired biological information therefore requires new tools of computational image analysis and modeling. Here, we review some of the recent advances in computational analysis of images obtained from fluorescence microscopy, focusing on bacterial systems. We emphasize techniques that are readily available to molecular and cell biologists but also point out examples where problem-specific image analyses are necessary. Thus, image analysis is not only a toolkit to be applied to new images but is an integral part of the design and implementation of a microscopy experiment.
doi:10.1002/bies.201100148
PMCID: PMC4056673  PMID: 22415868
bacteria; computational image analysis; fluorescence microscopy
11.  Imaging the boundaries—innovative tools for microscopy of living cells and real-time imaging 
Journal of Chemical Biology  2008;1(1-4):3-15.
Recently, light microscopy moved back into the spotlight, which is mainly due to the development of revolutionary technologies for imaging real-time events in living cells. It is truly fascinating to see enzymes “at work” and optically acquired images certainly help us to understand biological processes better than any abstract measurements. This review aims to point out elegant examples of recent cell-biological imaging applications that have been developed with a chemical approach. The discussed technologies include nanoscale fluorescence microscopy, imaging of model membranes, automated high-throughput microscopy control and analysis, and fluorescent probes with a special focus on visualizing enzyme activity, free radicals, and protein–protein interaction designed for use in living cells.
doi:10.1007/s12154-008-0004-4
PMCID: PMC2698318  PMID: 19568794
Fluorescence microscopy; Live-cell imaging; Fluorescent probes; Real-time imaging
12.  Imaging the boundaries—innovative tools for microscopy of living cells and real-time imaging 
Journal of Chemical Biology  2008;1(1-4):3-15.
Recently, light microscopy moved back into the spotlight, which is mainly due to the development of revolutionary technologies for imaging real-time events in living cells. It is truly fascinating to see enzymes “at work” and optically acquired images certainly help us to understand biological processes better than any abstract measurements. This review aims to point out elegant examples of recent cell-biological imaging applications that have been developed with a chemical approach. The discussed technologies include nanoscale fluorescence microscopy, imaging of model membranes, automated high-throughput microscopy control and analysis, and fluorescent probes with a special focus on visualizing enzyme activity, free radicals, and protein–protein interaction designed for use in living cells.
doi:10.1007/s12154-008-0004-4
PMCID: PMC2698318  PMID: 19568794
Fluorescence microscopy; Live-cell imaging; Fluorescent probes; Real-time imaging
13.  Automated microscopy for high-content RNAi screening 
The Journal of Cell Biology  2010;188(4):453-461.
Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs.
doi:10.1083/jcb.200910105
PMCID: PMC2828931  PMID: 20176920
14.  High-Speed Imaging of Amoeboid Movements Using Light-Sheet Microscopy 
PLoS ONE  2012;7(12):e50846.
Light-sheet microscopy has been developed as a powerful tool for live imaging in biological studies. The efficient illumination of specimens using light-sheet microscopy makes it highly amenable to high-speed imaging. We therefore applied this technology to the observation of amoeboid movements, which are too rapid to capture with conventional microscopy. To simplify the setup of the optical system, we utilized the illumination optics from a conventional confocal laser scanning microscope. Using this set-up we achieved high-speed imaging of amoeboid movements. Three-dimensional images were captured at the recording rate of 40 frames/s and clearly outlined the fine structures of fluorescent-labeled amoeboid cellular membranes. The quality of images obtained by our system was sufficient for subsequent quantitative analysis for dynamics of amoeboid movements. This study demonstrates the application of light-sheet microscopy for high-speed imaging of biological specimens.
doi:10.1371/journal.pone.0050846
PMCID: PMC3515486  PMID: 23227214
15.  Fluorescence Anisotropy and Resonance Energy Transfer: Powerful Tools for Measuring Real Time Protein Dynamics in a Physiological Environment 
Current opinion in pharmacology  2010;10(6):731-737.
Fluorescence spectroscopy/microscopy is a versatile method for examining protein dynamics in vitro and in vivo that can be combined with other techniques to simultaneously examine complementary pharmacological parameters. The following review will highlight the advantages and challenges of using fluorescence spectroscopic methods for examining protein dynamics with a special emphasis on fluorescence resonance energy transfer and fluorescence anisotropy. Both of these methods are amenable to measurements on an ensemble of molecules as well as at the single molecule level, in live cells and in high throughput screening assays, providing a powerful set of tools to aid in the design and testing of new drugs under a variety of experimental conditions.
doi:10.1016/j.coph.2010.09.013
PMCID: PMC2981669  PMID: 20971683
16.  Cell Population Tracking and Lineage Construction with Spatiotemporal Context 1 
Medical image analysis  2008;12(5):546-566.
Automated visual-tracking of cell populations in vitro using phase contrast time-lapse microscopy enables quantitative, systematic and high-throughput measurements of cell behaviors. These measurements include the spatiotemporal quantification of cell migration, mitosis, apoptosis, and the construction of cell lineages. The combination of low signal-to-noise ratio of phase contrast microscopy images, high and varying densities of the cell cultures, topological complexities of cell shapes, and wide range of cell behaviors pose many challenges to existing tracking techniques. This paper presents a fully-automated multi-target tracking system that can efficiently cope with these challenges while simultaneously tracking and analyzing thousands of cells observed using time-lapse phase contrast microscopy. The system combines bottom-up and top-down image analysis by integrating multiple collaborative modules, which exploit a fast geometric active contour tracker in conjunction with adaptive interacting multiple models (IMM) motion filtering and spatiotemporal trajectory optimization. The system, which was tested using a variety of cell populations, achieved tracking accuracy in the range of 86.9%–92.5%.
doi:10.1016/j.media.2008.06.001
PMCID: PMC2670445  PMID: 18656418
Cell tracking; level set; jump Markov systems; IMM filter; quasi-Bayes estimation; linear programming; phase contrast; time-lapse microscopy; stem cell
17.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes 
Genome Biology  2006;7(10):R100.
CellProfiler, the first free, open-source system for flexible and high-throughput cell image analysis is described.
Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).
doi:10.1186/gb-2006-7-10-r100
PMCID: PMC1794559  PMID: 17076895
18.  Automated fluorescence lifetime imaging plate reader and its application to Förster resonant energy transfer readout of Gag protein aggregation 
Journal of Biophotonics  2012;6(5):398-408.
Fluorescence lifetime measurements can provide quantitative readouts of local fluorophore environment and can be applied to biomolecular interactions via Förster resonant energy transfer (FRET). Fluorescence lifetime imaging (FLIM) can therefore provide a high content analysis (HCA) modality to map protein-protein interactions (PPIs) with applications in drug discovery, systems biology and basic research. We present here an automated multiwell plate reader able to perform rapid unsupervised optically sectioned FLIM of fixed and live biological samples and illustrate its potential to assay PPIs through application to Gag protein aggregation during the HIV life cycle. We demonstrate both hetero-FRET and homo-FRET readouts of protein aggregation and report the first quantitative evaluation of a FLIM HCA assay by generating dose response curves through addition of an inhibitor of Gag myristoylation. Z ′ factors exceeding 0.6 are realised for this FLIM FRET assay.
Fluorescence lifetime plate map with representative images of high and low FRET cells and corresponding dose response plot.
doi:10.1002/jbio.201200185
PMCID: PMC3660788  PMID: 23184449
fluorescence lifetime imaging microscopy (FLIM); FRET; HIV-1 Gag; HCA; protein-protein interactions
19.  A functional genomic analysis of cell morphology using RNA interference 
Journal of Biology  2003;2(4):27.
Background
The diversity of metazoan cell shapes is influenced by the dynamic cytoskeletal network. With the advent of RNA-interference (RNAi) technology, it is now possible to screen systematically for genes controlling specific cell-biological processes, including those required to generate distinct morphologies.
Results
We adapted existing RNAi technology in Drosophila cell culture for use in high-throughput screens to enable a comprehensive genetic dissection of cell morphogenesis. To identify genes responsible for the characteristic shape of two morphologically distinct cell lines, we performed RNAi screens in each line with a set of double-stranded RNAs (dsRNAs) targeting 994 predicted cell shape regulators. Using automated fluorescence microscopy to visualize actin filaments, microtubules and DNA, we detected morphological phenotypes for 160 genes, one-third of which have not been previously characterized in vivo. Genes with similar phenotypes corresponded to known components of pathways controlling cytoskeletal organization and cell shape, leading us to propose similar functions for previously uncharacterized genes. Furthermore, we were able to uncover genes acting within a specific pathway using a co-RNAi screen to identify dsRNA suppressors of a cell shape change induced by Pten dsRNA.
Conclusions
Using RNAi, we identified genes that influence cytoskeletal organization and morphology in two distinct cell types. Some genes exhibited similar RNAi phenotypes in both cell types, while others appeared to have cell-type-specific functions, in part reflecting the different mechanisms used to generate a round or a flat cell morphology.
doi:10.1186/1475-4924-2-27
PMCID: PMC333409  PMID: 14527345
20.  Total Internal Reflection Fluorescence Flow Cytometry 
Analytical chemistry  2008;80(24):9840-9844.
Total internal reflection fluorescence microscopy (TIRFM) has been widely used to explore biological events that are close to the cell membrane by illuminating fluorescent molecules using the evanescent wave. However, TIRFM is typically limited to the examination of a low number of cells and the results do not reveal potential heterogeneity in the cell population. In this report, we develop an analytical tool referred to as total internal reflection fluorescence flow cytometry (TIRF-FC) to examine the region of the cell membrane with a throughput of ~100–150 cells/s and single cell resolution. We use an elastomeric valve that is partially closed to force flowing cells in contact with the glass surface where the evanescent field resides. We demonstrate that TIRF-FC is able to detect the differences in the subcellular location of an intracellular fluorescent protein. Proper data processing and analysis allows TIRF-FC to be quantitative. With the high throughput, TIRF-FC will be a very useful tool for generating information on cell populations with events and dynamics close to the cell surface.
doi:10.1021/ac801940w
PMCID: PMC2798166  PMID: 19007249
21.  Automated image-based phenotypic analysis in zebrafish embryos 
Presently, the zebrafish is the only vertebrate model compatible with contemporary paradigms of drug discovery. Zebrafish embryos are amenable to automation necessary for high-throughput chemical screens, and optical transparency makes them potentially suited for image-based screening. However, the lack of tools for automated analysis of complex images presents an obstacle to utilizing the zebrafish as a high-throughput screening model. We have developed an automated system for imaging and analyzing zebrafish embryos in multi-well plates regardless of embryo orientation and without user intervention. Images of fluorescent embryos were acquired on a high-content reader and analyzed using an artificial intelligence-based image analysis method termed Cognition Network Technology (CNT). CNT reliably detected transgenic fluorescent embryos (Tg(fli1:EGFP)y1) arrayed in 96-well plates and quantified intersegmental blood vessel development in embryos treated with small molecule inhibitors of anigiogenesis. The results demonstrate it is feasible to adapt image-based high-content screening methodology to measure complex whole organism phenotypes.
doi:10.1002/dvdy.21892
PMCID: PMC2861575  PMID: 19235725
cognition network technology; high-content screening; angiogenesis; pironetin; zebrafish
22.  THE RABIT: A RAPID AUTOMATED BIODOSIMETRY TOOL FOR RADIOLOGICAL TRIAGE 
Health physics  2010;98(2):209-217.
In response to the recognized need for high throughput biodosimetry methods for use after large scale radiological events, a logical approach is complete automation of standard biodosimetric assays that are currently performed manually. We describe progress to date on the RABIT (Rapid Automated BIodosimetry Tool), designed to score micronuclei or γ-H2AX fluorescence in lymphocytes derived from a single drop of blood from a fingerstick. The RABIT system is designed to be completely automated, from the input of the capillary blood sample into the machine, to the output of a dose estimate. Improvements in throughput are achieved through use of a single drop of blood, optimization of the biological protocols for in-situ analysis in multi-well plates, implementation of robotic plate and liquid handling, and new developments in high-speed imaging. Automating well-established bioassays represents a promising approach to high-throughput radiation biodosimetry, both because high throughputs can be achieved, but also because the time to deployment is potentially much shorter than for a new biological assay. Here we describe the development of each of the individual modules of the RABIT system, and show preliminary data from key modules. Ongoing is system integration, followed by calibration and validation.
doi:10.1097/HP.0b013e3181ab3cb6
PMCID: PMC2923588  PMID: 20065685
radiation biodosimetry; high-throughput; complete automation; micronucleus; γ-H2AX
23.  An image score inference system for RNAi genome-wide screening based on fuzzy mixture regression modeling 
With recent advances in fluorescence microscopy imaging techniques and methods of gene knock down by RNA interference (RNAi), genome-scale high-content screening (HCS) has emerged as a powerful approach to systematically identify all parts of complex biological processes. However, a critical barrier preventing fulfillment of the success is the lack of efficient and robust methods for automating RNAi image analysis and quantitative evaluation of the gene knock down effects on huge volume of HCS data. Facing such opportunities and challenges, we have started investigation of automatic methods towards the development of a fully automatic RNAi-HCS system. Particularly important are reliable approaches to cellular phenotype classification and image-based gene function estimation.
We have developed a HCS analysis platform that consists of two main components: fluorescence image analysis and image scoring. For image analysis, we used a two-step enhanced watershed method to extract cellular boundaries from HCS images. Segmented cells were classified into several predefined phenotypes based on morphological and appearance features. Using statistical characteristics of the identified phenotypes as a quantitative description of the image, a score is generated that reflects gene function. Our scoring model integrates fuzzy gene class estimation and single regression models. The final functional score of an image was derived using the weighted combination of the inference from several support vector-based regression models. We validated our phenotype classification method and scoring system on our cellular phenotype and gene database with expert ground truth labeling.
We built a database of high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells that were treated with RNAi to perturb gene function. The proposed informatics system for microscopy image analysis is tested on this database. Both of the two main components, automated phenotype classification and image scoring system, were evaluated. The robustness and efficiency of our system were validated in quantitatively predicting the biological relevance of genes.
doi:10.1016/j.jbi.2008.04.007
PMCID: PMC2763194  PMID: 18547870
High-content screening; Image score inference
24.  High-throughput, single-cell NF-κB dynamics 
Summary
Single cells in a population often respond differently to perturbations in the environment. Live-cell microscopy has enabled scientists to observe these differences at the single-cell level. Some advantages of live-cell imaging over population-based methods include better time resolution, higher sensitivity, automation, and richer datasets. One specific area where live-cell microscopy has made a significant impact is the field of NF-κB signaling dynamics, and recent efforts have focused on making live-cell imaging of these dynamics more high throughput. We highlight the major aspects of increasing throughput and describe a current system that can monitor, image and analyze the NF-κB activation of thousands of single cells in parallel.
doi:10.1016/j.gde.2010.08.005
PMCID: PMC2982878  PMID: 20846851
25.  Fast automatic quantitative cell replication with fluorescent live cell imaging 
BMC Bioinformatics  2012;13:21.
Background
live cell imaging is a useful tool to monitor cellular activities in living systems. It is often necessary in cancer research or experimental research to quantify the dividing capabilities of cells or the cell proliferation level when investigating manipulations of the cells or their environment. Manual quantification of fluorescence microscopic image is difficult because human is neither sensitive to fine differences in color intensity nor effective to count and average fluorescence level among cells. However, auto-quantification is not a straightforward problem to solve. As the sampling location of the microscopy changes, the amount of cells in individual microscopic images varies, which makes simple measurement methods such as the sum of stain intensity values or the total number of positive stain within each image inapplicable. Thus, automated quantification with robust cell segmentation techniques is required.
Results
An automated quantification system with robust cell segmentation technique are presented. The experimental results in application to monitor cellular replication activities show that the quantitative score is promising to represent the cell replication level, and scores for images from different cell replication groups are demonstrated to be statistically significantly different using ANOVA, LSD and Tukey HSD tests (p-value < 0.01). In addition, the technique is fast and takes less than 0.5 second for high resolution microscopic images (with image dimension 2560 × 1920).
Conclusion
A robust automated quantification method of live cell imaging is built to measure the cell replication level, providing a robust quantitative analysis system in fluorescent live cell imaging. In addition, the presented unsupervised entropy based cell segmentation for live cell images is demonstrated to be also applicable for nuclear segmentation of IHC tissue images.
doi:10.1186/1471-2105-13-21
PMCID: PMC3359210  PMID: 22292799

Results 1-25 (953122)