Identification of gap junction blockers using automated fluorescence microscopy imaging

Gap junctions coordinate electrical signals and facilitate metabolic synchronization between cells. In this study, the authors have developed a novel assay for the identification of gap junction blockers using fluorescence microscopy imaging-based high-content screening technology. In the assay, the communication between neighboring cells through gap junctions was measured by following the redistribution of a fluorescent marker. The movement of calcein dye from dye-loaded donor cells to dye-free acceptor cells through gap junctions overexpressed on cell surface membranes was monitored using automated fluorescence microscopy imaging in a high-throughput compatible format. The fluorescence imaging technology consisted of automated focusing, image acquisition, image processing, and data mining. The authors have successfully performed a high-throughput screening of a 486,000- compound program with this assay, and they were able to identify false positives without additional experiments. Selective and pharmacologically interesting compounds were identified for further optimization.     

Identification of lysosomotropic compounds based on the distribution and size of lysosomes

Lysosomal accumulation of drugs with their specific physicochemical properties is of key importance to drug distribution in the body. Several attempts have been made to treat various human diseases by employing the accumulation of lysosomal drugs, and many methods to identify lysosomal accumulation of drugs have been proposed. Among those, the use of high-content screening has increased tremendously because of improved efficiency and accuracy as well as the development of automatic image acquisition and analytical techniques. Conventional methods to identify lysosomal accumulation of drugs by evaluating changes in the lysosomal area are unable to maximize the advantages of phenotypic high-content screening. Lysosomal distribution and the size of lysosomes are affected by lysosomal accumulating drugs. Therefore, we present image acquisition conditions and analytical methods to utilize lysosomal distribution and size as parameters for identifying lysosomal accumulating drugs. These two parameters will help to improve the reliability of the screening methods for identifying lysosomal accumulation of drugs by maximizing usage of information from image-based screening.     

High-Content Analysis of Sequential Events during the Early Phase of Influenza A Virus Infection

Influenza A virus (IAV) represents a worldwide threat to public health by causing severe morbidity and mortality every year. Due to high mutation rate, new strains of IAV emerge frequently. These IAVs are often drug-resistant and require vaccine reformulation. A promising approach to circumvent this problem is to target host cell determinants crucial for IAV infection, but dispensable for the cell. Several RNAi-based screens have identified about one thousand cellular factors that promote IAV infection. However, systematic analyses to determine their specific functions are lacking. To address this issue, we developed quantitative, imaging-based assays to dissect seven consecutive steps in the early phases of IAV infection in tissue culture cells. The entry steps for which we developed the assays were: virus binding to the cell membrane, endocytosis, exposure to low pH in endocytic vacuoles, acid-activated fusion of viral envelope with the vacuolar membrane, nucleocapsid uncoating in the cytosol, nuclear import of viral ribonucleoproteins, and expression of the viral nucleoprotein. We adapted the assays to automated microscopy and optimized them for high-content screening. To quantify the image data, we performed both single and multi-parametric analyses, in combination with machine learning. By time-course experiments, we determined the optimal time points for each assay. Our quality control experiments showed that the assays were sufficiently robust for high-content analysis. The methods we describe in this study provide a powerful high-throughput platform to understand the host cell processes, which can eventually lead to the discovery of novel anti-pathogen strategies.     

Toward high-content/high-throughput imaging and analysis of embryonic morphogenesis

In vivo study of embryonic morphogenesis tremendously benefits from recent advances in live microscopy and computational analyses. Quantitative and automated investigation of morphogenetic processes opens the field to high-content and high-throughput strategies. Following experimental workflow currently developed in cell biology, we identify the key challenges for applying such strategies in developmental biology. We review the recent progress in embryo preparation and manipulation, live imaging, data registration, image segmentation, feature computation, and data mining dedicated to the study of embryonic morphogenesis. We discuss a selection of pioneering studies that tackled the current methodological bottlenecks and illustrated the investigation of morphogenetic processes in vivo using quantitative and automated imaging and analysis of hundreds or thousands of cells simultaneously, paving the way for high-content/high-throughput strategies and systems analysis of embryonic morphogenesis.     

Cellular phenotype recognition for high-content RNA interference genome-wide screening

Genome-wide, cell-based screens using high-content screening (HCS) techniques and automated fluorescence microscopy generate thousands of high-content images that contain an enormous wealth of cell biological information. Such screens are key to the analysis of basic cell biological principles, such as control of cell cycle and cell morphology. However, these screens will ultimately only shed light on human disease mechanisms and potential cures if the analysis can keep up with the generation of data. A fundamental step toward automated analysis of high-content screening is to construct a robust platform for automatic cellular phenotype identification. The authors present a framework, consisting of microscopic image segmentation and analysis components, for automatic recognition of cellular phenotypes in the context of the Rho family of small GTPases. To implicate genes involved in Rac signaling, RNA interference (RNAi) was used to perturb gene functions, and the corresponding cellular phenotypes were analyzed for changes. The data used in the experiments are high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells stained with markers that allow visualization of DNA, polymerized actin filaments, and the constitutively activated Rho protein Rac(V12). The performance of this approach was tested using a cellular database that contained more than 1000 samples of 3 predefined cellular phenotypes, and the generalization error was estimated using a cross-validation technique. Moreover, the authors applied this approach to analyze the whole high-content fluorescence images of Drosophila cells for further HCS-based gene function analysis.     

Automated Detection of Soma Location and Morphology in Neuronal Network Cultures

Automated identification of the primary components of a neuron and extraction of its sub-cellular features are essential steps in many quantitative studies of neuronal networks. The focus of this paper is the development of an algorithm for the automated detection of the location and morphology of somas in confocal images of neuronal network cultures. This problem is motivated by applications in high-content screenings (HCS), where the extraction of multiple morphological features of neurons on large data sets is required. Existing algorithms are not very efficient when applied to the analysis of confocal image stacks of neuronal cultures. In addition to the usual difficulties associated with the processing of fluorescent images, these types of stacks contain a small number of images so that only a small number of pixels are available along the z-direction and it is challenging to apply conventional 3D filters. The algorithm we present in this paper applies a number of innovative ideas from the theory of directional multiscale representations and involves the following steps: (i) image segmentation based on support vector machines with specially designed multiscale filters; (ii) soma extraction and separation of contiguous somas, using a combination of level set method and directional multiscale filters. We also present an approach to extract the soma’s surface morphology using the 3D shearlet transform. Extensive numerical experiments show that our algorithms are computationally efficient and highly accurate in segmenting the somas and separating contiguous ones. The algorithms presented in this paper will facilitate the development of a high-throughput quantitative platform for the study of neuronal networks for HCS applications.     

The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging

The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.     

Automated reporter quantification in vivo: high-throughput screening method for reporter-based assays in zebrafish

Reporter-based assays underlie many high-throughput screening (HTS) platforms, but most are limited to in vitro applications. Here, we report a simple whole-organism HTS method for quantifying changes in reporter intensity in individual zebrafish over time termed, Automated Reporter Quantification in vivo (ARQiv). ARQiv differs from current "high-content" (e.g., confocal imaging-based) whole-organism screening technologies by providing a purely quantitative data acquisition approach that affords marked improvements in throughput. ARQiv uses a fluorescence microplate reader with specific detection functionalities necessary for robust quantification of reporter signals in vivo. This approach is: 1) Rapid; achieving true HTS capacities (i.e., >50,000 units per day), 2) Reproducible; attaining HTS-compatible assay quality (i.e., Z'-factors of ≥0.5), and 3) Flexible; amenable to nearly any reporter-based assay in zebrafish embryos, larvae, or juveniles. ARQiv is used here to quantify changes in: 1) Cell number; loss and regeneration of two different fluorescently tagged cell types (pancreatic beta cells and rod photoreceptors), 2) Cell signaling; relative activity of a transgenic Notch-signaling reporter, and 3) Cell metabolism; accumulation of reactive oxygen species. In summary, ARQiv is a versatile and readily accessible approach facilitating evaluation of genetic and/or chemical manipulations in living zebrafish that complements current "high-content" whole-organism screening methods by providing a first-tier in vivo HTS drug discovery platform.     

Quantification of Dynamic Morphological Drug Responses in 3D Organotypic Cell Cultures by Automated Image Analysis

Glandular epithelial cells differentiate into complex multicellular or acinar structures, when embedded in three-dimensional (3D) extracellular matrix. The spectrum of different multicellular morphologies formed in 3D is a sensitive indicator for the differentiation potential of normal, non-transformed cells compared to different stages of malignant progression. In addition, single cells or cell aggregates may actively invade the matrix, utilizing epithelial, mesenchymal or mixed modes of motility. Dynamic phenotypic changes involved in 3D tumor cell invasion are sensitive to specific small-molecule inhibitors that target the actin cytoskeleton. We have used a panel of inhibitors to demonstrate the power of automated image analysis as a phenotypic or morphometric readout in cell-based assays. We introduce a streamlined stand-alone software solution that supports large-scale high-content screens, based on complex and organotypic cultures. AMIDA (Automated Morphometric Image Data Analysis) allows quantitative measurements of large numbers of images and structures, with a multitude of different spheroid shapes, sizes, and textures. AMIDA supports an automated workflow, and can be combined with quality control and statistical tools for data interpretation and visualization. We have used a representative panel of 12 prostate and breast cancer lines that display a broad spectrum of different spheroid morphologies and modes of invasion, challenged by a library of 19 direct or indirect modulators of the actin cytoskeleton which induce systematic changes in spheroid morphology and differentiation versus invasion. These results were independently validated by 2D proliferation, apoptosis and cell motility assays. We identified three drugs that primarily attenuated the invasion and formation of invasive processes in 3D, without affecting proliferation or apoptosis. Two of these compounds block Rac signalling, one affects cellular cAMP/cGMP accumulation. Our approach supports the growing needs for user-friendly, straightforward solutions that facilitate large-scale, cell-based 3D assays in basic research, drug discovery, and target validation.     

A three-dimensional colocalization RNA interference screening platform to elucidate the alternative lengthening of telomeres pathway

A high-content colocalization RNA interference screen based on automatic three-color confocal fluorescence microscopy was developed to analyze the alternative lengthening of telomeres (ALT) pathway. Via this pathway telomerase-negative cancer cells can maintain their telomeres and with it their unlimited proliferative potential. A hallmark of ALT cells is the colocalization of promyelocytic leukemia (PML) nuclear bodies with telomeres to form ALT-associated PML nuclear bodies (APBs). In our screen, the presence of APBs was used as a marker to identify proteins required for the ALT mechanism. A cell-based assay and an automatic confocal image acquisition procedure were established. Using automatic image analysis based on 3D parametric intensity models to identify APBs, we conducted an unbiased and quantitative analysis of nine different candidate genes. A comparison with the literature and manual analysis of the gene knockdown demonstrates the reliability of our approach. It extends the available repertoire of high-content screening to studies of cellular colocalizations and allows the identification of candidate genes for the ALT mechanism that represent possible targets for cancer therapy.     

Comparison of search engine contributions in protein mass fingerprinting for protein identification

Peptide mass fingerprinting (PMF) is a valuable method for rapid and high-throughput protein identification using the proteomics approach. Automated search engines, such as Ms-Fit, Mascot, ProFound, and Peptldent, have facilitated protein identification through PMF. The potential to obtain a true MS protein identification result depends on the choice of algorithm as well as experimental factors that influence the information content in MS data. When mass spectral data are incomplete and/or have low mass accuracy, the “number of matches” approach may be inadequate for a useful identification. Several studies have evaluated factors influencing the quality of mass spectrometry (MS) experiments. Missed cleavages, posttranslational modifications of peptides and contaminants (e.g., keratin) are important factors that can affect the results of MS analyses by influencing the identification process as well as the quality of the MS spectra. We compared search engines frequently used to identify proteins fromHomo sapiens andHalobacterium salinarum by evaluating factors, including data-based and mass tolerance to develop an improved search engine for PMF. This study may provide information to help develop a more effective algorithm for protein identification in each species through PMF.     

Can plant-pollinator network metrics indicate environmental quality?

Plant-pollinator interaction networks may be more informative than the diversity of species in the evaluation of the effects of environmental change. Considering that networks vary with the integrity of ecosystems, their changes may help to predict the consequences of anthropogenic impacts on biodiversity and ecological processes. This characteristic highlights its use as environmental quality indicator. However, to employ interaction networks as ecological indicators it is necessary to identify the most sensitive metrics and understand how and why they vary with environmental changes. This review aimed to identify, in empirical studies, which network metrics have been evidenced as being more sensitive to changes in environmental quality. We analyzed published empirical studies, that applied the network approach on environmental quality gradients. In addition to the network metric behavior, we studied the interactions between them and possible causes of their variation. The available empirical data indicated that degree, nestedness and connectance did not have a simple, linear or unidirectional response to habitat degradation. Conversely, the metrics interaction asymmetry, d' (reciprocal specialization index of the species) showed the most consistent responses to environmental change. The role of the species changed, ranging between generalists and specialists under different conditions. In addition, specialist species with morphological and behavioral constraints were lost in worse environmental quality situations. The identity of interacting species and their role in the network, with a further specification of groups and interactions most affected, are the properties with greater potential to indicate changes in environmental quality. Most of the available studies focused on metrics at the network level, but several studies and this review indicate that the patterns at the network level can be better understood in the light of metrics analyzed at the species level. Our results provide information that enrich the network analysis, highlighting the need to consider important features that are often neglected. Discussions and information compiled here are important for deciding how to look at empirical data and what to look for, as well as to indicate some caveats when interpreting data on plant-pollinator interactions with a complex network approach. Network metrics can be good indicators of environmental quality if the underlying ecological causes of the numerical changes are carefully analyzed.     

Phaedra, a protocol-driven system for analysis and validation of high-content imaging and flow cytometry

High-content screening has brought new dimensions to cellular assays by generating rich data sets that characterize cell populations in great detail and detect subtle phenotypes. To derive relevant, reliable conclusions from these complex data, it is crucial to have informatics tools supporting quality control, data reduction, and data mining. These tools must reconcile the complexity of advanced analysis methods with the user-friendliness demanded by the user community. After review of existing applications, we realized the possibility of adding innovative new analysis options. Phaedra was developed to support workflows for drug screening and target discovery, interact with several laboratory information management systems, and process data generated by a range of techniques including high-content imaging, multicolor flow cytometry, and traditional high-throughput screening assays. The application is modular and flexible, with an interface that can be tuned to specific user roles. It offers user-friendly data visualization and reduction tools for HCS but also integrates Matlab for custom image analysis and the Konstanz Information Miner (KNIME) framework for data mining. Phaedra features efficient JPEG2000 compression and full drill-down functionality from dose-response curves down to individual cells, with exclusion and annotation options, cell classification, statistical quality controls, and reporting.     

Integration of Chemical and RNAi Multiparametric Profiles Identifies Triggers of Intracellular Mycobacterial Killing

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Highlights? A high-content chemical screen identifies modulators of intracellular mycobacteria ? Three compounds that kill bacteria by altering host processes were validated ? Multiparametric chemical and genetic data sets integrated to identify mode of action ? Modulation of autophagy and endosomal trafficking identified as mode of action     

High-content imaging of neutral lipid droplets with 1,6-diphenylhexatriene

Neutral lipid droplets (LDs) are dynamic lipid storage organelles found in all eukaryotic cells from yeast to mammals and higher plants. LDs are important to many physiological processes that include basic cellular maintenance, metabolism, and diverse medical pathologies. LD accumulation has been studied extensively by a range of methods, but particularly by microscopy with several fluorescent dyes extensively used for qualitative and quantitative imaging. Here, we compared established LD stains Nile Red and BODIPY 493/503 to the 4', 6-diamidino-2-phenylindole (DAPI)-range dye 1,6-diphenyl-1,3,5-hexatriene (DPH; excitation/emission λmax=350 nm/420 nm) using high-content image analysis. HeLa cells treated with oleic acid or vehicle were used to compare staining patterns between DPH and Nile Red as well as DPH and the LD protein adipophilin. DPH, Nile Red, and BODIPY 493/503 were compared as assay reagents in oleic acid dose-response experiments. Treatment of MCF-7 cells with sodium butyrate was used as a second cellular system for high-content analysis of LD formation. In this experimental context, we demonstrate the compatibility of DPH with GFP, a technical limitation of Nile Red and BODIPY 493/503 dyes. These data show that DPH has comparable sensitivity and specificity to that of Nile Red. Z'-factor analysis of dose-response experiments indicated that DPH and BODIPY 493/503 are well suited for quantitative analysis of LDs for high-throughput screening (HTS) applications.     

Robust hit identification by quality assurance and multivariate data analysis of a high-content, cell-based assay

Recent technological advances in high-content screening instrumentation have increased its ease of use and throughput, expanding the application of high-content screening to the early stages of drug discovery. However, high-content screens produce complex data sets, presenting a challenge for both extraction and interpretation of meaningful information. This shifts the high-content screening process bottleneck from the experimental to the analytical stage. In this article, the authors discuss different approaches of data analysis, using a phenotypic neurite outgrowth screen as an example. Distance measurements and hierarchical clustering methods lead to a profound understanding of different high-content screening readouts. In addition, the authors introduce a hit selection procedure based on machine learning methods and demonstrate that this method increases the hit verification rate significantly (up to a factor of 5), compared to conventional hit selection based on single readouts only.     

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam principles)

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed upon two primary needs for the wide use of quality metrics: (i) an evolving list of comprehensive quality metrics and (ii) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in Proteomics, Proteomics Clinical Applications, Journal of Proteome Research, and Molecular and Cellular Proteomics, as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.     

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles)

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: (1) an evolving list of comprehensive quality metrics and (2) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in the Journal of Proteome Research, Molecular and Cellular Proteomics, Proteomics, and Proteomics Clinical Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.     

Machine learning improves the precision and robustness of high-content screens: using nonlinear multiparametric methods to analyze screening results

Imaging-based high-content screens often rely on single cell-based evaluation of phenotypes in large data sets of microscopic images. Traditionally, these screens are analyzed by extracting a few image-related parameters and use their ratios (linear single or multiparametric separation) to classify the cells into various phenotypic classes. In this study, the authors show how machine learning-based classification of individual cells outperforms those classical ratio-based techniques. Using fluorescent intensity and morphological and texture features, they evaluated how the performance of data analysis increases with increasing feature numbers. Their findings are based on a case study involving an siRNA screen monitoring nucleoplasmic and nucleolar accumulation of a fluorescently tagged reporter protein. For the analysis, they developed a complete analysis workflow incorporating image segmentation, feature extraction, cell classification, hit detection, and visualization of the results. For the classification task, the authors have established a new graphical framework, the Advanced Cell Classifier, which provides a very accurate high-content screen analysis with minimal user interaction, offering access to a variety of advanced machine learning methods.