High-Content Imaging and Informatics: A Joint Special Issue with Society for Biomolecular Imaging and Informatics and SLAS
Year: 2020
Type: editorial
Abstract: This Society of Biomolecular Imaging and Informatics (SBI2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar; https://sbi2.org/) special issue of SLAS Discovery was originally planned to coincide with the seventh annual SBI2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar conference and exhibit show (SBI2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar High Content 2020) from September 15 to 17 at the University of Pittsburgh. Due to the COVID-19 pandemic and our commitment to the health and safety of the community, the SBI2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar board of directors canceled the in-person SBI2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar High Content 2020 meeting in Pittsburgh. SBI2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar will instead be hosting the annual meeting in a virtual setting, as a free event, going live on September 16 and 17, 2020 (https://sbi2.org/2020-virtual-meeting-letter/), registration open June 1, 2020. This special issue of SLAS Discovery contains 13 peer-reviewed papers, including 10 original research articles, 1 perspective, a technical note, and an application note. Many of these papers were recruited directly from the SBI2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar membership and high-content screening (HCS) vendors, although some were submitted in response to a general request for papers issued to both SLAS and SBI2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar. The papers cover a wide range of HCS and analysis applications, including three-dimensional (3D) cell culture models, phenotypic screening strategies, deep learning for image analysis, androgen receptor (AR) screening of environmentally relevant chemicals, use of convergent biosciences to study cancer, an assay to quantify misfolded protein accumulation on aggresomes, and computational identification of kinases that control axon growth in mouse. Shortly after its founding in 2012, SBI2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar began co-hosting the HCS/HCA Data and Informatics Special Interest Group (SIG) session from 2013 to 2020 at SLAS’s annual convention and exhibition meeting. In this edition, Fong et al.1Fong E.J. Strelez C. Mumenthaler S.M. A Perspective on Expanding Our Understanding of Cancer Treatments by Integrating Approaches from the Biological and Physical Sciences.SLAS Discov. 2020; 27: 672-683Google Scholar present “A Perspective on Expanding Our Understanding of Cancer Treatments by Integrating Two Approaches from the Biological and Physical Sciences,” the discussion topic of the 2019 SIG led by Dr. Shannon Mumenthaler. Multicellular systems such as cancer suffer from immense complexity, and it is important to capture the heterogeneity to achieve a deeper understanding of the underlying biology and develop effective treatment strategies. At the SIG, academic and industry leaders engaged in discussions surrounding what biological model systems are appropriate to study cancer complexity, what assays are necessary to interrogate this complexity, and how physical sciences approaches may be useful to detangle this complexity. The authors highlight the utility of mathematical models in predicting cancer progression and treatment response when tightly integrated with reproducible, quantitative, and dynamic biological measurements achieved using HCS concentration–response screening (quantitative high-throughput screening [qHTS]) and analysis. They discuss the impetus for convergent biosciences bringing new perspectives to cancer research and therapeutics. Two of the papers in this issue focus on the AR, a member of the nuclear receptor family of transcription factors (TFs) and central regulator of male reproduction that is involved in many pathophysiological processes. Stossi and coworkers describe a single-cell distribution analysis of AR levels by high-throughput microscopy in cell models.2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar Cell-to-cell variation of protein expression in genetically homogeneous populations is a biological trait often neglected in HTS campaigns and rarely used to characterize chemicals. The authors describe the capture of single-cell distributions of AR nuclear levels to evaluate assay reproducibility and to characterize responses to a small subset of chemicals. The authors describe a quality control metric that accounts for the distribution of single-cell data, and how it changes after treatment. Exposure to 45 Environmental Protection Agency (EPA)-provided chemicals that included many endocrine disrupting chemicals (EDCs) was then studied. Data from six compounds were then integrated with orthogonal assays to show that quantitative single-cell distribution analysis of AR protein levels is a sensitive and reproducible method to detect the potential androgenic and antiandrogenic action of environmentally relevant chemicals. Human health is at risk due to environmental exposures to a wide range of chemical toxicants and EDCs. The U.S. EPA has been pursuing new in vitro HTS assays and computational models to characterize EDCs. Szafran and coauthors describe the application of a mechanistic high-content analysis assay using a chimeric androgen receptor (ARER) that rapidly characterizes androgenic chemicals.3Szafran A.T. Bolt M.J. Obkirchner C.E. et al.A Mechanistic High-Content Analysis Assay Using Chimeric Androgen Receptor That Rapidly Characterizes Androgenic Chemicals.SLAS Discov. 2020; 27: 695-708Google Scholar The high-content assay (HCA)-based GFP-ER:PRLHeLa assay allows for direct visualization of estrogen receptor binding to DNA regulatory elements. The authors describe the characterization of a modified functional assay based on the stable expression of ARER, wherein a region containing the native AR DNA-binding domain (DBD) was replaced with the ERα DBD. They demonstrate that an AR agonist DHT induces GFP-ARER nuclear translocation, PRL promoter binding, and transcriptional activity at physiologically relevant concentrations (<1 nM). In contrast, the AR antagonist bicalutamide induced only nuclear translocation of the GFP-ARER receptor and estradiol failed to induce chromatin binding, indicating androgen specificity. An HCS screen of reference chemicals from the EPA and ATSDR identified and mechanistically grouped known (anti-)androgens. The GFP-ARER cell model was then used to screen for potential antiandrogens in environmental samples in collaboration with the Houston Ship Channel/Galveston Bay TAMU EPA Superfund Research Program. The chromatin-binding GFP-ARER model represents a selective tool for rapidly identifying androgenic activity associated with drugs, chemicals, and environmental samples. 3D cell culture models that better recapitulate the physiological environments of human diseases are being deployed for drug discovery and development. 3D models exhibit improved morphology characteristics, cellular complexity, longer life spans in culture, and higher physiological relevance than two-dimensional (2D) cell culture models. HCS of 3D models has the potential to provide valuable information to untangle disease pathophysiology and interrogate drug responses. However, the practicalities of transitioning from 2D to 3D cultures for HCS studies poses challenges with respect to model and assay development, automation and instrumentation, image acquisition and analysis, and resources. Wardwell-Swanson et al. present a framework for optimizing high-content imaging of 3D models for drug discovery.4Wardwell-Swanson J. Suzuki M. Dowell K.G. et al.A Framework for Optimizing High-Content Imaging of 3D Models for Drug Discovery.SLAS Discov. 2020; 27: 709-722Google Scholar They describe three case studies of scaffold-free, multicellular spheroid models with scalable, automation-compatible plate technology enabling image-based applications: investigation of lipid droplet accumulation in a human liver nonalcoholic steatohepatitis (NASH) model, real-time immune cell interactions in a multicellular 3D lung cancer model, and HTS in a 3D co-culture model of gastric carcinoma. The proof-of-concept studies presented provide evidence of the potential for high-resolution image-based analysis of 3D spheroid models for drug discovery, and the application of cell-level and temporal-spatial analyses to explore the multicellular features of 3D models by HCS and more complex, lower-throughput microphysiological systems of organ networks. Kessel and Chan describe a high-throughput image cytometry method for the formation, morphometric, and viability analysis of drug-treated mammospheres (MMSs).5Kessel S.L. Chan L.L.-Y. A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres.SLAS Discov. 2020; 27: 723-733Google Scholar Nonadherent MMS assays have been used to investigate cancer stem cell (CSC) activities in breast cancers that can form tumorspheres (TSs) and maintain tumor growth. They have also been used to assess the effects of drug treatment on TSs formed from primary cancer cells or cell lines. The authors describe the development of a method using the Celigo Image Cytometer to characterize MMS formation and morphology in controlled conditions, and to evaluate drug effects on viability using calcein AM and propidium iodide staining. MMS formation and morphometric analysis of adherent and nonadherent propagation of four breast cancer cell lines are presented together with the effects of exposure to four drugs. They describe MMS growth and morphometric differences between adherent and nonadherent cell line propagation, among the four cell lines, and in response to drug exposure. Esquer and colleagues report on advanced HCS applications of clonogenicity in cancer.6Esquer H. Zhou Q. Abraham A.D. et al.Advanced High-Content-Screening Applications of Clonogenicity in Cancer.SLAS Discov. 2020; 27: 734-743Google Scholar Clonogenic assays have been used to observe the effects of radiation on cell survival, particularly cancer cells, and as a measure of CSC stemness where a single CSC has the ability to grow into a colony. CSCs are highly tumorigenic, have an unlimited proliferation potential with the capacity to generate tumor metastasis, and exhibit resistance to cytotoxic and targeted chemotherapy. The authors describe an HCS method to quantify the number and size of colonies in 2D and 3D culture models and to distinguish colonies based on fluorescent markers imaged on an Opera Phenix HCS platform. They describe how to scan at low magnification and rescan at a higher magnification to capture in greater detail colonies or even single cells of interest. Given the complexity of CSCs and their clinical relevance, these new methods provide a way to more effectively study and characterize CSC mechanisms that allow them to proliferate and persist, and for the discovery and development of therapies that can more effectively target these populations. Kim et al. present a comparison of cell and organoid-level analysis of patient-derived tumor organoids (PDTOs) to evaluate cell growth dynamics and drug response.7Kim S. Choung S. Sun R.X. et al.Comparison of Cell and Organoid-Level Analysis of Patient-Derived 3D Organoids to Evaluate Tumor Cell Growth Dynamics and Drug Response.SLAS Discov. 2020; 27: 744-754Google Scholar These organoids possess unique features given their heterogeneity in size, shape, and growth patterns. Using dynamic confocal live-cell imaging and image analysis methods, the authors examined tumor cell growth rates and drug responses in colorectal cancer (CRC) PDTOs. High-resolution imaging of H2B-GFP-labeled CRC organoids with DRAQ7 vital dye permitted tracking of cellular changes in individual organoids. Morphological features of 3D organoids, including volume, sphericity, and ellipticity, were used to evaluate intra- and interpatient tumor organoid heterogeneity. A strong correlation between organoid live-cell number and volume was found, and linear growth rate calculations based on volume or live-cell counts were used to determine differential drug effects such as cytotoxic versus cytostatic responses in PDTO cultures. Overall, the HCS analysis of CRC PDTOs produced multiple parameters that provided both patient- and drug-specific information. There has been a resurgence in phenotypic drug discovery strategies. Cell painting is an image-based multiplexed phenotypic assay that uses relatively simple fluorescent dyes to stain cellular components or organelles together with image analysis to identify cells and extract numerous morphological features that can be organized into profiles that correspond with distinct cellular phenotypes. The cell painting assay has been used to compare the phenotypic profiles of cells from normal and diseased tissues, to detect altered phenotypic profiles elicited by chemical or genetic perturbation, and to group compounds and/or genes into functional pathways. The cell painting assay can be used to infer mechanism of action (MoA) by comparing the phenotypic profiles of compounds of unknown MoA with the phenotypic signatures or fingerprints of drugs and probes with known MoAs. The next-generation blueprint of computational toxicology at the U.S. EPA advocates the use of nontargeted HTS profiling assays for an initial characterization of the biological activity of environmental chemicals. Profiling assays should be compatible with concentration–response screening (qHTS) and provide HCS data that identify potency thresholds for the perturbation of cellular biology and provide information on putative mechanisms of toxicity. Willis et al. present a report on phenotypic profiling of reference chemicals across biologically diverse cell types using the cell painting assay.8Willis C. Nyffeler J. Harrill J. Phenotypic Profiling of Reference Chemicals across Biologically Diverse Cell Types Using the Cell Painting Assay.SLAS Discov. 2020; 27: 755-769Google Scholar The authors describe the use of the cell painting assay to characterize the phenotypic effects of 16 reference chemicals in qHTS mode across six biologically diverse human-derived cell lines. All cell lines were labeled using the same cytochemistry protocol, and the same set of phenotypic features were calculated. While it was necessary to optimize image acquisition settings and cell segmentation parameters for each cell type, the authors did not need to adjust the cytochemistry protocol. For some reference chemicals, similar subsets of phenotypic features corresponding to a specific organelle were associated with the highest effect magnitudes in each affected cell type, and the cell painting assay yielded qualitatively similar biological activity profiles across the group of diverse, morphologically distinct human-derived cell lines. Hughes and coworkers present a report on high-content phenotypic profiling in esophageal adenocarcinoma (EAC) that identifies selectively active pharmacological classes of drugs for repurposing and chemical starting points for novel drug discovery.9Hughes R.E. Elliott R.J.R. Munro A.F. et al.High-Content Phenotypic Profiling in Esophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery.SLAS Discov. 2020; 27: 770-782Google Scholar EAC is a highly heterogeneous disease, dominated by large-scale genomic rearrangements and copy number alterations that have hindered target-directed drug discovery and personalized medicine strategies. The authors describe the implementation of an HCS cell painting assay to profile the phenotypic responses to 19,555 compounds across a panel of six EAC cell lines and two tissue-matched control lines. They showcase the automated HCS image analysis pipeline used to identify compounds that selectively modified the phenotype of EAC cell lines and trained a machine learning model to predict the MoA of EAC-selective compounds using phenotypic fingerprints from a library of reference compounds. Several phenotypic clusters were enriched by similar pharmacological classes, including methotrexate and three antimetabolites that were highly selective for EAC cell lines. A small number of hits that exhibited potent and selective activity for EAC cell lines did not cluster with the phenotypic profiles of reference compounds and may be selectively targeting novel esophageal cancer biology. Steigele et al. present an application note on deep learning-based HCS image analysis for the enterprise.10Steigele S. Siegismund D. Fassler M. et al.Deep Learning-Based HCS Image Analysis for the Enterprise.SLAS Discov. 2020; 27: 812-821Google Scholar Extracting information from complex imaging data poses substantial challenges that may prevent the broad adoption of more sophisticated phenotypic assays. Deep learning-based image analysis can reduce the effort required to analyze large volumes of complex image data at a quality and speed compatible with phenotypic HCS. The authors describe five essential design principles that should guide deep learning-based analysis of HCS images and multiparameter data: (1) insightful data representation, (2) automation of training, (3) multilevel quality control, (4) knowledge embedding and transfer to new assays, and (5) enterprise is deep learning-based that these and the authors used imaging data from a cell painting assay and a receptor assay to the of the The deep learning-based knowledge from on assay and can it to novel assays in an automated In a technical note, and coauthors describe a live-cell and fluorescent imaging HCS using three fluorescent probes and to determine for and provide into mechanisms of cell from to M. et High-Content to Discov. 2020; 27: Scholar The authors a small library of and drugs to the method in four tumor cell lines on the HCS platform. et al. present a report on high-throughput image-based et Discov. 2020; 27: Scholar are where misfolded by on is a fluorescent fluorescent when it to protein The authors describe the of an imaging assay using in and the development of an HCS method for the of on the platform. 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A set of genes were identified that levels of information to axon Analysis of information changes in response to and their effects on to or axon growth the authors to a between genes and their axon that this SBI2Stossi F. Mistry R.M. Singh P.K. et al.Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.SLAS Discov. 2020; 27: 684-694Google Scholar special issue of SLAS Discovery and of interest. The of HCS to with the and implementation of new imaging image analysis methods, and strategies. The in this issue how HCS is being to more complex and physiologically relevant biology for phenotypic drug discovery and how machine learning is being integrated to extract and analyze the information drug discovery, HCS is to environmentally relevant chemicals for endocrine disrupting activity and phenotypic HCS data with expression studies to develop computational models to determine information for systems biology of The authors potential of with respect to the and/or of this The authors for the and/or of this
Source: SLAS DISCOVERY
Authors: Myles Fennell, Paul A. Johnston
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,000
OpenAlex ID: W3045160831