Multiplex cytological profiling assay to measure diverse cellular states

High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells

New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10-3 μM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R2 = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals.

In Vitro Predictions of Acute Fish Toxicity for Development of Sustainable Crop Protection Products

New agrochemicals must demonstrate safety to numerous ecological systems, including aquatic systems, and aquatic vertebrate toxicity is typically evaluated by using the in vivo acute fish toxicity (AFT) test. Here, we investigated two alternative in vitro assays using a cell line isolated from rainbow trout (Onchorhynchus mykiss) gill tissue: (i) adenosine triphosphate (ATP) luminescence and (ii) cell painting. The former assay measures cytotoxicity, while the latter measures changes in cellular morphology in response to chemical exposure. We assessed how well end points in these two assays predicted acute lethality (i.e., LC50 values) in independent in vivo AFT tests. When compared to results from OECD TG 249 (in vitro), we found that the ATP assay was not as predictive (R2 = 0.53) as the cell painting assay. Similarly, when compared to results from OECD TG 203 (in vivo), the cell painting was much more predictive (R2 = 0.67). Our results show that such in vitro assays are useful for fast and efficient screening alternatives to in vivo fish testing that can aid in the agrochemical discovery phase, where thousands of potential new actives are tested each year.

Applying cell painting in non-tumorigenic breast cells to understand impacts of common chemical exposures

The general population is exposed to many chemicals which have putative, but incompletely understood, links to breast cancer. Cell Painting is a high-content imaging-based in vitro assay that allows for unbiased measurements of concentration-dependent effects of chemical exposures on cellular morphology. We used Cell Painting to measure effects of 16 human exposure relevant chemicals, along with 21 small molecules with known mechanisms of action, in non-tumorigenic mammary epithelial cells, the MCF10A cell line. Using CellProfiler image analysis software, we quantified 3042 morphological features across approximately 1.2 million cells. We used benchmark concentration modeling to identify features both conserved and different across chemicals. Benchmark concentrations were compared to exposure biomarker concentration measurements from the National Health and Nutrition Examination Survey to assess which chemicals induce morphological alterations at human-relevant concentrations. We found significant feature overlaps between chemicals, including similarities between the organochlorine pesticide DDT metabolite p,p'-DDE and an activator of Wnt signaling CHIR99201. We validated these findings by assaying the activation of Wnt, as reflected by translocation of ꞵ-catenin, following p'-p' DDE exposure. Consistent with Wnt signaling activation, low concentration p',p'-DDE (25 nM) significantly enhanced the nuclear translocation of ꞵ-catenin. Overall, these findings highlight the ability of Cell Painting to enhance mode-of-action studies for toxicants which are common in our environment but incompletely characterized with respect to breast cancer risk.

Cell-vision fusion: A Swin transformer-based approach for predicting kinase inhibitor mechanism of action from Cell Painting data

Image-based profiling of the cellular response to drug compounds has proven effective at characterizing the morphological changes resulting from perturbation experiments. As data availability increases, however, there are growing demands for novel deep-learning methods. We applied the SwinV2 computer vision architecture to predict the mechanism of action of 10 kinase inhibitor compounds directly from Cell Painting images. This method outperforms the standard approach of using image-based profiles (IBP)-multidimensional feature set representations generated by bioimaging software. Furthermore, our fusion approach-cell-vision fusion, combining three different data modalities, images, IBPs, and chemical structures-achieved 69.79% accuracy and 70.56% F1 score, 4.20% and 5.49% higher, respectively, than the best-performing IBP method. We provide three techniques, specific to Cell Painting images, which enable deep-learning architectures to train effectively and demonstrate approaches to combat the significant batch effects present in large Cell Painting datasets.

Identification of lysosomotropism using explainable machine learning and morphological profiling cell painting data

Lysosomotropism is a phenomenon of diverse pharmaceutical interests because it is a property of compounds with diverse chemical structures and primary targets. While it is primarily reported to be caused by compounds having suitable lipophilicity and basicity values, not all compounds that fulfill such criteria are in fact lysosomotropic. Here, we use morphological profiling by means of the cell painting assay (CPA) as a reliable surrogate to identify lysosomotropism. We noticed that only 35% of the compound subset with matching physicochemical properties show the lysosomotropic phenotype. Based on a matched molecular pair analysis (MMPA), no key substructures driving lysosomotropism could be identified. However, using explainable machine learning (XML), we were able to highlight that higher lipophilicity, basicity, molecular weight, and lower topological polar surface area are among the important properties that induce lysosomotropism in the compounds of this subset.

Detection of a Mitochondrial Fragmentation and Integrated Stress Response Using the Cell Painting Assay

Mitochondria are cellular powerhouses and are crucial for cell function. However, they are vulnerable to internal and external perturbagens that may impair mitochondrial function and eventually lead to cell death. In particular, small molecules may impact mitochondrial function, and therefore, their influence on mitochondrial homeostasis is at best assessed early on in the characterization of biologically active small molecules and drug discovery. We demonstrate that unbiased morphological profiling by means of the cell painting assay (CPA) can detect mitochondrial stress coupled with the induction of an integrated stress response. This activity is common for compounds addressing different targets, is not shared by direct inhibitors of the electron transport chain, and enables prediction of mitochondrial stress induction for small molecules that are profiled using CPA.

Evaluating batch correction methods for image-based cell profiling

High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects severely limit community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmark ten high-performing single-cell RNA sequencing (scRNA-seq) batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, JUMP. We focus on five scenarios with varying complexity, ranging from batches prepared in a single lab over time to batches imaged using different microscopes in multiple labs. We find that Harmony and Seurat RPCA are noteworthy, consistently ranking among the top three methods for all tested scenarios while maintaining computational efficiency. Our proposed framework, benchmark, and metrics can be used to assess new batch correction methods in the future. This work paves the way for improvements that enable the community to make the best use of public Cell Painting data for scientific discovery.

Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning

Image-based cell profiling is a powerful tool that compares perturbed cell populations by measuring thousands of single-cell features and summarizing them into profiles. Typically a sample is represented by averaging across cells, but this fails to capture the heterogeneity within cell populations. We introduce CytoSummaryNet: a Deep Sets-based approach that improves mechanism of action prediction by 30-68% in mean average precision compared to average profiling on a public dataset. CytoSummaryNet uses self-supervised contrastive learning in a multiple-instance learning framework, providing an easier-to-apply method for aggregating single-cell feature data than previously published strategies. Interpretability analysis suggests that the model achieves this improvement by downweighting small mitotic cells or those with debris and prioritizing large uncrowded cells. The approach requires only perturbation labels for training, which are readily available in all cell profiling datasets. CytoSummaryNet offers a straightforward post-processing step for single-cell profiles that can significantly boost retrieval performance on image-based profiling datasets.

Unleashing the potential of cell painting assays for compound activities and hazards prediction

The cell painting (CP) assay has emerged as a potent imaging-based high-throughput phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico prediction of compound activities and potential hazards in drug discovery and toxicology. CP enables the rapid, multiplexed investigation of various molecular mechanisms for thousands of compounds at the single-cell level. The resulting large volumes of image data provide great opportunities but also pose challenges to image and data analysis routines as well as property prediction models. This review addresses the integration of CP-based phenotypic data together with or in substitute of structural information from compounds into machine (ML) and deep learning (DL) models to predict compound activities for various human-relevant disease endpoints and to identify the underlying modes-of-action (MoA) while avoiding unnecessary animal testing. The successful application of CP in combination with powerful ML/DL models promises further advances in understanding compound responses of cells guiding therapeutic development and risk assessment. Therefore, this review highlights the importance of unlocking the potential of CP assays when combined with molecular fingerprints for compound evaluation and discusses the current challenges that are associated with this approach.

Illuminating Dark Chemical Matter Using the Cell Painting Assay

Screening for small-molecule modulators of disease-relevant targets and phenotypes is the first step on the way to new drugs. Large compound libraries have been synthesized by academia and, particularly, pharmaceutical companies to meet the need for novel chemical entities that are as diverse as possible. Screening of these compound libraries revealed a portion of small molecules that is inactive in more than 100 different assays and was therefore termed "dark chemical matter" (DCM). Deorphanization of DCM promises to yield very selective compounds as they are expected to have less off-target effects. We employed morphological profiling using the Cell Painting assay to detect bioactive DCM. Within the DCM collection, we identified bioactive compounds and confirmed several modulators of microtubules, DNA synthesis, and pyrimidine biosynthesis. Profiling approaches are, therefore, powerful tools to probe compound collections for bioactivity in an unbiased manner and are particularly suitable for deorphanization of DCM.

Profiling Cellular Morphological Changes Induced by Dual-Targeting PROTACs of Aurora Kinase and RNA-Binding Protein YTHDF2

Proteolysis targeting chimeras (PROTACs) are new chemical modalities that degrade proteins of interest, including established kinase targets and emerging RNA-binding proteins (RBPs). Whereas diverse sets of biochemical, biophysical and cellular assays are available for the evaluation and optimizations of PROTACs in understanding the involved ubiquitin-proteasome-mediated degradation mechanism and the structure-degradation relationship, a phenotypic method profiling the cellular morphological changes is rarely used. In this study, first, we reported the only examples of PROTACs degrading the mRNA-binding protein YTHDF2 via screening of multikinase PROTACs. Second, we reported the profiling of cellular morphological changes of the dual kinase- and RBP-targeting PROTACs using the unbiased cell painting assay (CPA). The CPA analysis revealed the high biosimilarity with the established aurora kinase cluster and annotated aurora kinase inhibitors, which reflected the association between YTHDF2 and the aurora kinase signaling network. Broadly, the results demonstrated that the cell painting assay can be a straightforward and powerful approach to evaluate PROTACs. Complementary to the existing biochemical, biophysical and cellular assays, CPA provided a new perspective in characterizing PROTACs at the cellular morphology.

Discovery of a Novel Pseudo-Natural Product Aurora Kinase Inhibitor Chemotype through Morphological Profiling

The pseudo-natural product (pseudo-NP) concept aims to combine NP fragments in arrangements that are not accessible through known biosynthetic pathways. The resulting compounds retain the biological relevance of NPs but are not yet linked to bioactivities and may therefore be best evaluated by unbiased screening methods resulting in the identification of unexpected or unprecedented bioactivities. Herein, various NP fragments are combined with a tricyclic core connectivity via interrupted Fischer indole and indole dearomatization reactions to provide a collection of highly three-dimensional pseudo-NPs. Target hypothesis generation by morphological profiling via the cell painting assay guides the identification of an unprecedented chemotype for Aurora kinase inhibition with both its relatively highly 3D structure and its physicochemical properties being very different from known inhibitors. Biochemical and cell biological characterization indicate that the phenotype identified by the cell painting assay corresponds to the inhibition of Aurora kinase B.

Morphological profiling for drug discovery in the era of deep learning

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

From Pipeline to Plant Protection Products: Using New Approach Methodologies (NAMs) in Agrochemical Safety Assessment

The human population will be approximately 9.7 billion by 2050, and food security has been identified as one of the key issues facing the global population. Agrochemicals are an important tool available to farmers that enable high crop yields and continued access to healthy foods, but the average new agrochemical active ingredient takes more than ten years, 350 million dollars, and 20,000 animals to develop and register. The time, monetary, and animal costs incentivize the use of New Approach Methodologies (NAMs) in early-stage screening to prioritize chemical candidates. This review outlines NAMs that are currently available or can be adapted for use in early-stage screening agrochemical programs. It covers new in vitro screens that are on the horizon in key areas of regulatory concern. Overall, early-stage screening with NAMs enables the prioritization of development for agrochemicals without human and environmental health concerns through a more directed, agile, and iterative development program before animal-based regulatory testing is even considered.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other -omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

Applying Cell Painting in Non-Tumorigenic Breast Cells to Understand Impacts of Common Chemical Exposures

There are a substantial number of chemicals to which individuals in the general population are exposed which have putative, but still poorly understood, links to breast cancer. Cell Painting is a high-content imaging-based in vitro assay that allows for rapid and unbiased measurements of the concentration-dependent effects of chemical exposures on cellular morphology. We optimized the Cell Painting assay and measured the effect of exposure to 16 human exposure relevant chemicals, along with 21 small molecules with known mechanisms of action, for 48 hours in non-tumorigenic mammary epithelial cells, the MCF10A cell line. Through unbiased imaging analyses using CellProfiler, we quantified 3042 morphological features across approximately 1.2 million cells. We used benchmark concentration modeling to quantify significance and dose-dependent directionality to identify morphological features conserved across chemicals and find features that differentiate the effects of toxicants from one another. Benchmark concentrations were compared to chemical exposure biomarker concentration measurements from the National Health and Nutrition Examination Survey to assess which chemicals induce morphological alterations at human-relevant concentrations. Morphometric fingerprint analysis revealed similar phenotypes between small molecules and prioritized NHANES-toxicants guiding further investigation. A comparison of feature fingerprints via hypergeometric analysis revealed significant feature overlaps between chemicals when stratified by compartment and stain. One such example was the similarities between a metabolite of the organochlorine pesticide DDT (p,p'-DDE) and an activator of canonical Wnt signaling CHIR99201. As CHIR99201 is a known Wnt pathway activator and its role in β-catenin translocation is well studied, we studied the translocation of β-catenin following p'-p' DDE exposure in an orthogonal high-content imaging assay. Consistent with activation of Wnt signaling, low dose p',p'-DDE (25nM) significantly enhances the nuclear translocation of β-catenin. Overall, these findings highlight the ability of Cell Painting to enhance mode-of-action studies for toxicants which are common exposures in our environment but have previously been incompletely characterized with respect to breast cancer risk.

High throughput functional profiling of genes at intraocular pressure loci reveals distinct networks for glaucoma

INTRODUCTION

Primary open angle glaucoma (POAG) is a leading cause of blindness globally. Characterized by progressive retinal ganglion cell degeneration, the precise pathogenesis remains unknown. Genome-wide association studies (GWAS) have uncovered many genetic variants associated with elevated intraocular pressure (IOP), one of the key risk factors for POAG. We aimed to identify genetic and morphological variation that can be attributed to trabecular meshwork cell (TMC) dysfunction and raised IOP in POAG.

METHODS

62 genes across 55 loci were knocked-out in a primary human TMC line. Each knockout group, including five non-targeting control groups, underwent single-cell RNA-sequencing (scRNA-seq) for differentially-expressed gene (DEG) analysis. Multiplexed fluorescence coupled with CellProfiler image analysis allowed for single-cell morphological profiling.

RESULTS

Many gene knockouts invoked DEGs relating to matrix metalloproteinases and interferon-induced proteins. We have prioritized genes at four loci of interest to identify gene knockouts that may contribute to the pathogenesis of POAG, including ANGPTL2, LMX1B, CAV1, and KREMEN1. Three genetic networks of gene knockouts with similar transcriptomic profiles were identified, suggesting a synergistic function in trabecular meshwork cell physiology. TEK knockout caused significant upregulation of nuclear granularity on morphological analysis, while knockout of TRIOBP, TMCO1 and PLEKHA7 increased granularity and intensity of actin and the cell-membrane.

CONCLUSION

High-throughput analysis of cellular structure and function through multiplex fluorescent single-cell analysis and scRNA-seq assays enabled the direct study of genetic perturbations at the single-cell resolution. This work provides a framework for investigating the role of genes in the pathogenesis of glaucoma and heterogenous diseases with a strong genetic basis.

Morphological profiling in human neural progenitor cells classifies hits in a pilot drug screen for Alzheimer's disease

Alzheimer's disease accounts for 60-70% of dementia cases. Current treatments are inadequate and there is a need to develop new approaches to drug discovery. Recently, in cancer, morphological profiling has been used in combination with high-throughput screening of small-molecule libraries in human cells in vitro. To test feasibility of this approach for Alzheimer's disease, we developed a cell morphology-based drug screen centred on the risk gene, SORL1 (which encodes the protein SORLA). Increased Alzheimer's disease risk has been repeatedly linked to variants in SORL1, particularly those conferring loss or decreased expression of SORLA, and lower SORL1 levels are observed in post-mortem brain samples from individuals with Alzheimer's disease. Consistent with its role in the endolysosomal pathway, SORL1 deletion is associated with enlarged endosomes in neural progenitor cells and neurons. We, therefore, hypothesized that multi-parametric, image-based cell phenotyping would identify features characteristic of SORL1 deletion. An automated morphological profiling method (Cell Painting) was adapted to neural progenitor cells and used to determine the phenotypic response of SORL1-/- neural progenitor cells to treatment with compounds from a small internationally approved drug library (TargetMol, 330 compounds). We detected distinct phenotypic signatures for SORL1-/- neural progenitor cells compared to isogenic wild-type controls. Furthermore, we identified 16 compounds (representing 14 drugs) that reversed the mutant morphological signatures in neural progenitor cells derived from three SORL1-/- induced pluripotent stem cell sub-clones. Network pharmacology analysis revealed the 16 compounds belonged to five mechanistic groups: 20S proteasome, aldehyde dehydrogenase, topoisomerase I and II, and DNA synthesis inhibitors. Enrichment analysis identified DNA synthesis/damage/repair, proteases/proteasome and metabolism as key pathways/biological processes. Prediction of novel targets revealed enrichment in pathways associated with neural cell function and Alzheimer's disease. Overall, this work suggests that (i) a quantitative phenotypic metric can distinguish induced pluripotent stem cell-derived SORL1-/- neural progenitor cells from isogenic wild-type controls and (ii) phenotypic screening combined with multi-parametric high-content image analysis is a viable option for drug repurposing and discovery in this human neural cell model of Alzheimer's disease.

SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data

Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has been central in the field, allowing for large screening campaigns leading to a vast number of data-rich images. Currently, to analyze data of this scale, available open-source software ( i.e. , CellProfiler) requires computational resources that are not available to most laboratories worldwide. In addition, the image-embedded cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. Here we introduce SPACe ( S wift P henotypic A nalysis of Ce lls), an open source, Python-based platform for the analysis of single cell image-based morphological profiles produced by CP experiments. SPACe can process a typical dataset approximately ten times faster than CellProfiler on common desktop computers without loss in mechanism of action (MOA) recognition accuracy. It also computes directional distribution-based distances (Earth Mover's Distance - EMD) of morphological features for quality control and hit calling. We highlight several advantages of SPACe analysis on CP assays, including reproducibility across multiple biological replicates, easy applicability to multiple (∼20) cell lines, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We ultimately illustrate the advantages of SPACe in a screening campaign of cell metabolism small molecule inhibitors which we performed in seven cell lines to highlight the importance of testing perturbations across models.

Enhancing subcellular protein localization mapping analysis using Sc2promap utilizing attention mechanisms

BACKGROUND

Aberrant protein localization is a prominent feature in many human diseases and can have detrimental effects on the function of specific tissues and organs. High-throughput technologies, which continue to advance with iterations of automated equipment and the development of bioinformatics, enable the acquisition of large-scale data that are more pattern-rich, allowing for the use of a wider range of methods to extract useful patterns and knowledge from them.

METHODS

The proposed sc2promap (Spatial and Channel for SubCellular Protein Localization Mapping) model, designed to proficiently extract meaningful features from a vast repository of single-channel grayscale protein images for the purposes of protein localization analysis and clustering. Sc2promap incorporates a prediction head component enriched with supplementary protein annotations, along with the integration of a spatial-channel attention mechanism within the encoder to enables the generation of high-resolution protein localization maps that encapsulate the fundamental characteristics of cells, including elemental cellular localizations such as nuclear and non-nuclear domains.

RESULTS

Qualitative and quantitative comparisons were conducted across internal and external clustering evaluation metrics, as well as various facets of the clustering results. The study also explored different components of the model. The research outcomes conclusively indicate that, in comparison to previous methods, Sc2promap exhibits superior performance.

CONCLUSIONS

The amalgamation of the attention mechanism and prediction head components has led the model to excel in protein localization clustering and analysis tasks.

GENERAL SIGNIFICANCE

The model effectively enhances the capability to extract features and knowledge from protein fluorescence images.

Using a Novel Multiplexed Algal Cytological Imaging (MACI) Assay and Machine Learning as a Way to Characterize Complex Phenotypes in Plant-Type Organisms

High-throughput phenotypic profiling assays, popular for their ability to characterize alternations in single-cell morphological feature data, have been useful in recent years for predicting cellular targets and mechanisms of action (MoAs) for different chemicals and novel drugs. However, this approach has not been extensively used in environmental toxicology due to the lack of studies and established methods for performing this kind of assay in environmentally relevant species. Here, we developed a multiplexed algal cytological imaging (MACI) assay, based on the subcellular structures of the unicellular microalgae, Raphidocelis subcapitata, a toxicology and ecological model species. Several different herbicides and antibiotics with unique MoAs were exposed to R. subcapitata cells, and MACI was used to characterize cellular impacts by measuring subtle changes in their morphological features, including metrics of area, shape, quantity, fluorescence intensity, and granularity of individual subcellular components. This study demonstrates that MACI offers a quick and effective framework for characterizing complex phenotypic responses to environmental chemicals that can be used for determining their MoAs and identifying their cellular targets in plant-type organisms.

From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability

Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict in vitro and in vivo drug effects. However, Cell Painting features extracted from classical software such as CellProfiler are based on statistical calculations and often not readily biologically interpretable. In this study, we propose a new feature space, which we call BioMorph, that maps these Cell Painting features with readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. Overall, BioMorph space offers a biologically relevant way to interpret the cell morphological features derived using software such as CellProfiler and to generate hypotheses for experimental validation.

Evaluating batch correction methods for image-based cell profiling

High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects pose severe limitations to community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmarked seven high-performing scRNA-seq batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, the largest publicly accessible image-based dataset. We focused on five different scenarios with varying complexity, and we found that Harmony, a mixture-model based method, consistently outperformed the other tested methods. Our proposed framework, benchmark, and metrics can additionally be used to assess new batch correction methods in the future. Overall, this work paves the way for improvements that allow the community to make best use of public Cell Painting data for scientific discovery.

Learning representations for image-based profiling of perturbations

Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN. We evaluated our strategy on three publicly available Cell Painting datasets, and observed that the Cell Painting CNN improves performance in downstream analysis up to 30% with respect to classical features, while also being more computationally efficient.

Investigating open access new approach methods (NAM) to assess biological points of departure: A case study with 4 neurotoxic pesticides

Open access new approach methods (NAM) in the US EPA ToxCast program and NTP Integrated Chemical Environment (ICE) were used to investigate activities of four neurotoxic pesticides: endosulfan, fipronil, propyzamide and carbaryl. Concordance of in vivo regulatory points of departure (POD) adjusted for interspecies extrapolation (AdjPOD) to modelled human Administered Equivalent Dose (AEDHuman) was assessed using 3-compartment or Adult/Fetal PBTK in vitro to in vivo extrapolation. Model inputs were from Tier 1 (High throughput transcriptomics: HTTr, high throughput phenotypic profiling: HTPP) and Tier 2 (single target: ToxCast) assays. HTTr identified gene expression signatures associated with potential neurotoxicity for endosulfan, propyzamide and carbaryl in non-neuronal MCF-7 and HepaRG cells. The HTPP assay in U-2 OS cells detected potent effects on DNA endpoints for endosulfan and carbaryl, and mitochondria with fipronil (propyzamide was inactive). The most potent ToxCast assays were concordant with specific components of each chemical mode of action (MOA). Predictive adult IVIVE models produced fold differences (FD) < 10 between the AEDHuman and the measured in vivo AdjPOD. The 3-compartment model was concordant (i.e., smallest FD) for endosulfan, fipronil and carbaryl, and PBTK was concordant for propyzamide. The most potent AEDHuman predictions for each chemical showed HTTr, HTPP and ToxCast were mainly concordant with in vivo AdjPODs but assays were less concordant with MOAs. This was likely due to the cell types used for testing and/or lack of metabolic capabilities and pathways available in vivo. The Fetal PBTK model had larger FDs than adult models and was less predictive overall.

Key aspects of modern GPCR drug discovery

G-protein-coupled receptors (GPCRs) are the largest and most versatile cell surface receptor family with a broad repertoire of ligands and functions. We've learned an enormous amount about discovering drugs of this receptor class since the first GPCR was cloned and expressed in 1986, such that it's now well-recognized that GPCRs are the most successful target class for approved drugs. Here we take the reader through a GPCR drug discovery journey from target to the clinic, highlighting the key learnings, best practices, challenges, trends and insights on discovering drugs that ultimately modulate GPCR function therapeutically in patients. The future of GPCR drug discovery is inspiring, with more desirable drug mechanisms and new technologies enabling the delivery of better and more successful drugs.

Immunotoxic, genotoxic, and endocrine disrupting impacts of polyamide microplastic particles and chemicals

Due to their exceptional properties and cost effectiveness, polyamides or nylons have emerged as widely used materials, revolutionizing diverse industries, including industrial 3D printing or additive manufacturing (AM). Powder-based AM technologies employ tonnes of polyamide microplastics to produce complex components every year. However, the lack of comprehensive toxicity assessment of particulate polyamides and polyamide-associated chemicals, especially in the light of the global microplastics crisis, calls for urgent action. This study investigated the physicochemical properties of polyamide-12 microplastics used in AM, and assessed a number of toxicity endpoints focusing on inflammation, immunometabolism, genotoxicity, aryl hydrocarbon receptor (AhR) activation, endocrine disruption, and cell morphology. Specifically, microplastics examination by means of field emission scanning electron microscopy revealed that work flow reuse of material created a fraction of smaller particles with an average size of 1-5 µm, a size range readily available for uptake by human cells. Moreover, chemical analysis by means of gas chromatography high-resolution mass spectrometry detected several polyamide-associated chemicals including starting material, plasticizer, thermal stabilizer/antioxidant, and migrating slip additive. Even if polyamide particles and chemicals did not induce an acute inflammatory response, repeated and prolonged exposure of human primary macrophages disclosed a steady increase in the levels of proinflammatory chemokine Interleukin-8 (IL-8/CXCL-8). Moreover, targeted metabolomics disclosed that polyamide particles modulated the kynurenine pathway and some of its key metabolites. The p53-responsive luciferase reporter gene assay showed that particles per se were able to activate p53, being indicative of a genotoxic stress. Polyamide-associated chemicals triggered moderate activation of AhR and elicited anti-androgenic activity. Finally, a high-throughput and non-targeted morphological profiling by Cell Painting assay outlined major sites of bioactivity of polyamide-associated chemicals and indicated putative mechanisms of toxicity in the cells. These findings reveal that the increasing use of polyamide microplastics may pose a potential health risk for the exposed individuals, and it merits more attention.

High content screening strategies for large-scale compound libraries with a focus on high-containment viruses

A majority of viral diseases do not have FDA-approved drugs. The recent outbreaks caused by SARS-CoV-2, monkeypox, and Sudan ebolavirus have exposed the critical need for rapid screening and identification of antiviral compounds against emerging/re-emerging viral pathogens. A high-content screening (HCS) platform is becoming an essential part of the drug discovery process, thanks to developments in image acquisition and analysis. While HCS has several advantages, its full potential has not been realized in antiviral drug discovery compared to conventional drug screening approaches, such as fluorescence or luminescence-based microplate assays. Therefore, this review aims to summarize HCS workflow, strategies, and developments in image-based drug screening, focusing on high-containment viruses.

Discovery of pyrazolopyrimidines that selectively inhibit CSF-1R kinase by iterative design, synthesis and screening against glioblastoma cells

Glioblastoma multiforme (GBM) is the most aggressive type of brain cancer in adults, with an average life expectancy under treatment of approx. 15 months. GBM is characterised by a complex set of genetic alterations that results in significant disruption of receptor tyrosine kinase (RTK) signaling. We report here an exploration of the pyrazolo[3,4-d]pyrimidine scaffold in search for antiproliferative compounds directed to GBM treatment. Small compound libraries were synthesised and screened against GBM cells to build up structure-antiproliferative activity-relationships (SAARs) and inform further rounds of design, synthesis and screening. 76 novel compounds were generated through this iterative process that found low micromolar potencies against selected GBM lines, including patient-derived stem cells. Phenomics analysis demonstrated preferential activity against glioma cells of the mesenchymal subtype, whereas kinome screening identified colony stimulating factor-1 receptor (CSF-1R) as the lead's target, a RTK implicated in the tumourigenesis and progression of different cancers and the immunoregulation of the GBM microenvironment.

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

Evolution and impact of high content imaging

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.

Machine learning for cross-scale microscopy of viruses

Despite advances in virological sciences and antiviral research, viruses continue to emerge, circulate, and threaten public health. We still lack a comprehensive understanding of how cells and individuals remain susceptible to infectious agents. This deficiency is in part due to the complexity of viruses, including the cell states controlling virus-host interactions. Microscopy samples distinct cellular infection stages in a multi-parametric, time-resolved manner at molecular resolution and is increasingly enhanced by machine learning and deep learning. Here we discuss how state-of-the-art artificial intelligence (AI) augments light and electron microscopy and advances virological research of cells. We describe current procedures for image denoising, object segmentation, tracking, classification, and super-resolution and showcase examples of how AI has improved the acquisition and analyses of microscopy data. The power of AI-enhanced microscopy will continue to help unravel virus infection mechanisms, develop antiviral agents, and improve viral vectors.

Cooperative Lewis Acid-1,2,3-Triazolium-Aryloxide Catalysis: Pyrazolone Addition to Nitroolefins as Entry to Diaminoamides

Pyrazolones represent an important structural motif in active pharmaceutical ingredients. Their asymmetric synthesis is thus widely studied. Still, a generally highly enantio- and diastereoselective 1,4-addition to nitroolefins providing products with adjacent stereocenters is elusive. In this article, a new polyfunctional CuII -1,2,3-triazolium-aryloxide catalyst is presented which enables this reaction type with high stereocontrol. DFT studies revealed that the triazolium stabilizes the transition state by hydrogen bonding between C(5)-H and the nitroolefin and verify a cooperative mode of activation. Moreover, they show that the catalyst adopts a rigid chiral cage/pore structure by intramolecular hydrogen bonding, by which stereocontrol is achieved. Control catalyst systems confirm the crucial role of the triazolium, aryloxide and CuII , requiring a sophisticated structural orchestration for high efficiency. The addition products were used to form pyrazolidinones by chemoselective C=N reduction. These heterocycles are shown to be valuable precursors toward β,γ'-diaminoamides by chemoselective nitro and N-N bond reductions. Morphological profiling using the Cell painting assay identified biological activities for the pyrazolidinones and suggest modulation of DNA synthesis as a potential mode of action. One product showed biological similarity to Camptothecin, a lead structure for cancer therapy.

Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler

A primary obstacle in translating genetic associations with disease into therapeutic strategies is elucidating the cellular programs affected by genetic risk variants and effector genes. Here, we introduce LipocyteProfiler, a cardiometabolic-disease-oriented high-content image-based profiling tool that enables evaluation of thousands of morphological and cellular profiles that can be systematically linked to genes and genetic variants relevant to cardiometabolic disease. We show that LipocyteProfiler allows surveillance of diverse cellular programs by generating rich context- and process-specific cellular profiles across hepatocyte and adipocyte cell-state transitions. We use LipocyteProfiler to identify known and novel cellular mechanisms altered by polygenic risk of metabolic disease, including insulin resistance, fat distribution, and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse deep phenotypic profiling in lipocytes and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.

Optimizing the Cell Painting assay for image-based profiling

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations on the basis of their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells and predicting assay outcomes by using machine learning, among many others. Here, we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum and mitochondria. The original protocol was updated in 2016 on the basis of several years' experience running it at two sites, after optimizing it by visual stain quality. Here, we describe the work of the Joint Undertaking for Morphological Profiling Cell Painting Consortium, to improve upon the assay via quantitative optimization by measuring the assay's ability to detect morphological phenotypes and group similar perturbations together. The assay gives very robust outputs despite various changes to the protocol, and two vendors' dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for typically sized batches of ≤20 plates; feature extraction and data analysis take an additional 1-2 weeks.This protocol is an update to Nat. Protoc. 11, 1757-1774 (2016): https://doi.org/10.1038/nprot.2016.105.

Development of an Imaging Flow Cytometry Method for Fungal Cytological Profiling and Its Potential Application in Antifungal Drug Development

Automated imaging techniques have been in increasing demand for the more advanced analysis and efficient characterization of cellular phenotypes. The success of the image-based profiling method hinges on assays that can rapidly and simultaneously capture a wide range of phenotypic features. We have developed an automated image acquisition method for fungal cytological profiling (FCP) using an imaging flow cytometer that can objectively measure over 250 features of a single fungal cell. Fungal cells were labeled with calcofluor white and FM4-64FX, which bind to the cell wall and lipophilic membrane, respectively. Images of single cells were analyzed using IDEAS^® software. We first acquired FCPs of fungal cells treated with fluconazole, amphotericin B, and caspofungin, each with a distinct mode of action, to establish FCP databases of profiles associated with specific antifungal treatment. Once fully established, we investigated the potential application of this technique as a screening methodology to identify compounds with novel antifungal activity against Candida albicans and Cryptococcus neoformans. Altogether, we have developed a rapid, powerful, and novel image-profiling method for the phenotypic characterization of fungal cells, also with potential applications in antifungal drug development.

Morphological subprofile analysis for bioactivity annotation of small molecules

Fast prediction of the mode of action (MoA) for bioactive compounds would immensely foster bioactivity annotation in compound collections and may early on reveal off-targets in chemical biology research and drug discovery. Morphological profiling, e.g., using the Cell Painting assay, offers a fast, unbiased assessment of compound activity on various targets in one experiment. However, due to incomplete bioactivity annotation and unknown activities of reference compounds, prediction of bioactivity is not straightforward. Here we introduce the concept of subprofile analysis to map the MoA for both, reference and unexplored compounds. We defined MoA clusters and extracted cluster subprofiles that contain only a subset of morphological features. Subprofile analysis allows for the assignment of compounds to, currently, twelve targets or MoA. This approach enables rapid bioactivity annotation of compounds and will be extended to further clusters in the future.

Leveraging Cell Painting Images to Expand the Applicability Domain and Actively Improve Deep Learning Quantitative Structure-Activity Relationship Models

The search for chemical hit material is a lengthy and increasingly expensive drug discovery process. To improve it, ligand-based quantitative structure-activity relationship models have been broadly applied to optimize primary and secondary compound properties. Although these models can be deployed as early as the stage of molecule design, they have a limited applicability domain─if the structures of interest differ substantially from the chemical space on which the model was trained, a reliable prediction will not be possible. Image-informed ligand-based models partly solve this shortcoming by focusing on the phenotype of a cell caused by small molecules, rather than on their structure. While this enables chemical diversity expansion, it limits the application to compounds physically available and imaged. Here, we employ an active learning approach to capitalize on both of these methods' strengths and boost the model performance of a mitochondrial toxicity assay (Glu/Gal). Specifically, we used a phenotypic Cell Painting screen to build a chemistry-independent model and adopted the results as the main factor in selecting compounds for experimental testing. With the additional Glu/Gal annotation for selected compounds we were able to dramatically improve the chemistry-informed ligand-based model with respect to the increased recognition of compounds from a 10% broader chemical space.

Human nuclear hormone receptor activity contributes to malaria parasite liver stage development

Chemical genetic approaches have had a transformative impact on discovery of drug targets for malaria but have primarily been used for parasite targets. To identify human pathways required for intrahepatic development of parasite, we implemented multiplex cytological profiling of malaria infected hepatocytes treated with liver stage active compounds. Some compounds, including MMV1088447 and MMV1346624, exhibited profiles similar to cells treated with nuclear hormone receptor (NHR) agonist/antagonists. siRNAs targeting human NHRs, or their signaling partners identified eight genes that were critical for Plasmodium berghei infection. Knockdown of NR1D2, a host NHR, significantly impaired parasite growth by downregulation of host lipid metabolism. Importantly, treatment with MMV1088447 and MMV1346624 but not other antimalarials, phenocopied the lipid metabolism defect of NR1D2 knockdown. Our data underlines the use of high-content imaging for host-cellular pathway deconvolution, highlights host lipid metabolism as a drug-able human pathway and provides new chemical biology tools for studying host-parasite interactions.

Morphological Profiling Identifies the Motor Protein Eg5 as Cellular Target of Spirooxindoles

Oxindoles and iso-oxindoles are natural product-derived scaffolds that provide inspiration for the design and synthesis of novel biologically relevant compound classes. Notably, the spirocyclic connection of oxindoles with iso-oxindoles has not been explored by nature but promises to provide structurally related compounds endowed with novel bioactivity. Therefore, methods for their efficient synthesis and the conclusive discovery of their cellular targets are highly desirable. We describe a selective RhIII -catalyzed scaffold-divergent synthesis of spirooxindole-isooxindoles and spirooxindole-oxindoles from differently protected diazooxindoles and N-pivaloyloxy aryl amides which includes a functional group-controlled Lossen rearrangement as key step. Unbiased morphological profiling of a corresponding compound collection in the Cell Painting assay efficiently identified the mitotic kinesin Eg5 as the cellular target of the spirooxindoles, defining a unique Eg5 inhibitor chemotype.

Rapid and Accurate Identification of Cell Phenotypes of Different Drug Mechanisms by Using Single-Cell Fluorescence Images Via Deep Learning

Identification of a drug mechanism is vital for drug development. However, it often resorts to the expensive and cumbersome omics methods along with complex data analysis. Herein, we developed a methodology to analyze organelle staining images of single cells using a deep learning algorithm (TL-ResNet50) for rapid and accurate identification of different drug mechanisms. Based on the organelle-related cell morphological changes caused by drug action, the constructed deep learning model can fast predict the drug mechanism with a high accuracy of 92%. Further analysis reveals that drug combination at different ratios can enhance a certain mechanism or generate a new mechanism. This work would highly facilitate clinical medication and drug screening.

Application of cell painting for chemical hazard evaluation in support of screening-level chemical assessments

'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ~100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

Predicting compound activity from phenotypic profiles and chemical structures

Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources-chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)-to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6-10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process.

Robust and unbiased estimation of the background distribution for automated quantitative imaging

Background estimation is the first step in quantitative analysis of images. It has an impact on all subsequent analyses, in particular for segmentation and calculation of ratiometric quantities. Most methods recover only a single value such as the median or yield a biased estimation in non-trivial cases. We introduce, to our knowledge, the first method to recover an unbiased estimation of background distribution. It leverages the lack of local spatial correlation in background pixels to robustly select a subset that accurately represents the background. The resulting background distribution can be used to test for foreground membership of individual pixels or estimate confidence intervals in derived quantities.

Reference compounds for characterizing cellular injury in high-content cellular morphology assays

Robust, generalizable approaches to identify compounds efficiently with undesirable mechanisms of action in complex cellular assays remain elusive. Such a process would be useful for hit triage during high-throughput screening and, ultimately, predictive toxicology during drug development. Here we generate cell painting and cellular health profiles for 218 prototypical cytotoxic and nuisance compounds in U-2 OS cells in a concentration-response format. A diversity of compounds that cause cellular damage produces bioactive cell painting morphologies, including cytoskeletal poisons, genotoxins, nonspecific electrophiles, and redox-active compounds. Further, we show that lower quality lysine acetyltransferase inhibitors and nonspecific electrophiles can be distinguished from more selective counterparts. We propose that the purposeful inclusion of cytotoxic and nuisance reference compounds such as those profiled in this resource will help with assay optimization and compound prioritization in complex cellular assays like cell painting.

In vivo quantitative high-throughput screening for drug discovery and comparative toxicology

Quantitative high-throughput screening (qHTS) pharmacologically evaluates chemical libraries for therapeutic uses, toxicological risk and, increasingly, for academic probe discovery. Phenotypic high-throughput screening assays interrogate molecular pathways, often relying on cell culture systems, historically less focused on multicellular organisms. Caenorhabditis elegans has served as a eukaryotic model organism for human biology by virtue of genetic conservation and experimental tractability. Here, a paradigm enabling C. elegans qHTS using 384-well microtiter plate laser-scanning cytometry is described, in which GFP-expressing organisms revealing phenotype-modifying structure-activity relationships guide subsequent life-stage and proteomic analyses, and Escherichia coli bacterial ghosts, a non-replicating nutrient source, allow compound exposures over two life cycles, mitigating bacterial overgrowth complications. We demonstrate the method with libraries of anti-infective agents, or substances of toxicological concern. Each was tested in seven-point titration to assess the feasibility of nematode-based in vivo qHTS, and examples of follow-up strategies were provided to study organism-based chemotype selectivity and subsequent network perturbations with a physiological impact. We anticipate that this qHTS approach will enable analysis of C. elegans orthologous phenotypes of human pathologies to facilitate drug library profiling for a range of therapeutic indications.

Linking chemicals, genes and morphological perturbations to diseases

The progress in image-based high-content screening technology has facilitated high-throughput phenotypic profiling notably the quantification of cell morphology perturbation by chemicals. However, understanding the mechanism of action of a chemical and linking it to cell morphology and phenotypes remains a challenge in drug discovery. In this study, we intended to integrate molecules that induced transcriptomic perturbations and cellular morphological changes into a biological network in order to assess chemical-phenotypic relationships in humans. Such a network was enriched with existing disease information to suggest molecular and cellular profiles leading to phenotypes. Two datasets were used for this study. Firstly, we used the "Cell Painting morphological profiling assay" dataset, composed of 30,000 compounds tested on human osteosarcoma cells (named U2OS). Secondly, we used the "L1000 mRNA profiling assay" dataset, a collection of transcriptional expression data from cultured human cells treated with approximately 20,000 bioactive small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). Furthermore, pathways, gene ontology terms and disease enrichments were performed on the transcriptomics data. Overall, our study makes it possible to develop a biological network combining chemical-gene-pathway-morphological perturbation and disease relationships. It contains an ensemble of 9989 chemicals, 732 significant morphological features and 12,328 genes. Through diverse examples, we demonstrated that some drugs shared similar genes, pathways and morphological profiles that, taken together, could help in deciphering chemical-phenotype observations.