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

Scalable, compressed phenotypic screening using pooled perturbations

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.

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.

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.

Reproducible image-based profiling with Pycytominer

Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, image analysis pipelines then produce single-cell features. To process these single-cells for downstream applications, we present Pycytominer, a user-friendly, open-source python package that implements the bioinformatics steps, known as "image-based profiling". We demonstrate Pycytominer's usefulness in a machine learning project to predict nuisance compounds that cause undesirable cell injuries.

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.

An Enantioselective Aminocatalytic Cascade Reaction Affording Bioactive Hexahydroazulene Scaffolds

A novel cascade reaction initiated by an enantioselective aminocatalysed 1,3-dipolar [6+4] cycloaddition between catalytically generated trienamines and 3-oxidopyridinium betaines is presented. The [6+4] cycloadduct spontaneously undergoes an intramolecular enamine-mediated aldol, hydrolysis, and E1cb sequence, which ultimately affords a chiral hexahydroazulene framework. In this process, three new C-C bonds and three new stereocenters are formed, enabled by a formal unfolding of the pyridine moiety from the dipolar reagent. The hexahydroazulenes are formed with excellent diastereo-, regio- and periselectivity (>20 : 1), up to 96 % ee, and yields up to 52 %. Synthetic elaborations of this scaffold were performed, providing access to a variety of functionalised hydroazulene compounds, of which some were found to display biological activity in U-2OS osteosarcoma cells in cell painting assays.

Exploring the mechanism and phytochemicals in Psoraleae Fructus-induced hepatotoxicity based on RNA-seq, in vitro screening and molecular docking

Psoraleae Fructus (PF) is a widely-used herb with diverse pharmacological activities, while its related hepatic injuries have aroused public concerns. In this work, a systematic approach based on RNA sequencing (RNA-seq), high-content screening (HCS) and molecular docking was developed to investigate the potential mechanism and identify major phytochemicals contributed to PF-induced hepatotoxicity. Animal experiments proved oral administration of PF water extracts disturbed lipid metabolism and promoted hepatic injuries by suppressing fatty acid and cholesterol catabolism. RNA-seq combined with KEGG enrichment analysis identified mitochondrial oxidative phosphorylation (OXPHOS) as the potential key pathway. Further experiments validated PF caused mitochondrial structure damage, mtDNA depletion and inhibited expressions of genes engaged in OXPHOS. By detecting mitochondrial membrane potential and mitochondrial superoxide, HCS identified bavachin, isobavachalcone, bakuchiol and psoralidin as most potent mitotoxic compounds in PF. Moreover, molecular docking confirmed the potential binding patterns and strong binding affinity of the critical compounds with mitochondrial respiratory complex. This study unveiled the underlying mechanism and phytochemicals in PF-induced liver injuries from the view of mitochondrial dysfunction.

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.