Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments

Nyffeler, Jo; Willis, Clinton; Harris, Felix R.; Foster, Matthew; Chambers, Bryant; Culbreth, Megan; Brockway, Richard E; Davidson-Fritz, Sarah; Dawson, Daniel E.; Shah, Imran; Friedman, Katie Paul; Chang, Dan; Everett, Logan J.; Wambaugh, John F.; Patlewicz, Grace; Harrill, Joshua

doi:10.1016/j.taap.2023.116513

Cited by 26 publications

(10 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a second step, we aimed to evaluate the relevance of our morphological feature selection in the development of a predictive DILI model that could be used to assess the potential of a compound to induce DILI based on its Cell Painting profile. So far, Cell Painting data have been used for cytotoxicity or for DILI prediction , and concentration-response modeling approaches to estimate drug bioactivity − thresholds. With the improvement of machine learning strategies observed these past years, many studies have shown that in silico models can be trained to predict the toxicity outcomes of hundreds of chemicals using image-based profiling .…”

Section: Discussionmentioning

confidence: 99%

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Lejal,

Cerisier,

Rouquié

et al. 2023

Chem. Res. Toxicol.

View full text Add to dashboard Cite

Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to drug withdrawal. Although many studies aim to identify biomarkers and gene/pathway signatures related to liver toxicity and aim to predict DILI compounds, this remains a challenge in drug discovery. With a strong development of high-content screening/imaging (HCS/HCI) for phenotypic screening, we explored the morphological cell perturbations induced by DILI compounds. In the first step, cell morphological signatures were associated with two datasets of DILI chemicals (DILIRank and eTox). The mechanisms of action were then analyzed for chemicals having transcriptomics data and sharing similar morphological perturbations. Signaling pathways associated with liver toxicity (cell cycle, cell growth, apoptosis, ...) were then captured, and a hypothetical relation between cell morphological perturbations and gene deregulation was illustrated within our analysis. Finally, using the cell morphological signatures, machine learning approaches were developed to predict chemicals with a potential risk of DILI. Some models showed relevant performance with validation set balanced accuracies between 0.645 and 0.739. Overall, our findings demonstrate the utility of combining HCI with transcriptomics data to identify the morphological and gene expression signatures related to DILI chemicals. Moreover, our protocol could be extended to other toxicity end points, offering a promising avenue for comprehensive toxicity assessment in drug discovery.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Lejal,

Cerisier,

Rouquié

et al. 2023

Chem. Res. Toxicol.

View full text Add to dashboard Cite

show abstract

“…In 2023, Nyffeler et al determined a phenotype altering concentration of compounds and used IVIVE to determine the administered equivalent doses (AEDs) to predict human exposure. 99 For 18 out of 412 chemicals, in vivo -relevant toxicity was observed at the given exposure, that is, the AEDs overlapped with predicted human exposures. 99 On a larger scale, the U.S. Environmental Protection Agency (EPA) is working towards using transcriptomics and Cell Painting data in their risk assessments.…”

Section: Discussionmentioning

confidence: 99%

“…99 For 18 out of 412 chemicals, in vivo -relevant toxicity was observed at the given exposure, that is, the AEDs overlapped with predicted human exposures. 99 On a larger scale, the U.S. Environmental Protection Agency (EPA) is working towards using transcriptomics and Cell Painting data in their risk assessments. 100 In the future, new sources of relevant in vivo toxicity annotations, and the inclusion of pharmacokinetic (PK) information 101,102 , might be used to augment Cell Painting data in predictive toxicology.…”

Section: Discussionmentioning

confidence: 99%

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

Seal,

Trapotsi,

Spjuth

et al. 2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…28 These high-content representations have mostly been used for drug discovery 29,30 , but recent research has applied this approach for basic research studies, such as predicting cell health, toxicity, and cancer cell resistance. [30][31][32][33][34] While the most common image-based profiling approach is to aggregate single-cell measurements per well, single-cell approaches enable more systematic analyses and are becoming increasingly common. 35 For example, Schorpp et al developed a machine-learning approach to predict apoptosis and necrosis from Cell Painting data at single-cell resolution.…”

Section: Introductionmentioning

confidence: 99%

A morphology and secretome map of pyroptosis

Lippincott,

Tomkinson,

Bunten

et al. 2024

Preprint

View full text Add to dashboard Cite

Pyroptosis represents one type of Programmed Cell Death (PCD). It is a form of inflammatory cell death that is canonically defined by caspase-1 cleavage and Gasdermin-mediated membrane pore formation. Caspase-1 initiates the inflammatory response (through IL-1β processing), and the N-terminal cleaved fragment of Gasdermin D polymerizes at the cell periphery forming pores to secrete pro-inflammatory markers. Cell morphology also changes in pyroptosis, with nuclear condensation and membrane rupture. However, recent research challenges canon, revealing a more complex secretome and morphological response in pyroptosis, including overlapping molecular characterization with other forms of cell death, such as apoptosis. Here, we take a multimodal, systems biology approach to characterize pyroptosis. We treated human Peripheral Blood Mononuclear Cells (PBMCs) with 36 different combinations of stimuli to induce pyroptosis or apoptosis. We applied both secretome profiling (nELISA) and high-content fluorescence microscopy (Cell Painting). To differentiate apoptotic, pyroptotic and healthy cells, we used canonical secretome markers and modified our Cell Painting assay to mark the N-terminus of Gasdermin-D. We trained hundreds of machine learning (ML) models to reveal intricate morphology signatures of pyroptosis that implicate changes across many different organelles and predict levels of many pro-inflammatory markers. Overall, our analysis provides a detailed map of pyroptosis which includes overlapping and distinct connections with apoptosis revealed through a mechanistic link between cell morphology and cell secretome.

show abstract

Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments

Cited by 26 publications

References 63 publications

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

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

A morphology and secretome map of pyroptosis

Contact Info

Product

Resources

About