Cell Painting for cytotoxicity and mode-of-action analysis in primary human hepatocytes

Scalable, human-relevant approaches for detecting compound modes of action are needed to improve chemical safety evaluation. Here, we apply image-based profiling (Cell Painting) alongside two cytotoxicity assays in primary human hepatocytes exposed to eight concentrations of 1,085 compounds spanning pharmaceuticals, pesticides, and industrial chemicals. We compared three computational approaches (CellProfiler, a Cell Painting-specific convolutional neural network, and a pretrained vision transformer) to extract morphological features from single cells or whole images. These features were used to predict activity in the measured cytotoxicity assays and in ToxCast assays covering cytotoxicity, cell-based, and cell-free assay endpoints. Morphological profiles detected bioactivity at lower concentrations than standard cytotoxicity assays and provided mode-of-action insights. In supervised analyses, they predicted cytotoxicity and targeted cell-based assays but not cell-free assays. Feature extraction methods performed similarly, and filtering concentrations did not improve performance. We envision that image-based profiling could be a key component of modern safety assessment.

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Publication Details

Journal
Cell Systems
Published
2026-03-01
DOI
https://doi.org/10.1016/j.cels.2026.101566
Primary Topic
3D Printing in Biomedical Research
Type
article
Field-Weighted Citation Impact
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article

Cell Painting for cytotoxicity and mode-of-action analysis in primary human hepatocytes

Katherine Titterton, Sarabjeet Singh, Alex Bäuerle, Bram L. Gorissen et al.
Cell Systems
3D Printing in Biomedical Research
article

Cell Painting for cytotoxicity and mode-of-action analysis in primary human hepatocytes

Katherine Titterton, Sarabjeet Singh, Alex Bäuerle, Bram L. Gorissen, Brandon M. White, Erin Weisbart, Jaime H. Cheah, Srijit Seal, Beth A. Cimini, Jessica Ewald, Anne E. Carpenter, Joshua Harrill, Ángel Alexander Cabrera, Daniil A. Boiko, Thouis R. Jones, David Rouquié, Alex Beatson, Christine E. Crute, Konrad J. Karczewski
article en

Abstract

Scalable, human-relevant approaches for detecting compound modes of action are needed to improve chemical safety evaluation. Here, we apply image-based profiling (Cell Painting) alongside two cytotoxicity assays in primary human hepatocytes exposed to eight concentrations of 1,085 compounds spanning pharmaceuticals, pesticides, and industrial chemicals. We compared three computational approaches (CellProfiler, a Cell Painting-specific convolutional neural network, and a pretrained vision transformer) to extract morphological features from single cells or whole images. These features were used to predict activity in the measured cytotoxicity assays and in ToxCast assays covering cytotoxicity, cell-based, and cell-free assay endpoints. Morphological profiles detected bioactivity at lower concentrations than standard cytotoxicity assays and provided mode-of-action insights. In supervised analyses, they predicted cytotoxicity and targeted cell-based assays but not cell-free assays. Feature extraction methods performed similarly, and filtering concentrations did not improve performance. We envision that image-based profiling could be a key component of modern safety assessment.

Cell Systems
Broad Institute (US), European Bioinformatics Institute (GB), Research Triangle Park Foundation (US), Foundation Center (US), Novo Nordisk (United States) (US), Axiom (United States) (US), Health and Environmental Sciences Institute (US), Fondation Sophia Antipolis (FR)
Zero hunger
Openalex Percentile: Top 7%
3D Printing in Biomedical Research
DOIOpen Access PDFOpenAlex
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