Cell Painting Identifies Phenotypic Clusters Associated with Anticancer Drug Responses in OVCAR-3 Ovarian Cancer Cells

Cell Painting is a high-content imaging approach that enables systematic characterization of cellular responses to chemical perturbations through quantitative morphological profiling. Although this methodology has been widely applied in drug discovery and functional genomics, ovarian cancer models remain comparatively underrepresented in Cell Painting studies. In the present work, we established a Cell Painting workflow for OVCAR-3 ovarian cancer cells and applied it to the phenotypic profiling of 54 anticancer compounds representing diverse mechanisms of action. Cells were exposed to compounds under short- and prolonged-treatment conditions, followed by multiplex fluorescent staining, high-content imaging, feature extraction using CellProfiler, and computational analysis of the resulting morphological profiles. Profiles obtained at the two treatment durations were analyzed separately. For each compound, the treatment condition showing greater within-compound consistency in PCA space was additionally retained to construct a combined dataset. Morphological relationships were explored using Pearson correlation analysis, dimensionality reduction, and unsupervised clustering. Morphological profiles showed reproducible relationships among compounds. Compounds with related mechanisms showed greater morphological similarity than expected by chance, although substantial within-class heterogeneity remained. Statistically significant cluster enrichment was observed for microtubule-targeting agents and topoisomerase inhibitors, whereas other mechanism-related patterns were incomplete. Among the evaluated clustering approaches, Leiden and agglomerative clustering produced similar separation of the Combined dataset, with silhouette scores of 0.486 and 0.482, respectively. Cell Painting identified mechanism-associated but heterogeneous morphological responses in OVCAR-3 cells. The results support the use of this model for exploratory phenotypic profiling while highlighting the influence of exposure conditions and the limited ability of morphology alone to recover annotated molecular mechanisms.

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Journal
International Journal of Molecular Sciences
Published
2026-09-30
DOI
https://doi.org/10.3390/ijms27198791
Primary Topic
Cell Image Analysis Techniques
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article
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article

Cell Painting Identifies Phenotypic Clusters Associated with Anticancer Drug Responses in OVCAR-3 Ovarian Cancer Cells

Dmitry A. Lioznov, Daria Danilenko, Ramziya G. Kiyamova, Mikhail A. Trofimov et al.
International Journal of Molecular Sciences
Cell Image Analysis Techniques
article

Cell Painting Identifies Phenotypic Clusters Associated with Anticancer Drug Responses in OVCAR-3 Ovarian Cancer Cells

Dmitry A. Lioznov, Daria Danilenko, Ramziya G. Kiyamova, Mikhail A. Trofimov, Velemir Lavrinenko, Stanislav Tyazhelnikov, Vladimir Popov, Ekaterina V. Litau, Alena A. Shurova
article en

Abstract

Cell Painting is a high-content imaging approach that enables systematic characterization of cellular responses to chemical perturbations through quantitative morphological profiling. Although this methodology has been widely applied in drug discovery and functional genomics, ovarian cancer models remain comparatively underrepresented in Cell Painting studies. In the present work, we established a Cell Painting workflow for OVCAR-3 ovarian cancer cells and applied it to the phenotypic profiling of 54 anticancer compounds representing diverse mechanisms of action. Cells were exposed to compounds under short- and prolonged-treatment conditions, followed by multiplex fluorescent staining, high-content imaging, feature extraction using CellProfiler, and computational analysis of the resulting morphological profiles. Profiles obtained at the two treatment durations were analyzed separately. For each compound, the treatment condition showing greater within-compound consistency in PCA space was additionally retained to construct a combined dataset. Morphological relationships were explored using Pearson correlation analysis, dimensionality reduction, and unsupervised clustering. Morphological profiles showed reproducible relationships among compounds. Compounds with related mechanisms showed greater morphological similarity than expected by chance, although substantial within-class heterogeneity remained. Statistically significant cluster enrichment was observed for microtubule-targeting agents and topoisomerase inhibitors, whereas other mechanism-related patterns were incomplete. Among the evaluated clustering approaches, Leiden and agglomerative clustering produced similar separation of the Combined dataset, with silhouette scores of 0.486 and 0.482, respectively. Cell Painting identified mechanism-associated but heterogeneous morphological responses in OVCAR-3 cells. The results support the use of this model for exploratory phenotypic profiling while highlighting the influence of exposure conditions and the limited ability of morphology alone to recover annotated molecular mechanisms.

International Journal of Molecular SciencesVol. 27(19)
Saint Petersburg Academic University (RU), Kazan Federal University (RU), First Pavlov State Medical University of St. Petersburg (RU), Research Institute of Influenza (RU), BIOCAD (Russia) (RU)
Good health and well-being
Openalex Percentile: Top 13%
Cell Image Analysis Techniques
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