Cell Painting Identifies Phenotypic Clusters Associated with Anticancer Drug Responses in OVCAR-3 Ovarian Cancer Cells
5 affiliations
- 1JSC BIOCAD, 191186 Saint Petersburg, Russia
- 2Biomarker Research Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University, 420008 Kazan, Russia
- 3Nanotechnology Research and Education Centre, Saint Petersburg Academic University, 194021 Saint Petersburg, Russia
- 4Smorodintsev Research Institute of Influenza, 197376 Saint Petersburg, Russia
- 5Department of Infectious Diseases and Epidemiology, Pavlov First Saint Petersburg State Medical University, 197022 Saint Petersburg, Russia
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.
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BibTeX
@article{popov2026cell,
title = {{Cell Painting Identifies Phenotypic Clusters Associated with Anticancer Drug Responses in OVCAR-3 Ovarian Cancer Cells}},
author = {Vladimir E. Popov and Velemir S. Lavrinenko and Ekaterina V. Litau and Alena A. Shurova and Mikhail A. Trofimov and Daria M. Danilenko and Dmitry A. Lioznov and Ramziya G. Kiyamova and Stanislav F. Tyazhelnikov},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = sep,
doi = {10.3390/ijms27198791},
url = {https://www.mdpi.com/1422-0067/27/19/8791},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Cell Painting Identifies Phenotypic Clusters Associated with Anticancer Drug Responses in OVCAR-3 Ovarian Cancer Cells
AU - Vladimir E. Popov
AU - Velemir S. Lavrinenko
AU - Ekaterina V. Litau
AU - Alena A. Shurova
AU - Mikhail A. Trofimov
AU - Daria M. Danilenko
AU - Dmitry A. Lioznov
AU - Ramziya G. Kiyamova
AU - Stanislav F. Tyazhelnikov
PY - 2026
DA - 2026/09/30
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27198791
UR - https://www.mdpi.com/1422-0067/27/19/8791
AB - 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.
SN - 1422-0067
ER -