A morphology-based machine learning model for scoring epithelial-mesenchymal plasticity using organelle dynamics.

ABSTRACT: Re-activation of epithelial-mesenchymal transition (EMT), a key developmental process, contributes to cancer progression and therapy resistance. Modulating EMT could be attractive as a therapeutic strategy, but there is a lack of methods that can quantify EMT states, including hybrid phenotypes. Here, we developed a morphology-based machine learning approach to score EMT based on changes in organelle dynamics. Using the Cell Painting assay and high-throughput microscopy, we trained a histogram gradient boosting classifier to identify stage-specific organelle remodeling during a time course of TGF-β1-induced EMT in mammary epithelial cells. The model achieved robust performance across datasets, capturing EMT kinetics, hybrid states, and reversal by mesenchymal-epithelial transition (MET). I

SUBMITTER: Slager J 

PROVIDER: S-EPMC12800221 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature