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

Justin Slager1, Francesca Gatto1, Benjamin Frey2

  • 1Division of Pathology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden.

Communications Biology
|December 10, 2025
PubMed
Summary

We developed a machine learning method to quantify epithelial-mesenchymal transition (EMT) states by analyzing organelle dynamics. This approach accurately scores EMT in various cancer cells, offering a new tool for drug discovery and therapy resistance research.

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Area of Science:

  • Cell Biology
  • Cancer Research
  • Computational Biology

Background:

  • Epithelial-mesenchymal transition (EMT) is crucial for cancer progression and therapy resistance.
  • Quantifying EMT states, including hybrid phenotypes, is challenging but vital for therapeutic strategies.
  • Existing methods lack the ability to capture the full spectrum of EMT dynamics.

Purpose of the Study:

  • To develop a novel morphology-based machine learning approach to quantify EMT states.
  • To establish a scalable framework for assessing epithelial-mesenchymal plasticity.
  • To enable drug discovery and identify resistance-overcoming strategies.

Main Methods:

  • Utilized the Cell Painting assay and high-throughput microscopy to profile organelle dynamics.
  • Trained a histogram gradient boosting classifier on time-course data of TGF-β1-induced EMT.
  • Validated the model's performance across different datasets and cell types.

Main Results:

  • The machine learning model accurately quantified EMT kinetics, hybrid states, and mesenchymal-epithelial transition (MET).
  • Demonstrated cross-species, cross-inducer, and cross-cancer applicability in human breast and lung cancer cells.
  • Established organelle morphology profiling as a robust method for assessing epithelial-mesenchymal plasticity.

Conclusions:

  • Organelle morphology profiling provides a scalable and accurate method for quantifying EMT.
  • This approach can be applied to various cancer types and inducers, including hypoxia.
  • The developed framework offers a promising platform for therapeutic interventions against EMT-associated cancer progression and resistance.