{"entity": "researcher", "timestamp": "2026-07-17T05:49:27.620Z", "family": "Fuxe", "given": "Jonas", "initials": "J", "orcid": "0000-0003-4576-9377", "affiliations": ["Division of Pathology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden. jonas.fuxe@ki.se."], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/df87648f16bc45b9999eb5c0da269a4e.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/df87648f16bc45b9999eb5c0da269a4e"}}, "publications": [{"entity": "publication", "iuid": "bdaeb5bc238e46fcbec53a574a0e9f89", "links": {"self": {"href": "https://publications.scilifelab.se/publication/bdaeb5bc238e46fcbec53a574a0e9f89.json"}, "display": {"href": "https://publications.scilifelab.se/publication/bdaeb5bc238e46fcbec53a574a0e9f89"}}, "title": "A morphology-based machine learning model for scoring epithelial-mesenchymal plasticity using organelle dynamics.", "authors": [{"family": "Slager", "given": "Justin", "initials": "J", "orcid": "0009-0005-0481-8154", "researcher": {"href": "https://publications.scilifelab.se/researcher/b628fcc675354b86989c79e30cedc71a.json"}}, {"family": "Gatto", "given": "Francesca", "initials": "F"}, {"family": "Frey", "given": "Benjamin", "initials": "B", "orcid": "0009-0004-7649-8340", "researcher": {"href": "https://publications.scilifelab.se/researcher/2e352dfc1f814b2abf8d38cde784557c.json"}}, {"family": "Shi", "given": "Wenyang", "initials": "W"}, {"family": "Porebski", "given": "Bartlomiej", "initials": "B"}, {"family": "Carreras-Puigvert", "given": "Jordi", "initials": "J"}, {"family": "Parniewska", "given": "Malgorzata Maria", "initials": "MM"}, {"family": "Fuxe", "given": "Jonas", "initials": "J", "orcid": "0000-0003-4576-9377", "researcher": {"href": "https://publications.scilifelab.se/researcher/df87648f16bc45b9999eb5c0da269a4e.json"}}], "type": "journal article", "published": "2025-12-10", "journal": {"title": "Commun Biol", "issn": "2399-3642", "issn-l": "2399-3642"}, "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-\u03b21-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). Importantly, the method accurately scored EMT in human breast cancer cells and lung cancer cells undergoing hypoxia-induced EMT, demonstrating cross-species, cross-inducer, and cross-cancer applicability. The results establish organelle morphology profiling as a scalable framework for quantifying epithelial-mesenchymal plasticity. The method offers a platform for drug discovery and identifying strategies to overcome EMT-associated resistance.", "doi": "10.1038/s42003-025-09326-8", "pmid": "41372576", "labels": {"Chemical Biology Consortium Sweden": "Collaborative"}, "xrefs": [{"db": "pii", "key": "10.1038/s42003-025-09326-8"}], "notes": [], "created": "2025-12-11T16:08:39.796Z", "modified": "2025-12-11T16:08:40.050Z"}]}