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Yet these transitions also involve coordinated shifts in cell morphology and behavior, which remain poorly characterized in densely packed epithelia. We developed a quantitative live-imaging and computational framework to track thousands of individual cells over time in the rapidly differentiating\n                    <jats:italic>Xenopus<\/jats:italic>\n                    mucociliary epithelium (MCE). From segmentations and trajectories, we extracted dynamic features\u2014cell and nuclear shape, movement, and position\u2014to create a time-resolved morphodynamic dataset spanning the full course of differentiation. While single features showed high noise and low separability of ground-truth cell types, supervised machine learning revealed that integrating time-resolved features improves the prediction of final cell fate. Gradient-boosted trees and multinomial logistic regression achieved moderate but consistent accuracy, especially for abundant epithelial lineages. Key discriminants included normalized Z position, membrane\u2013nucleus offset, and absolute experimental time, whereas movement contributed minimally to the results. Our data show that morphodynamic signatures encode predictive information about cell identity and provide a framework linking cellular dynamics with molecular state.\n                  <\/jats:p>","DOI":"10.1038\/s44320-026-00212-x","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T16:35:21Z","timestamp":1778517321000},"page":"1040-1069","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation"],"prefix":"10.1038","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4395-9022","authenticated-orcid":false,"given":"Mari","family":"Tolonen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1507-4358","authenticated-orcid":false,"given":"Ziwei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6226-7235","authenticated-orcid":false,"given":"Ozgur","family":"Beker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5331-7966","authenticated-orcid":false,"given":"Varun","family":"Kapoor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8328-2354","authenticated-orcid":false,"given":"Bianca","family":"Dumitrascu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1788-0329","authenticated-orcid":false,"given":"Jakub","family":"Sedzinski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"212_CR1","doi-asserted-by":"publisher","first-page":"104044","DOI":"10.1016\/j.compbiomed.2020.104044","volume":"126","author":"E Alizadeh","year":"2020","unstructured":"Alizadeh E, Castle J, Quirk A, Taylor CDL, Xu W, Prasad A (2020) Cellular morphological features are predictive markers of cancer cell state. 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