biostudies-literatureUnknownTolonen MEuropean Research CouncilNIGMS NIH HHSCell state transitions underlie the emergence of diverse cell types and are traditionally defined by changes in gene expression. Yet these transitions also involve coordinated shifts in cell morphology and behaviour, 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 Xenopus mucociliary epithelium (MCE). From segmentations and trajectories, we extracted dynamic features-cell and nuclear shape, movement, position-to 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 thatbioRxiv : the preprint server for biology2025.09.17.676780https://www.ebi.ac.uk/biostudies/studies/S-EPMC12637583biostudies-literatureSingle-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.PMC12637583101125803R35 GM157082Dumitrascu BTolonen MSedzinski JBeker OKapoor VXu ZfalseSingle-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.Cell state transitions underlie the emergence of diverse cell types and are traditionally defined by changes in gene expression. Yet these transitions also involve coordinated shifts in cell morphology and behaviour, 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 Xenopus mucociliary epithelium (MCE). From segmentations and trajectories, we extracted dynamic features-cell and nuclear shape, movement, position-to 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 that2025-01-01T00:00:00Z2025 Nov2026-06-03T03:17:02.873Z2026-06-03T03:08:56.998ZS-EPMC126375834128004310.1101/2025.09.17.676780