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<p>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 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 <i>Xenopus</i> mucociliary epithelium (MCE). From segmentations and trajectories, we extracted dynamic features—cell and nuclear shape, movement, and 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 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–nucleus 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.</p>

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Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月11日

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  • Mari Tolonen,
  • Ziwei Xu,
  • Ozgur Beker,
  • Varun Kapoor,
  • Bianca Dumitrascu,
  • Jakub Sedzinski

摘要

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 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 Xenopus mucociliary epithelium (MCE). From segmentations and trajectories, we extracted dynamic features—cell and nuclear shape, movement, and 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 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–nucleus 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.