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December 8, 2025npj Systems Biology and ApplicationsOpen Access

Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data

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Authors

ZZZhenyi ZhangPeking UniversityYSYuhao SunPeking UniversityJSJianhong ShenVanderbilt University

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Implication

This review highlights how spatiotemporal and single-cell transcriptomic data improve understanding of dynamic cellular processes, suggesting generative modeling can illustrate cellular states.

Key Points

  • The aim is to explore how spatiotemporal single-cell transcriptomic data can elucidate cell-fate trajectories and dynamic cellular processes.
  • Reviewing recent modeling strategies for transcriptomic data
  • Emphasizing the connection between dynamical systems and computational tools
  • Analyzing time-series data to characterize cellular states
  • Generative modeling enhances biological insight into cellular dynamics
  • Dynamical systems theories provide a framework for interpreting single-cell data
  • Time-series analysis reveals important trends in cellular evolution

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/693624984fa91c937236c1c8https://doi.org/10.1038/s41540-025-00624-9
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