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<p>Cellular processes evolve dynamically across time and space. Single-cell and spatial omics technologies have provided high-resolution snapshots of gene expression, greatly expanding the capability to characterize cellular states. This review summarizes recent modeling strategies for time-series and spatiotemporal transcriptomic data, emphasizing links between dynamical systems, generative modeling, and biological insight. These approaches illustrate how computational tools can deepen our understanding of the dynamic nature of single cells.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月4日

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  • Zhenyi Zhang,
  • Zihan Wang,
  • Yuhao Sun,
  • Jiantao Shen,
  • Qiangwei Peng,
  • Tiejun Li,
  • Peijie Zhou

摘要

Cellular processes evolve dynamically across time and space. Single-cell and spatial omics technologies have provided high-resolution snapshots of gene expression, greatly expanding the capability to characterize cellular states. This review summarizes recent modeling strategies for time-series and spatiotemporal transcriptomic data, emphasizing links between dynamical systems, generative modeling, and biological insight. These approaches illustrate how computational tools can deepen our understanding of the dynamic nature of single cells.