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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 SunChina Pharmaceutical UniversityJSJianhong ShenVanderbilt University

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Implication

This review summarizes modeling strategies for high-resolution transcriptomic data, revealing gene expression dynamics in single cells, emphasizing generative modeling implications.

Key Points

  • Computational tools improve understanding of gene expression in single cells over time and space.
  • Recent advances in modeling strategies enhance analysis of spatiotemporal transcriptomic data.
  • The relationship between dynamical systems and transcriptomic data informs biological insights.
  • Generative modeling techniques provide new ways to interpret cellular processes and states.

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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Also Consider

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  1. 1Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays2022 · 1,891 citations
  2. 2Reconstructing growth and dynamic trajectories from single-cell transcriptomics data2023 · 111 citations