Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data
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.
Authors
- Qiangwei Peng (ORCID: https://orcid.org/0009-0003-4251-0666)
- Peijie Zhou (ORCID: https://orcid.org/0000-0002-4585-2923)
- Zhenyi Zhang (ORCID: https://orcid.org/0009-0009-5351-7154)
- Zihan Wang (ORCID: https://orcid.org/0000-0001-7563-3839)
- Tiejun Li (ORCID: https://orcid.org/0000-0001-5983-2985)
- Yuhao Sun (ORCID: https://orcid.org/0000-0001-6640-6888)
- Jianhong Shen (ORCID: https://orcid.org/0000-0002-8215-7508)
Institutions
- Peking University (CN)
- Beijing Institute of Big Data Research (CN)
- Chinese Institute for Brain Research (CN)
Publication Details
- Journal
- npj Systems Biology and Applications
- Published
- 2025-12-04
- DOI
- https://doi.org/10.1038/s41540-025-00624-9
- Citations
- 2
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- National Natural Science Foundation of China
- Peking University