Year: 2025
Type: article
Abstract: 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. More recently, temporally resolved single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data have made it possible to investigate population-level changes across multiple time points and tissue contexts, creating new opportunities to reconstruct cellular dynamics. In this review, we summarize recent progresses in modeling strategies for time-series scRNA-seq and spatiotemporal data. We introduce the mathematical foundations of dynamical systems and generative modeling and highlight algorithmic advances that bring these approaches into practice. We further provide practical guidelines for selecting and applying appropriate methods in various research scenarios. By connecting mathematical frameworks with biological applications, this review illustrates how computational tools can deepen our understanding of the dynamic nature of single cells.
Authors: Zhenyi Zhang, Zihan Wang, Yuhao Sun, Jiantao Shen, Qiangwei Peng, Tiejun Li, Peijie Zhou
Institution: Peking University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,490
OpenAlex ID: W4416976589