近日，美国密歇根大学教授Joshua D. Welch及其团队的最新研究提出了从空间转录组数据推断拓扑速度。这一研究成果发表在2025年7月16日出版的国际学术期刊《自然—生物技术》上。

在这里，研究小组提出拓扑速度推断（TopoVelo），这是一个从空间转录组数据联合推断细胞命运转变的空间和时间动态的模型。TopoVelo扩展了RNA速度框架，用空间耦合微分方程模拟整个组织的单细胞基因表达动力学。TopoVelo通过大脑皮层的发育来估计细胞速度，了解与配体受体基因表达相关的可解释的空间细胞状态依赖性，并揭示无主旋律神经管关闭的空间特征。最后，课题组研究人员从体外人类发育模型和主题TopoVelo中生成Slide-seq数据，以研究早期分化的空间格局。他们的工作为细胞命运转变的研究引入了一个新的维度，并为组成整个组织的细胞集体动力学建模奠定了基础。

据悉，将空间和时间纳入细胞命运转变模型将是表征邻近细胞、局部生态位因子和细胞迁移之间的相互作用如何促进组织发育的关键一步。

附：英文原文

Title: Topological velocity inference from spatial transcriptomic data

Author: Gu, Yichen, Liu, Jialin, Lee, Kun H., Li, Chen, Lu, Lu, Moline, Jaimee, Guan, Renxiang, Welch, Joshua D.

Issue&Volume: 2025-07-16

Abstract: Incorporating space and time into models of cell fate transition will be a key step toward characterizing how interactions among neighboring cells, local niche factors and cell migration contribute to tissue development. Here we propose Topological Velocity Inference (TopoVelo), a model for jointly inferring spatial and temporal dynamics of cell fate transition from spatial transcriptomic data. TopoVelo extends the RNA velocity framework to model single-cell gene expression dynamics of an entire tissue with spatially coupled differential equations. TopoVelo estimates cell velocity from developing mouse cerebral cortex, learns interpretable spatial cell state dependencies that correlate with the expression of ligand–receptor genes and reveals spatial signatures of mouse neural tube closure. Finally, we generate Slide-seq data from an in vitro model of human development and use TopoVelo to study the spatial patterns of early differentiation. Our work introduces a new dimension into the study of cell fate transitions and lays a foundation for modeling the collective dynamics of cells comprising an entire tissue.

DOI: 10.1038/s41587-025-02688-8

Source: https://www.nature.com/articles/s41587-025-02688-8