ArticleNature biotechnology2026
Topological velocity inference from spatial transcriptomic data.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- A dependency-aware deep generative model for inferring RNA velocity from spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Review
- STEER: decoupling kinetics with Spatial-Temporal Explainable Expert model for RNA velocity inference.National science review · 2026Article
- Spatial instruction of tissue immunity.ImmunoHorizons · 2026Review
- Article
- Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications.Cancer cell · 2026Review
- RNAPaceDB: a dedicated database to dissect RNA velocity across diverse cell types.Nucleic acids research · 2026Article
- Spatiotemporal Atlas of Heart Development Reveals Blood-Flow-Dependent Cellular, Structural, Metabolic, and Spatial Remodeling.bioRxiv : the preprint server for biology · 2025Article
- stTrace: detecting spatial-temporal domains from spatial transcriptome to trace developmental path.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
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