ArticleNature methods2026
STORIES: learning cell fate landscapes from spatial transcriptomics using optimal transport.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- CardamomOT: A mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling.PLoS computational biology · 2026Article
- Machine learning and computational approaches to model therapeutic response and resistance in diffuse midline glioma.Journal of neuro-oncology · 2026Review
- st2traj: deconvolution-informed trajectory inference for multi-timepoint spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Riemannian metric learning for alignment of spatial multiomics.Bioinformatics (Oxford, England) · 2026Article
- Transitional cell states at the crossroads of development, disease, and repair-regeneration.Physiological reviews · 2026Review
- GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.Genome biology · 2026Article
- Spatial transcriptomics in epilepsy research: Early successes, opportunities, and challenges.Epilepsia · 2026Article
- Cell-type-specific alkaloid and terpenoid biosynthesis in glandular trichomes: single-cell and spatial transcriptomic perspectives.Frontiers in plant science · 2026Review
- Riemannian Metric Learning for Alignment of Spatial Multiomics.bioRxiv : the preprint server for biology · 2025Article
- Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.NPJ systems biology and applications · 2025Review
- Quantifying Landscape and Flux from Single-Cell Omics: Unraveling the Physical Mechanisms of Cell Function.JACS Au · 2025Review
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Authors and funding
6 authors.
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Abstract
In dynamic biological processes such as development, spatial transcriptomics is revolutionizing the study of the mechanisms underlying spatial organization within tissues. Inferring cell fate trajectories from spatial transcriptomics profiled at several time points has thus emerged as a critical goal, requiring novel computational methods. Wasserstein gradient flow learning is a promising framework for analyzing sequencing data across time, built around a neural network representing the differentiation potential. However, existing gradient flow learning methods face challenges in analyzing spatially resolved transcriptomic data. Here, we propose STORIES, a method that uses an extension of Optimal Transport to learn a spatially informed potential. We benchmark our approach using three large Stereo-seq spatiotemporal atlases and demonstrate superior spatial coherence compared to existing approaches. Finally, we provide an in-depth analysis of axolotl neural regeneration and mouse gliogenesis, recovering gene trends for known markers such as Nptx1 in neuron regeneration and Aldh1l1 in gliogenesis and additional putative drivers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.