Cells in a tissue influence each other through physical and chemical cues as they differentiate to their final fates and migrate through space. A new technique integrates spatial transcriptomic data with the dynamics of RNA transcription and splicing across an entire tissue to model the directions of cell differentiation and migration.
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References
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018). The research article that introduced RNA velocity.
Gu, Y., Blaauw, D. T. & Welch, J. Variational mixtures of ODEs for inferring cellular gene expression dynamics. In Proc. 39th International Conference on Machine Learning 162, 7887–7901 (PMLR, 2022); https://proceedings.mlr.press/v162/gu22a.html. A machine learning conference paper presenting the foundation for RNA velocity inference with variational autoencoders.
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This is a summary of: Gu, Y. et al. Topological velocity inference from spatial transcriptomic data. Nat. Biotechnol. https://doi.org/10.1038/s41587-025-02688-8 (2025).
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Mapping cell fate transition in space and time. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02704-x
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DOI: https://doi.org/10.1038/s41587-025-02704-x
