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stVCR: spatiotemporal dynamics of single cells

Nature Methods volume 23, pages 542–553 (2026) Cite this article

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Abstract

Time-series spatial transcriptomics with single-cell resolution provides an opportunity to study cell differentiation, proliferation and migration in physical space over time. However, because sequencing is destructive, reconstructing spatiotemporal dynamics from snapshots remains challenging. In particular, inferring migration is difficult because samples collected at different time points often lie in different coordinate systems across biological replicates. Here we show that spatiotemporal video cassette recorder (stVCR), a generative deep-learning framework, can reconstruct continuous cell differentiation, proliferation, physical-space migration and spatial alignment in an end-to-end manner. The model integrates dynamical optimal transport in an unbalanced setting, density matching that is invariant to rigid transformations, and biologically informed priors to preserve spatial structure. stVCR also enables interpretable analysis of how phenotype transitions interact with spatial migration and proliferation. Using both simulated and real datasets, we demonstrate that stVCR is effective and robust, and we apply it to uncover spatiotemporal dynamics in axolotl brain regeneration and 3D Drosophila embryo development.

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Fig. 1: Overview of stVCR.
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Fig. 2: Benchmark of stVCR.
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Fig. 3: stVCR reconstructs spatiotemporal dynamics of axolotl brain regeneration.
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Fig. 4: stVCR gene-level analysis of axolotl brain regeneration.
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Fig. 5: stVCR analysis of 3D Drosophila embryos and organs with biological state-transition prior and spatial structure preserving prior.
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Data availability

All the datasets used in this paper are publicly available. The simulation datasets of synthetic circuits are available at https://github.com/QiangweiPeng/stVCR/tree/main/tutorial. The axolotl brain-regeneration datasets are freely accessible in CNGB Nucleotide Sequence Archive under accession code CNP0002068. Processed data can be downloaded from https://db.cngb.org/stomics/artista/ (ref. 59). The processed 3D Drosophila embryo datasets can be downloaded from the Spateo package53 (https://www.dropbox.com/s/bvstb3en5kc6wui/E7-9h_cellbin_tdr_v2.h5ad?dl=1 and https://www.dropbox.com/s/q02sx6acvcqaf35/E9-10h_cellbin_tdr_v2.h5ad?dl=1).

Code availability

stVCR is implemented in Python and is available at https://github.com/QiangweiPeng/stVCR. The notebooks to reproduce all the results in the paper are available at https://github.com/QiangweiPeng/stVCR/tree/main/tutorial. To reproduce the figures and tables presented in the paper, please visit our Code Ocean capsule at https://doi.org/10.24433/CO.9796381.v1 (ref. 77).

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Acknowledgements

T.L. and Q.P. acknowledge the support of the National Key R&D Program of China under grant 2021YFA1003301, and National Science Foundation of China under grant 12288101. P.Z. acknowledges the support of the National Science Foundation of China under grants 12288101 and 8206100646, and the Fundamental Research Funds for the Central Universities. We also thank the High-performance Computing Platform of Peking University.

Author information

Authors and Affiliations

  1. LMAM and School of Mathematical Sciences, Peking University, Beijing, China

    Qiangwei Peng & Tiejun Li

  2. Center for Machine Learning Research, Peking University, Beijing, China

    Peijie Zhou & Tiejun Li

  3. Center for Quantitative Biology, Peking University, Beijing, China

    Peijie Zhou

  4. National Engineering Laboratory for Big Data Analysis and Applications, Beijing, China

    Peijie Zhou & Tiejun Li

  5. AI for Science Institute, Beijing, China

    Peijie Zhou

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  1. Qiangwei Peng
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  2. Peijie Zhou
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  3. Tiejun Li
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Contributions

All authors conceived the project. Q.P. designed and implemented the algorithm, and performed data analysis. All authors interpreted the results and wrote the paper. Q.P. wrote the supplementary materials. P.Z. and T.L. supervised the research.

Corresponding authors

Correspondence to Peijie Zhou or Tiejun Li.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

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Peng, Q., Zhou, P. & Li, T. stVCR: spatiotemporal dynamics of single cells. Nat Methods 23, 542–553 (2026). https://doi.org/10.1038/s41592-026-03010-3

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