ArticleNature methods2026
stVCR: spatiotemporal dynamics of single cells.
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 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Semi-metric optimal transport enables robust spatial multi-omics integration.Research square · 2026Article
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- st2traj: deconvolution-informed trajectory inference for multi-timepoint spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Review
- StPedf: Cell trajectory inference of spatial transcriptomics via spatial proximity embedding and spatial density-adaptive fusion.PLoS computational biology · 2026Article
- Kinemomics: spatiotemporal morphodynamic mapping of ventricular kinematic subpopulations in organotypic fetal heart slices.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
41820580What OpenQuestion holds
Registered trials
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.