Generalizing RNA velocity to transient cell states through dynamical modeling
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Abstract
RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell's position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation.
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The rate of cell cycle entry of the mutated cells was already near or at the point of saturation due to the CALR mutation.
“However, in contrast to the other myeloid progenitors, within the MkPs (wherein the CALR mutation exerts the greatest phenotypic effects due to the activation of the thrombopoietin receptor 21,54-57 ), the proliferative effects of IFNa were modest in the mutated cells compared to those at baseline ( Fig. 3e ), suggesting that the rate of cell cycle entry of the mutated cells was already near or at”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [55]
The passages on cycling endocrine progenitors (pp. 7–8) concern scVelo’s identification of cell-cycle states, not CALR-mutated megakaryocyte progenitors. Searches for CALR, IFNa, and saturation of cell-cycle entry found no passage addressing the claim in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredWithin the MkPs, the proliferative effects of IFNa were modest in the mutated cells compared to those at baseline.
“However, in contrast to the other myeloid progenitors, within the MkPs (wherein the CALR mutation exerts the greatest phenotypic effects due to the activation of the thrombopoietin receptor 21,54-57 ), the proliferative effects of IFNa were modest in the mutated cells compared to those at baseline ( Fig. 3e ), suggesting that the rate of cell cycle entry of the mutated cells was already near or at”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [55]
The cited paper’s ‘Identifying reaction rates in transient cell populations’ section (p. 4) concerns simulated RNA-splicing kinetics, not IFNa-treated MkPs. Searches for MkPs, CALR mutation, IFNa, and the thrombopoietin receptor found no passage in our copy addressing the claimed proliferation comparison. In the citing sentence, the numbered citations attach to the parenthetical about CALR, while the proliferation comparison points to the citing paper’s Fig. 3e. (LLM, conf=0.78)
Experimental — accuracy not yet measuredDistinct information in exonic vs intronic reads can be used to estimate changes in mRNA expression over time.
“Indeed, the distinct information in exonic vs. intronic reads can be used to estimate changes in mRNA expression over time (Bergen et al., 2020; La Manno et al., 2018).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Bergen et al., 2020
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Capturing Cardiogenesis in Gastruloids
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Single-cell brain organoid screening identifies developmental defects in autism
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“RNA velocity analysis (27, 28) revealed general developmental trajectories from neural progenitor cells to neuronal populations in both the dorsal and ventral telencephalon (Fig.”
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RNA velocity—current challenges and future perspectives
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Self-citationSingle-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
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A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain
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- Authors
- Bergen, V., Lange, M., Peidli, S., Wolf, F. A., & Theis, F. J.
- Affiliations
- Helmholtz Munich (DE)
- Technical University of Munich (DE)
- Corresponding author
- Wolf, F. Alexander
- Theis, Fabian J.
- Venue
- Nature Biotechnology
- Published
- 2020-08-03
- Volume
- 38
- Issue
- 12
- Pages
- 1408-1414
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W3047141442
- Cited by
- 2,951
- References
- 60
- Citations by year
- 2026: 511 · 2025: 608 · 2024: 573 · 2023: 444 · 2022: 428 · 2021: 290 · 2020: 92 · 2019: 4 · 2012: 1
- FWCI
- 104.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (GSC 1006, Collaborative Research Centre 1243, Subproject A17)
- Joachim Herz Stiftung
- Bayer
- Bundesministerium für Bildung und Forschung (01IS18036A, 01IS18053A)
- Helmholtz Association (ZT-I-0007)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- RNA velocity, splicing kinetics, transcriptional dynamics, cell differentiation, neurogenesis, gene expression kinetics, driver genes, gene expression regulation
- MeSH terms
- Animals, Cell Cycle, Endocrine System, Humans, Kinetics, Models, Genetic, RNA, RNA Splicing, Stem Cells, Stochastic Processes, Transcription, Genetic, Dentate Gyrus, Cell Lineage, Mice, Neurogenesis, Single-Cell Analysis
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