Cited by 2,951Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisGenomics and Phylogenetic Studies

Generalizing RNA velocity to transient cell states through dynamical modeling

Bergen, Volker, Lange, Marius, Peidli, Stefan, Wolf, F. Alexander, Theis, Fabian J.·2020·Nature Biotechnology

Published version of a preprint

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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Crossref · 10.1038/s41587-020-0591-3

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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 measured
  • Within 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 measured
  • Distinct 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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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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