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Generalizing RNA velocity to transient cell states through dynamical modeling

Nature Biotechnology volume 38, pages 1408–1414 (2020)Cite this article

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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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Fig. 1: Solving the full splicing kinetics generalizes RNA velocity to transient populations.
Fig. 2: Resolving subpopulation kinetics and identifying dynamical genes in neurogenesis.
Fig. 3: Delineating cycling progenitors and lineage commitment and disentangling cell fates and regimes of transcriptional activity through latent time in pancreatic endocrinogenesis.

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Data availability

The data sets analyzed in this paper are publicly available and published. The annotated data, results and Python implementation are available at https://scvelo.org. The raw data set of hippocampal dentate gyrus neurogenesis is available in the National Center for Biotechnology Informationʼs Gene Expression Omnibus repository under accession number GSE95753. We included samples from two experimental time points: P12 and P35. The raw data set of pancreatic endocrinogenesis has been deposited under accession number GSE132188. We included samples from the last experimental time point: E15.5.

Code availability

The results reported in this paper and our Python implementation are available at https://scvelo.org.

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Acknowledgements

We thank P. Kharchenko and S. Linnarsson for stimulating discussions, M. Luecken for valuable feedback on the manuscript and S. Tritschler for valuable feedback on the biological applications. This work was supported by BMBF grants (01IS18036A and 01IS18053A); by the German Research Foundation (DFG) within the Collaborative Research Centre 1243, Subproject A17; by the Helmholtz Association (sparse2big and ZT-I-0007); and by the Chan Zuckerberg Initiative DAF (182835). M.L. further acknowledges financial support by the DFG through the Graduate School of Quantitative Biosciences Munich (GSC 1006), by the Joachim Herz Stiftung Foundation and by the Bayer Foundation.

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Authors and Affiliations

  1. Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany

    Volker Bergen, Marius Lange, F. Alexander Wolf & Fabian J. Theis

  2. Department of Mathematics, Technical University of Munich, Munich, Germany

    Volker Bergen, Marius Lange, Stefan Peidli & Fabian J. Theis

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Contributions

V.B. designed and developed the method, implemented scVelo and analyzed the data. F.J.T. conceived the study with contributions from V.B. and F.A.W. V.B., F.A.W. and F.J.T. wrote the manuscript with contributions from the coauthors. S.P. contributed to developing scVelo. M.L. contributed to developing validation metrics. All authors read and approved the final manuscript.

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Correspondence to F. Alexander Wolf or Fabian J. Theis.

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Competing interests

F.A.W. is a full-time employee of Cellarity Inc.; the present work was carried out as an employee of Helmholtz Munich. F.J.T. reports receiving consulting fees from Roche Diagnostics GmbH and Cellarity Inc., and ownership interest in Cellarity, Inc.

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Bergen, V., Lange, M., Peidli, S. et al. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol 38, 1408–1414 (2020). https://doi.org/10.1038/s41587-020-0591-3

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