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Bergen, V. ; Lange, M. ; Peidli, S.* ; Wolf, F.A. ; Theis, F.J.

Generalizing RNA velocity to transient cell states through dynamical modeling.

Nat. Biotechnol. 38, 1408–1414 (2020)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
scVelo reconstructs transient cell states and differentiation pathways from single-cell RNA-sequencing data.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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Single; Expression; Identification; Protein
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Zeitschrift Nature Biotechnology
Quellenangaben Band: 38, Heft: , Seiten: 1408–1414 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)