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CellRank 2: Unified fate mapping in multiview single-cell data.
Nat. Methods, DOI: 10.1038/s41592-024-02303-9 (2024)
Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.
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Publication type
Article: Journal article
Document type
Scientific Article
Language
english
Publication Year
2024
HGF-reported in Year
2024
ISSN (print) / ISBN
1548-7091
e-ISSN
1548-7105
Journal
Nature Methods
Publisher
Nature Publishing Group
Publishing Place
New York, NY
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-503800-001
Scopus ID
85195872102
PubMed ID
38871986
Erfassungsdatum
2024-06-18