CellRank 2: unified fate mapping in multiview single-cell data.

Autor: Weiler P; Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany., Lange M; Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.; Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland., Klein M; Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.; Machine Learning Research, Apple, Paris, France., Pe'er D; Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.; Howard Hughes Medical Institute, Chevy Chase, MD, USA., Theis F; Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany. fabian.theis@helmholtz-munich.de.; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany. fabian.theis@helmholtz-munich.de.; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany. fabian.theis@helmholtz-munich.de.
Jazyk: angličtina
Zdroj: Nature methods [Nat Methods] 2024 Jul; Vol. 21 (7), pp. 1196-1205. Date of Electronic Publication: 2024 Jun 13.
DOI: 10.1038/s41592-024-02303-9
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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