Inferring population dynamics from single-cell RNA-sequencing time series data.

Autor: Fischer DS; Institute of Computational Biology, Helmholz Zentrum München, Neuherberg, Germany.; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany., Fiedler AK; Institute of Computational Biology, Helmholz Zentrum München, Neuherberg, Germany.; Department of Mathematics, Technical University of Munich, Garching bei München, Germany., Kernfeld EM; Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA, USA., Genga RMJ; Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA, USA., Bastidas-Ponce A; Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany.; Institute of Stem Cell Research, Helmholtz Zentrum München, Neuherberg, Germany.; Medical Faculty, Technical University of Munich, Munich, Germany.; German Center for Diabetes Research (DZD), Neuherberg, Germany., Bakhti M; Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany.; Institute of Stem Cell Research, Helmholtz Zentrum München, Neuherberg, Germany.; German Center for Diabetes Research (DZD), Neuherberg, Germany., Lickert H; Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany.; Institute of Stem Cell Research, Helmholtz Zentrum München, Neuherberg, Germany.; Medical Faculty, Technical University of Munich, Munich, Germany.; German Center for Diabetes Research (DZD), Neuherberg, Germany., Hasenauer J; Institute of Computational Biology, Helmholz Zentrum München, Neuherberg, Germany.; Department of Mathematics, Technical University of Munich, Garching bei München, Germany., Maehr R; Program in Molecular Medicine, Diabetes Center of Excellence, University of Massachusetts Medical School, Worcester, MA, USA., Theis FJ; Institute of Computational Biology, Helmholz Zentrum München, Neuherberg, Germany. fabian.theis@helmholtz-muenchen.de.; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany. fabian.theis@helmholtz-muenchen.de.; Department of Mathematics, Technical University of Munich, Garching bei München, Germany. fabian.theis@helmholtz-muenchen.de.
Jazyk: angličtina
Zdroj: Nature biotechnology [Nat Biotechnol] 2019 Apr; Vol. 37 (4), pp. 461-468. Date of Electronic Publication: 2019 Apr 01.
DOI: 10.1038/s41587-019-0088-0
Abstrakt: Recent single-cell RNA-sequencing studies have suggested that cells follow continuous transcriptomic trajectories in an asynchronous fashion during development. However, observations of cell flux along trajectories are confounded with population size effects in snapshot experiments and are therefore hard to interpret. In particular, changes in proliferation and death rates can be mistaken for cell flux. Here we present pseudodynamics, a mathematical framework that reconciles population dynamics with the concepts underlying developmental trajectories inferred from time-series single-cell data. Pseudodynamics models population distribution shifts across trajectories to quantify selection pressure, population expansion, and developmental potentials. Applying this model to time-resolved single-cell RNA-sequencing of T-cell and pancreatic beta cell maturation, we characterize proliferation and apoptosis rates and identify key developmental checkpoints, data inaccessible to existing approaches.
Databáze: MEDLINE
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