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Fischer, D.S. ; Fiedler, A. ; Kernfeld, E.M.* ; Genga, R.M.J.* ; Bastidas-Ponce, A. ; Bakhti, M. ; Lickert, H. ; Hasenauer, J. ; Maehr, R.* ; Theis, F.J.

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

Nat. Biotechnol. 37, 461-468 (2019)
Postprint DOI PMC
Open Access Green
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Parameter-estimation; Gene-expression; Beta-cells; Identification; Islets; Fate
Language english
Publication Year 2019
HGF-reported in Year 2019
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Journal Nature Biotechnology
Quellenangaben Volume: 37, Issue: 4, Pages: 461-468 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute of Diabetes and Regeneration Research (IDR)
POF-Topic(s) 30205 - Bioengineering and Digital Health
90000 - German Center for Diabetes Research
30201 - Metabolic Health
Research field(s) Enabling and Novel Technologies
Helmholtz Diabetes Center
PSP Element(s) G-503800-001
G-553800-001
G-501900-231
G-502300-001
DOI 10.1038/s41587-019-0088-0
WOS ID WOS:000463006000028
Scopus ID 85063739999
PubMed ID 30936567
Erfassungsdatum 2019-04-11
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