Inferring population dynamics from single-cell RNA-sequencing time series data
Fischer DS, Fiedler AK, Kernfeld EM, Genga RMJ, Bastidas-Ponce A, Bakhti M, Lickert H, Hasenauer J, Maehr R, Theis FJ (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 37
Pages Range: 461-468
Journal Issue: 4
DOI: 10.1038/s41587-019-0088-0
Abstract
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.
Involved external institutions
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
University of Massachusetts Amherst (UMass)
United States (USA) (US)
Germany (DE)
University of Massachusetts Amherst (UMass)
United States (USA) (US)
How to cite
APA:
Fischer, D.S., Fiedler, A.K., Kernfeld, E.M., Genga, R.M.J., Bastidas-Ponce, A., Bakhti, M.,... Theis, F.J. (2019). Inferring population dynamics from single-cell RNA-sequencing time series data. Nature Biotechnology, 37(4), 461-468. https://doi.org/10.1038/s41587-019-0088-0
MLA:
Fischer, David S., et al. "Inferring population dynamics from single-cell RNA-sequencing time series data." Nature Biotechnology 37.4 (2019): 461-468.
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