Open AccessInferring population dynamics from single-cell RNA-sequencing time series data
Author(s) -
David Fischer,
Anna Fiedler,
Eric Kernfeld,
Ryan M. Genga,
Aimée Bastidas-Ponce,
Mostafa Bakhti,
Heiko Lickert,
Jan Hasenauer,
René Maehr,
Fabian J. Theis
Publication year - 2019
Publication title -
nature biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 15.358
H-Index - 445
eISSN - 1546-1696
pISSN - 1087-0156
DOI - 10.1038/s41587-019-0088-0
Subject(s) - population , snapshot (computer storage) , biology , transcriptome , single cell analysis , rna , single cell sequencing , computational biology , asynchronous communication , cell , phenotype , computer science , genetics , gene , gene expression , computer network , demography , sociology , exome sequencing , operating system
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.