Title

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

UMMS Affiliation

Program in Molecular Medicine; Diabetes Center of Excellence

Publication Date

2019-04-01

Document Type

Article

Disciplines

Bioinformatics | Biotechnology | Cell Biology | Cells | Computational Biology

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.

Keywords

Cell proliferation, Computational models, Differential equations, Population dynamics, T cells

DOI of Published Version

10.1038/s41587-019-0088-0

Source

Nat Biotechnol. 2019 Apr;37(4):461-468. doi: 10.1038/s41587-019-0088-0. Epub 2019 Apr 1. Link to article on publisher's site

Journal/Book/Conference Title

Nature biotechnology

Related Resources

Link to Article in PubMed

PubMed ID

30936567

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