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Inferring population dynamics from single-cell RNA-sequencing time series data
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Inferring population dynamics from single-cell RNA-sequencing time series data
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Journal Article
Inferring population dynamics from single-cell RNA-sequencing time series data
2019
Overview
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.
A new computational method allows key developmental checkpoints and important parameters of population dynamics to be inferred from single-cell RNA-sequencing time series data.
Publisher
Nature Publishing Group US,Nature Publishing Group
Subject
/ Animals
/ Biomedical and Life Sciences
/ Biomedical Engineering/Biotechnology
/ Cell Differentiation - genetics
/ Cell Proliferation - genetics
/ Female
/ Insulin-Secreting Cells - cytology
/ Insulin-Secreting Cells - metabolism
/ Male
/ Methods
/ Mice
/ Mouse Embryonic Stem Cells - cytology
/ Mouse Embryonic Stem Cells - metabolism
/ Pancreas
/ RNA
/ Sequence Analysis, RNA - statistics & numerical data
/ Single-Cell Analysis - statistics & numerical data
/ T cells
