Inferring population dynamics from single-cell RNA-sequencing time series data
Program in Molecular Medicine; Diabetes Center of Excellence
Bioinformatics | Biotechnology | Cell Biology | Cells | Computational Biology
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.
Cell proliferation, Computational models, Differential equations, Population dynamics, T cells
DOI of Published Version
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
Fischer DS, Fiedler AK, Kernfeld EM, Genga R, Bastidas-Ponce A, Bakhti M, Lickert H, Hasenauer J, Maehr R, Theis FJ. (2019). Inferring population dynamics from single-cell RNA-sequencing time series data. Program in Molecular Medicine Publications and Presentations. https://doi.org/10.1038/s41587-019-0088-0. Retrieved from https://escholarship.umassmed.edu/pmm_pp/113