Pseudodynamics – inferring population dynamics from single-cell RNA-sequencing time series data

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.

Researchers from Helmholz Zentrum München and the Technical University of Munich have developed 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 population-based view of single-cell RNA-seq time-series experiments: concept of pseudodynamics and example fits on a mouse embryonic stem cell differentiation data set

rna-seq

a, Development can be modeled as the temporal progression of a population density in transcriptome (cell state) space. Here, the developmental process is a branched lineage from a progenitor to two terminal fates. b, Dimension reductions of the full cell state space are useful for dynamic modelling. Discrete cell types, such as from FACS gates, were previously used for ordinary differential equation models. Branched trajectories with pseudotime coordinates can be used in the context of pseudodynamics. c, Conceptual overview of the pseudodynamics algorithm. The input consists of developmental progress data (normalized distributions across cell state) and population size data (number of cells) for each time point. The output contains interpretable parameter estimates and imputed samples at unseen time points (dotted densities). d, Diffusion map of mouse embryonic stem cell development in vitro after leukemia inhibitory factor (LIF) removal1. Color: days after LIF removal in cell culture. e,f, Kernel density estimate and simulated density of cells across cell state coordinate (diffusion pseudotime) at four sampled time points (n0 = 933, n2 = 303, n4 = 683, n7 = 798 cells) for regularized (ρ = 1) and unregularized (ρ = 0) model fits. Colored density: kernel density estimate, solid line: simulated density based on model fitted to all data, dotted line: simulated density in leave-one-time-point-out cross-validation.

Availabilityhttps://github.com/theislab/pseudodynamics

Fischer DS, Fiedler AK, Kernfeld EM, Genga RMJ, 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. Nat Biotechnol [Epub ahead of print]. [abstract]

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