RT Journal Article
SR Electronic
T1 Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 219188
DO 10.1101/219188
A1 Fischer, David S.
A1 Fiedler, Anna K.
A1 Kernfeld, Eric
A1 Genga, Ryan M. J.
A1 Hasenauer, Jan
A1 Maehr, Rene
A1 Theis, Fabian J.
YR 2017
UL http://biorxiv.org/content/early/2017/11/14/219188.abstract
AB Cellular development has traditionally been described as a series of transitions between discrete cell states, such as the sequence of double negative, double positive and single positive stages in T-cell development. Recent advances in single cell transcriptomics suggest an alternative description of development, in which cells follow continuous transcriptomic trajectories. A cell’s state along such a trajectory can be captured with pseudotemporal ordering, which however is not able to predict development of the system in real time. We present pseudodynamics, a mathematical framework that integrates time-series and genetic knock-out information with such transcriptome-based descriptions in order to describe and analyze the real-time evolution of the system. Pseudodynamics models the distribution of a cell population across a continuous cell state coordinate over time based on a stochastic differential equation along developmental trajectories and random switching between trajectories in branching regions. To illustrate feasibility, we use pseudodynamics to estimate cell-state-dependent growth and differentiation of thymic T-cell development. The model approximates a developmental potential function (Waddington’s landscape) and suggests that thymic T-cell development is biphasic and not strictly deterministic before beta-selection. Pseudodynamics generalizes classical discrete population models to continuous states and thus opens possibilities such as probabilistic model selection to single cell genomics.