PT - JOURNAL ARTICLE
AU - Fischer, David S.
AU - Fiedler, Anna K.
AU - Kernfeld, Eric
AU - Genga, Ryan M. J.
AU - Hasenauer, Jan
AU - Maehr, Rene
AU - Theis, Fabian J.
TI - Beyond pseudotime: Following T-cell maturation in single-cell RNAseq time series
DP - 2017 Jan 01
TA - bioRxiv
4099 - http://biorxiv.org/content/early/2017/11/14/219188.short
4100 - http://biorxiv.org/content/early/2017/11/14/219188.full
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.