FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data.

Abstract

To understand stem cell differentiation along multiple lineages, it is necessary to resolve heterogeneous cellular states and the ancestral relationships between them. We developed a robotic miniaturized CEL-Seq2 implementation to carry out deep single-cell RNA-seq of ∼2,000 mouse hematopoietic progenitors enriched for lymphoid lineages, and used an improved clustering algorithm, RaceID3, to identify cell types. To resolve subtle transcriptome differences indicative of lineage biases, we developed FateID, an iterative supervised learning algorithm for the probabilistic quantification of cell fate bias in progenitor populations. Here we used FateID to delineate domains of fate bias and enable the derivation of high-resolution differentiation trajectories, thereby revealing a common progenitor population of B cells and plasmacytoid dendritic cells, which we validated by in vitro differentiation assays. We expect that FateID will improve understanding of the process of cell fate choice in complex multi-lineage differentiation systems.