To understand stem cell differentiation along multiple lineages, it is necessary to resolve heterogeneous cellular states and the ancestral relationships between them. Researchers from the Max Planck Institute of Immunobiology and Epigenetics 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, the researchers developed FateID, an iterative supervised learning algorithm for the probabilistic quantification of cell fate bias in progenitor populations. Here they 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. The developers expect that FateID will improve understanding of the process of cell fate choice in complex multi-lineage differentiation systems.
Elucidating transcriptome heterogeneity of multipotent hematopoietic progenitors by scRNA-seq
(a) Workflow of the automated mCEL-Seq2 protocol, featuring reduced volumes and costs compared with traditional workflows. Index-sorting enables simultaneous measurement of single-cell transcriptomes and cell-surface marker expression. (b) t-SNE map based on transcriptome correlation. The gate of origin is indicated for each cell. K, Kit; S, Sca-1; F, Flt3. (c) t-SNE map based on RaceID3 clustering. ILC, innate lymphoid cell. Data in b and c represent 1,949 cells from three independent experiments with n = 4 mice. (d) Log2-transformed mean normalized (norm.) expression of known marker genes across clusters. Cluster number is indicated on the right; color-coding corresponds to that in c. Only clusters with >3 cells were included. A hierarchical clustering dendrogram is shown on the left.
Availability – The FateID algorithm is available as an R package with a detailed vignette from CRAN (package name: FateID), and from GitHub (https://github.com/dgrun/FateID).
Herman JS, Sagar, Grün D. (2018) FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat Methods [Epub ahead of print]. [abstract]