Linking temporally matched cpRNA-seq and scRNA-seq data identifies developmental trajectory and timing-related regulatory events

Single-cell RNA sequencing (scRNA-seq) is a powerful method for dissecting intercellular heterogeneity during development. Conventional trajectory analysis provides only a pseudotime of development, and often discards cell-cycle events as confounding factors. Here using matched cell population RNA-seq (cpRNA-seq) as a reference, researchers from the Shanghai Institutes for Biological Sciences developed an “iCpSc” package for integrative analysis of cpRNA-seq and scRNA-seq data. By generating a computational model for reference “biological differentiation time” using cell population data and applying it to single-cell data, they unbiasedly associated cell-cycle checkpoints to the internal molecular timer of single cells. Through inferring a network flow from cpRNA-seq to scRNA-seq data, the researchers predicted a role of M phase in controlling the speed of neural differentiation of mouse embryonic stem cells, and validated it through gene knockout (KO) experiments. By linking temporally matched cpRNA-seq and scRNA-seq data, our approach provides an effective and unbiased approach for identifying developmental trajectory and timing-related regulatory events.

Inferring regulatory events for mESC neural differentiation timing

a Expression level distribution of signaling genes, kinases, and transcription factors, compared with scRNA-seq detectable genes and undetectable cpDEGs (left panel), T-/T1–4-genes, t-/t1–4-genes, or their overlapping genes (right panel) in the cell population RNA-seq data. b Fold enrichment for signaling genes, kinases, and transcription factors in scRNA-seq detectable and undetectable cpDEGs, in T-/T1–4-genes, t-/t1–4-genes, and their overlapping genes. c The largest component of each stage transition eResponseNet. d Significance of enrichment for three cell-cycle checkpoints and seven development-related signaling pathways’ targets in four stages and three stage transitions. e The CSI network among the T _n – or t _n -genes belonging to enriched signaling pathways and cell-cycle checkpoints, respectively (n = 1, 2, 3, and 4). Gene expression PCC-derived CSIs are calculated based on cell population RNA-seq expression values. The stage of a gene is defined by the stage where its pathway is activated (see Methods section). f Subnetwork of Fyn from e. Node shapes indicate cell-cycle checkpoints or signaling pathways. Node colors represent different gene categories. g Expression patterns of genes in f network during mESC neural differentiation

Availability – The iCpSc package can be downloaded from http://www.picb.ac.cn/hanlab/iCpSc.html.