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

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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

rna-seq

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 – or t -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.


Sun N, Yu X, Li F, Liu D, Suo S, Chen W, Chen S, Song L, Green CD, McDermott J, Shen Q, Jing N, Han JDJ. (2017) Inference of differentiation time for single cell transcriptomes using cell population reference data. Nature Comm [Epub ahead of print]. [article]

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