Abstract
Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency of individual lines, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.
Highlights
Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo
We considered a panel of well-characterised human induced pluripotent stem cells (iPSCs) lines derived from 125 unrelated British donors from the Human Induced Pluripotent Stem Cell initiative (HipSci) collection[1]
Cells were collected at four differentiation time points and their transcriptomes were assayed using full-length RNA-sequencing (Smart-Seq27) alongside the expression of selected cell surface markers using Fluorescence activated cell sorting (FACS) (TRA-1-60, CXCR4; Supplementary Fig. 3, 4; Methods)
Summary
To formally test for eQTL effects that change dynamically across differentiation (dynamic QTL), we tested for associations between pseudotime and the genetic effect size using a linear model (genetic effect defined based on ASE at the level of single cells; likelihood ratio test, considering linear and quadratic pseudotime), uncovering a total of 899 time dynamic eQTL (out of the joint set of 4422 eQTL across all stages; FDR < 10%; Methods), including a substantial fraction of eQTL that were not stage-specific (Supplementary Data 3). We asked whether levels of gene expression at the iPSC stage could represent molecular markers of differentiation efficiency This revealed 38 associations (FDR 10%, 11,231 genes tested; Supplementary Data 13), 9 of which were observed when using independent bulk RNA-seq data from the same cell. We did not identify any striking patterns other than the reported overrepresentation of chromosome X genes, partly due to a small sample size
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