Single-cell RNA-seq of differentiating human induced pluripotent stem cells reveals dynamic genetic effects on gene expression
Year: 2019
Type: other
Abstract: Recent developments in stem cell biology have enabled studies of cell fate decisions in early human development that are impossible in vivo . However, we lack understanding of how development varies across individuals and the influence of common genetic variants has not been characterised. Meanwhile, advances in single-cell genomics create new possibilities for expression quantitative trait locus (eQTL) mapping using single-cell phenotypes. Here, we studied population variation of endoderm differentiation using full-transcript single-cell RNA-sequencing (Smart-seq2). We used a novel pooled experimental design to scale to large numbers of donors and ameliorate batch effects, assaying cells at four time-points across 72-hour differentiations of human induced pluripotent stem cells (iPSCs) to definitive endoderm. Our experiments yielded data from 36,044 differentiating cells from 125 genetically distinct donors. We ordered individual cells in pseudo-time and grouped cells into iPSC, mesendoderm and definitive endoderm stages. Using linear mixed models, we identified 1,833 eGenes (genes with at least one eQTL) in iPSCs, 1,702 eGenes in mesendoderm and 1,342 eGenes in definitive endoderm cells. We observed regulatory and transcriptional remodelling upon iPS differentiation to definitive endoderm. Our time-course covers developmental stages that have never before been accessible to genetic analyses of molecular traits: 349 of our eQTL variants at the mesendoderm and definitive endoderm stages have not been reported in a recent iPSC eQTL study or in the 49 tissues of the GTEx project. We identified molecular markers that predict differentiation efficiency and hundreds of eQTLs that influence expression dynamically during differentiation. The full-length transcript data allowed us to study allele-specific expression (ASE) effects alongside eQTLs. Our combined analyses across pseudo-time uncovered 785 time-dynamic eQTLs in four clusters of pseudo-temporal behaviour. Finally, ASE analyses revealed interactions with fundamental cellular processes such as cell cycle and respiration, providing evidence that the cellular environment modulates genetic effects on gene expression.
Author: Davis J. McCarthy
Institution: St Vincents Institute of Medical Research
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W2959089144