Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression

Abstract

28Recent developments in stem cell biology have enabled the study of cell fate decisions in early 29 human development that are impossible to study in vivo. However, understanding how 30 development varies across individuals and, in particular, the influence of common genetic 31 variants during this process has not been characterised. Here, we exploit human iPS cell lines 32 from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study 33 population variation of endoderm differentiation. We identify molecular markers that are 34 predictive of differentiation efficiency, and utilise heterogeneity in the genetic background 35 across individuals to map hundreds of expression quantitative trait loci that influence 36 expression dynamically during differentiation and across cellular contexts. 37Population-scale single-cell profiling of differentiating iPS cells 63 We considered a panel of well-characterized human iPSC lines derived from 125 unrelated 64 donors from the Human Induced Pluripotent Stem Cell initiative (HipSci) collection [1]. In order 65to increase throughput and mitigate the effects of batch variation, we exploited a novel pooled 66 differentiation assay, combining sets of four to six lines in one well prior to differentiation (28 67 differentiation experiments performed in total; hereon "experiments"; Fig. 1A, S1, S2). Cells 68 were collected at four differentiation time points (iPSC; one, two and three days post initiation 69 -hereon day0, day1, day2 and day3) and their transcriptomes were assayed using full-length 70RNA-sequencing (Smart-Seq2 [7]) alongside the expression of selected cell surface markers 71using FACS (TRA-1-60, CXCR4; Fig. S3, S4; Methods). Following quality control (QC), 72 36,044 cells were retained for downstream analysis, across which 11,231 genes were 73 expressed ( Fig. S5; Methods). Exploiting that each cell line's genotype acts as a unique 74 barcode, we demultiplexed the pooled cell populations, enabling identification of the cell line 75 of origin for each cell (similar to [8]; Methods). At each time point, cells from between 104 and 76 112 donors were captured, with each donor being represented by an average of 286 cells 77 (after QC, Fig. S2; Tables S1, S2; Methods). The success of the differentiation protocol was 78 validated using canonical cell-surface marker expression: consistent with previous studies [9], 79 an average of 72% cells were TRA-1-60(+) in the undifferentiated state (day0) and an average 80 of 49% of cells were CXCR4(+) three days post differentiation (day3; Fig. S3). 81 82Variance component analysis across all genes (using a linear mixed model; Methods) 83 revealed the time point of collection as the main source of variation, followed by the cell line 84 of origin and the experimental batch (Fig. 1B). Consistent with this, the first Principal 85 Component (PC) was strongly associated with differentiation time (Fig. 1C, S6; Methods), 86 motivating its use to order cells by their differentiation status (hereafter "pseudotime" ...

Common genetic variation in humans impactsin vitrosusceptibility to SARS-CoV-2 infection

“…Perturb-seq (Dixit et al, 2016) and ECCITE-seq (Mimitou et al, 2019)) and pooled eQTL screens (e.g. crisprQTL mapping (Gasperini et al, 2019) and Census-seq (Cuomo et al, 2020;Jerber et al, 2020;Mitchell et al, 2020;Neavin et al, 2020)) approaches will more rapidly identify host variants that impact the entry, replication and egress of SARS-CoV-2, cellular survival, and immune response.…”

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

“…Discussion Single-cell RNA-seq eQTL analyses define an emerging research niche that brings major benefits for the understanding of functional genetic variation including the identification of cell-type and condition-specific correlations (2,(13)(14)(15)(16)45). In this paper, we present a new eQTL-based analysis in a scRNA-seq setting -scReQTL -which uses the VAFRNA at expressed heterozygous SNVs in place of the genotypes, to correlate allele prevalence to geneexpression levels.…”

“…By utilizing the advantages of the single cell resolution, these studies have revealed many new regulatory SNVs, including those with cell-specific or transient effects (2)(3)(4)(13)(14)(15)(16). To assess GE, these methods employ approaches specific to single cell transcriptomics, including accounting for drop-outs, classification of cells by type, and assessments of progressive cell stages (2)(3)(4)(13)(14)(15)(16). SNV information is traditionally obtained from the genotypes across multiple individuals and encoded as the number of alleles (0, 1 or 2) bearing the variant nucleotide.…”

Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells

“…Previous studies have identified cell type specific eQTL using scRNA-seq which were unobservable in bulk RNA-sequence studies 25–29 . The first study to report this enhanced cell type specific eQTL detection from scRNA-seq investigated 92 genes measured in 1,440 single cells from lymphoblastoid cell lines in 15 individuals 27 .…”