Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation

Abstract

Common genetic variants can have profound effects on cellular function, but studying these effects in primary human tissue samples and during development is challenging. Human induced pluripotent stem cell (iPSC) technology holds great promise for assessing these effects across different differentiation contexts. Here, we use an efficient pooling strategy to differentiate 215 iPS cell lines towards a midbrain neural fate, including dopaminergic neurons, and profile over 1 million cells sampled across three differentiation timepoints using single cell RNA sequencing. We find that the proportion of neuronal cells produced by each cell line is highly reproducible over different experimental batches, and identify robust molecular markers in pluripotent cells that predict line-to-line differences in cell fate. We identify expression quantitative trait loci (eQTL) that manifest at different stages of neuronal development, and in response to oxidative stress, by exposing cells to rotenone. We find over one thousand eQTL that colocalise with a known risk locus for a neurological trait, nearly half of which are not found in GTEx. Our study illustrates how coupling single cell transcriptomics with long-term iPSC differentiation can profile mechanistic effects of human trait-associated genetic variants in otherwise inaccessible cell states.

Optimization and scaling of patient-derived brain organoids uncovers deep phenotypes of disease

“…Clone contributes additional information beyond donor ( Fig. 3e-ii , RTG score 0.72 vs. 0.6, respectively), suggesting that variability from iPSC reprogramming and/or culture substantially affects differentiation outcomes even for clones sharing a common genetic background, in agreement with a recent study 25 . For this result, the contribution of clone is determined by comparing each organoid to all other organoids, while the contribution of donor is estimated by comparing only to organoids from different clones.…”

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.…”

Optimising expression quantitative trait locus mapping workflows for single-cell studies

“…Though cell throughput is higher, most often the number of reads quantified is lower for 10X studies, and reads are from the '3 or '5 end of transcripts (or both), but not from full-length transcripts as in Smart-Seq2. Specifically, we selected a midbrain floor plate progenitor (FPP) cell population from a recent differentiation study of iPSCs toward dopaminergic neurons [13] (n=174, Methods). Unfortunately, we did not have matching bulk RNA-seq data to assess replication, and thus could only assess differences in terms of cis-eQTL discovery power.…”

“…The ability to identify cell types and cell states in an unbiased manner from scRNAseq data from a single experiment can be used to define homogeneous cell populations, quantify expression levels within them, and then map eQTL in each of them separately. As a consequence, studies where single-cell expression profiles (rather than bulk) are used to perform eQTL mapping have emerged recently [9][10][11][12][13][14][15], and promise to greatly improve our understanding of the genetic architecture of gene regulation across tissues, in both human disease and development [16].…”

“…We call the corresponding methods "dr-mean", "dr-median", and "dr-sum". We chose to explore the dr aggregation level because in the datasets we considered some donors are present in multiple runs [12,13]. In all cases (i.e.…”