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

Cuomo, Anna; Seaton, Daniel D; McCarthy, Davis J.; Martinez, Iker; Bonder, Marc Jan; Garcia‐Bernardo, José; Amatya, Shradha; Madrigal, Pedro; Isaacson, Abigail; Buettner, Florian; Knights, Andrew; Natarajan, Kedar Nath; Vallier, Ludovic; Marioni, John C.; Chhatriwala, Mariya; Stegle, Oliver

doi:10.1038/s41467-020-14457-z

Cited by 291 publications

(320 citation statements)

References 62 publications

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“…In particular, human iPSCs facilitate the study of developmental time points and stimulation conditions that would be challenging to obtain in vivo . The creation of cell banks containing hundreds of iPSC lines 1 provides an exciting opportunity to carry out pop ulation-scale studies in vitro [2][3][4][5] . However, differentiating iPSCs is expensive and labour-intensive, and differentiation experiments are difficult to compare due to substantial batch variation.…”

Section: Introductionmentioning

confidence: 99%

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

Jerber

Seaton

Cuomo

et al. 2020

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

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

Jerber

Seaton

Cuomo

et al. 2020

Preprint

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“…Moreover, most of these resources focus on bulk tissue rather than specific cell-types, in which genetic variation can exert differing effects (Cuomo et al, 2019;Strober et al, 2019) .…”

Section: Introductionmentioning

confidence: 99%

Cell-type specific effects of genetic variation on chromatin accessibility during human neuronal differentiation

Liang

Elwell

Aygün

et al. 2020

Preprint

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Common genetic risk for neuropsychiatric disorders is enriched in regulatory elements active during cortical neurogenesis. However, the mechanisms mediating the effects of genetic variants on gene regulation are poorly understood. To determine the functional impact of common genetic variation on the non-coding genome longitudinally during human cortical development, we performed a chromatin accessibility quantitative trait loci (caQTL) analysis in neural progenitor cells and their differentiated neuronal progeny from 92 donors. We identified 8,111 caQTLs in progenitors and 3,676 caQTLs in neurons, with highly temporal, cell-type specific effects. A subset (~20%) of caQTLs were also associated with changes in gene expression. Motif-disrupting alleles of transcriptional activators generally led to decreases in chromatin accessibility, whereas motif-disrupting alleles of repressors led to increases in chromatin accessibility. By integrating cell-type specific caQTLs and brain-relevant genome-wide association data, we were able to fine-map loci and identify regulatory mechanisms underlying non-coding neuropsychiatric disorder risk variants.

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“…Bulk RNA-seq and scRNA-seq from HipSci project In order to link the inferred samples to other 'omics data, we used one scRNA-seq pool for iPSC differentiation in HipSci project (10x Genomics platform, experiment 44, day 0) with six samples: pipw, jejf, qehq, juuy, uilk, and toco [15], and their according bulk RNA-seq data for each sample [22] (http://www.hipsci.org). Both scRNA-seq and bulk RNA-seq data sets were downloaded in .bam files and genotyped on 7.4 millions common bi-allelic variants (minor allele frequency >5%) extracted from the 1000 Genome Project with cellSNP package.…”

Section: Scrna-seq Data From Demuxlet Papermentioning

confidence: 99%

“…Using variant information extracted from the scRNA-seq reads, each cell is assigned to a sample in the pool based on its genetic distance to the known genotypic states in a predefined reference database. While there is a growing interest in multi-sample analyses to study the effect of genetic variation between individuals at single-cell level, e.g., [15,16], the requirement to supply a genotype reference database is prohibitive for studies without a genetic focus per se. Consequently, the potential of pooled experimental designs is currently not fully realized.…”

Section: Introductionmentioning

confidence: 99%

Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference

Huang

McCarthy

Stegle

2019

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The joint analysis of multiple samples using single-cell RNA-seq is a promising experimental design, offering both increased throughput while allowing to account for batch variation. To achieve multi-sample designs, genetic variants that segregate between the samples in the pool have been proposed as natural barcodes for cell demultiplexing. Existing demultiplexing strategies rely on access to complete genotype data from the pooled samples, which greatly limits the applicability of such methods, in particular when genetic variation is not the primary object of study. To address this, we here present Vireo, a computationally efficient Bayesian model to demultiplex single-cell data from pooled experimental designs. Uniquely, our model can be applied in settings when only partial or no genotype information is available. Using simulations based on synthetic mixtures and results on real data, we demonstrate the robustness of our model and illustrate the utility of multi-sample experimental designs for common expression analyses.

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

Cited by 291 publications

References 62 publications

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

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

Cell-type specific effects of genetic variation on chromatin accessibility during human neuronal differentiation

Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference

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