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

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posted on 2023-05-21, 00:14 authored by Neavin, D, Nguyen, Q, Daniszewsk, MS, Liang, HH, Chiu, HS, Wee, YK, Lukowski, SW, Crombie, DE, Lidgerwood, GE, Hernandez, D, James VickersJames Vickers, Anthony CookAnthony Cook, Palpant, NJ, Pebay, A, Alexander HewittAlexander Hewitt, Powell, JE

Background: The discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) has provided a foundation for in vitro human disease modelling, drug development and population genetics studies. Gene expression plays a critical role in complex disease risk and therapeutic response. However, while the genetic background of reprogrammed cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells which would provide significant resolution. By integrating single cell RNA-sequencing (scRNA-seq) and population genetics, we apply a framework in which to evaluate cell type-specific effects of genetic variation on gene expression.

Results: Here, we perform scRNA-seq on 64,018 fibroblasts from 79 donors and map expression quantitative trait loci (eQTLs) at the level of individual cell types. We demonstrate that the majority of eQTLs detected in fibroblasts are specific to an individual cell subtype. To address if the allelic effects on gene expression are maintained following cell reprogramming, we generate scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type-specific eQTLs in iPSCs and show that the eQTLs in fibroblasts almost entirely disappear during reprogramming.

Conclusions: This work provides an atlas of how genetic variation influences gene expression across cell subtypes and provides evidence for patterns of genetic architecture that lead to cell type-specific eQTL effects.

Background: Mapping expression quantitative trait loci (eQTLs) is a powerful method to study how common genetic variation between individuals influences gene expression [1, 2]. To date, nearly all eQTL studies have been conducted while interrogating ‘bulk’ samples, where the RNA is collected from millions of lysed cells, and therefore, gene expression represents an average across all cells in a sample. However, even ‘bulk’ eQTL studies in different tissues [3, 4] and cultured cell lines [5, 6] have revealed specificity in both the presence and allelic effects of eQTLs [7, 8]. Single cell approaches have already revealed that stem cell cultures do not contain a single homogeneous cell type [5, 6, 9], but instead consist of multiple cell types that have unique transcriptional profiles. For this study, we harnessed recent technological advances for high-throughput generation of single cell data that leveraged cell multiplexing from multiple donors [10,11,12]. This experimental framework enabled the identification of cell type-specific genetic effects on gene expression which revealed eQTLs that were cell type specific and that would not be detected by ‘bulk’ approaches.

Previous studies have identified cell type-specific eQTLs using scRNA-seq which were unobservable in bulk RNA-sequence studies [13,14,15,16,17]. The first study to report this enhanced cell type-specific eQTL detection from scRNA-seq investigated 92 genes measured in 1440 single cells from lymphoblastoid cell lines in 15 individuals [15]. In the current study, we set out to understand the impact of common genetic variants on gene expression in fibroblast and reprogrammed iPSC cell types through eQTL mapping at the level of cell subpopulations.

Results: To identify cell type-specific eQTLs in an unbiased manner, we generated scRNA-seq expression profiles of 83,985 cells—64,018 cultured dermal fibroblasts, generated from skin biopsies from 79 unrelated individuals, and 19,967 iPSCs reprogrammed from 31 of the dermal fibroblast lines (Fig. 1a). After quality control, we used an unsupervised clustering approach [18] to identify six types of fibroblasts and four types of iPSCs (Fig. 1b, c). Fibroblast and iPSC types contained equal distributions of individual donors, pool batches and cell cycle states (Additional file 1: Figure S1 and S2). Cell types were classified based on the relative activity of the regulating transcription factors in fibroblasts (SIX5+, HOXC6+, ATF1+, TEAD2+, KLF10+ and RXRB+) and iPSCs (HIC2+, ATF2+, BRF2+ and CEBPG+) (Fig. 1d, e; Additional file 2: Table S1 and Additional file 3: Table S2; and Table 1). Further, pseudo-trajectory analysis demonstrated that the identified cell types were positioned along a clear lineage trajectory for both fibroblast and iPSC types which was exemplified by the top differentially expressed genes (Additional file 1: Figure S3–4, Additional file 4: Table S3 and Additional file 5: Table S4). We also used an unbiased approach to classify cells against reference transcriptome profiles from the human primary cell atlas [19, 20], which demonstrated that the majority of fibroblasts mapped to the fibroblast or mesenchymal stem cell (MSC) reference, while the majority of iPSCs mapped to the iPSC or embryonic stem cell references (Additional file 1: Figure S5A-B). Due to the phenotypic and transcriptional similarities of fibroblasts and MSCs (Additional file 1: Figure S5C), it is not surprising that some fibroblast cells mapped to the MSC reference [21].


Publication title

Genome Biology



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Wicking Dementia Research Education Centre


BioMed Central Ltd.

Place of publication

United Kingdom

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© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, (, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made

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Expanding knowledge in the biological sciences; Expanding knowledge in the biomedical and clinical sciences

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