The single-cell eQTLGen consortium
- University of Groningen, University Medical Center Groningen, Netherlands
- Wellcome Sanger Institute, United Kingdom
- RIKEN Center for Integrative Medical Sciences, Japan
- European Molecular Biology Laboratory, European Bioinformatics Institute, United Kingdom
- DKFZ, Germany
- New York University Abu Dhabi, United Arab Emirates
- University of California, San Francisco, United States
- Garvan Institute, Australia
- Helmholtz Zentrum München, Germany
- Leiden University Medical Center, Netherlands
- Institute of Computational Biology, Helmholtz Zentrum München, Technical University of Munich, Germany
Abstract
In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.
Data availability
Not applicable
Article and author information
Author details
Monique GP van der Wijst
Funding
Dutch Research Council (NWO-Veni 192.029)
- Monique GP van der Wijst
Dutch Research Council (ZonMW-VIDI 917.14.374)
- Lude Franke
European Research Council (ERC Starting grant Immrisk 637640)
- Lude Franke
Oncode Institute
- Lude Franke
National Health and Medical Research Council (Investigator grant 1175781)
- Joseph Powell
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, van der Wijst et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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The single-cell eQTLGen consortium
eLife 9:e52155.
https://doi.org/10.7554/eLife.52155
Further reading
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- Evolutionary Biology
- Genetics and Genomics
Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.
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- Developmental Biology
- Genetics and Genomics
The establishment and growth of the arterial endothelium require the coordinated expression of numerous genes. However, regulation of this process is not yet fully understood. Here, we combined in silico analysis with transgenic mice and zebrafish models to characterize arterial-specific enhancers associated with eight key arterial identity genes (Acvrl1/Alk1, Cxcr4, Cxcl12, Efnb2, Gja4/Cx37, Gja5/Cx40, Nrp1, and Unc5b). Next, to elucidate the regulatory pathways upstream of arterial gene transcription, we investigated the transcription factors binding each arterial enhancer compared to a similar assessment of non-arterial endothelial enhancers. These results found that binding of SOXF and ETS factors was a common occurrence at both arterial and pan-endothelial enhancers, suggesting neither are sufficient to direct arterial specificity. Conversely, FOX motifs independent of ETS motifs were over-represented at arterial enhancers. Further, MEF2 and RBPJ binding was enriched but not ubiquitous at arterial enhancers, potentially linked to specific patterns of behaviour within the arterial endothelium. Lastly, there was no shared or arterial-specific signature for WNT-associated TCF/LEF, TGFβ/BMP-associated SMAD1/5 and SMAD2/3, shear stress-associated KLF4, or venous-enriched NR2F2. This cohort of well-characterized and in vivo-verified enhancers can now provide a platform for future studies into the interaction of different transcriptional and signaling pathways with arterial gene expression.
