The single-cell eQTLGen consortium

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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.

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Most cited references 66

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Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis

Christopher S. Smillie, Moshe Biton, Jose Ordovas-Montanes … (2019)

Genome-wide association studies (GWAS) have revealed risk alleles for ulcerative colitis (UC). To understand their cell type specificities and pathways of action, we generate an atlas of 366,650 cells from the colon mucosa of 18 UC patients and 12 healthy individuals, revealing 51 epithelial, stromal, and immune cell subsets, including BEST4+ enterocytes, microfold-like cells, and IL13RA2+IL11+ inflammatory fibroblasts, which we associate with resistance to anti-TNF treatment. Inflammatory fibroblasts, inflammatory monocytes, microfold-like cells, and T cells that co-express CD8 and IL-17 expand with disease, forming intercellular interaction hubs. Many UC risk genes are cell type specific and co-regulated within relatively few gene modules, suggesting convergence onto limited sets of cell types and pathways. Using this observation, we nominate and infer functions for specific risk genes across GWAS loci. Our work provides a framework for interrogating complex human diseases and mapping risk variants to cell types and pathways.

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Accounting for technical noise in single-cell RNA-seq experiments.

Philip Brennecke, Simon Anders, Jong Kim … (2013)

Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.

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A cellular census of human lungs identifies novel cell states in health and in asthma

Felipe A. Vieira Braga, Gozde Kar, Marijn Berg … (2019)

Human lungs enable efficient gas exchange and form an interface with the environment, which depends on mucosal immunity for protection against infectious agents. Tightly controlled interactions between structural and immune cells are required to maintain lung homeostasis. Here, we use single-cell transcriptomics to chart the cellular landscape of upper and lower airways and lung parenchyma in healthy lungs, and lower airways in asthmatic lungs. We report location-dependent airway epithelial cell states and a novel subset of tissue-resident memory T cells. In the lower airways of patients with asthma, mucous cell hyperplasia is shown to stem from a novel mucous ciliated cell state, as well as goblet cell hyperplasia. We report the presence of pathogenic effector type 2 helper T cells (TH2) in asthmatic lungs and find evidence for type 2 cytokines in maintaining the altered epithelial cell states. Unbiased analysis of cell-cell interactions identifies a shift from airway structural cell communication in healthy lungs to a TH2-dominated interactome in asthmatic lungs.

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Author and article information

Contributors

MGP van der Wijst:

ORCID: https://orcid.org/0000-0003-1520-3970

Helena Pérez Valle: Role: Reviewing Editor

Peter Rodgers: Role: Senior Editor

Journal

Journal ID (nlm-ta): eLife

Journal ID (iso-abbrev): Elife

Journal ID (publisher-id): eLife

Title: eLife

Publisher: eLife Sciences Publications, Ltd

ISSN (Electronic): 2050-084X

Publication date (Electronic, pub): 09 March 2020

Publication date Collection: 2020

Volume: 9

Electronic Location Identifier: e52155

Affiliations

[1 ]Department of Genetics, Oncode Institute, University of Groningen, University Medical Center Groningen GroningenNetherlands

[2 ]Department of Cardiology, University of Groningen, University Medical Center Groningen GroningenNetherlands

[3 ]Wellcome Sanger Institute HinxtonUnited Kingdom

[4 ]Open Targets HinxtonUnited Kingdom

[5 ]RIKEN Center for Integrative Medical Sciences YokahamaJapan

[6 ]Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ) HeidelbergGermany

[7 ]Genome Biology Unit, European Molecular Biology Laboratory HeidelbergGermany

[8 ]Department of Pathology and Medical Biology, GRIAC Research Institute, University of Groningen, University Medical Center Groningen GroningenNetherlands

[9 ]Program in Biology, Public Health Research Center, New York University Abu Dhabi Abu DhabiUnited Arab Emirates

[10 ]Institute for Human Genetics, Bakar Computational Health Sciences Institute, Bakar ImmunoX Initiative, Department of Medicine, Department of Bioengineering and Therapeutic Sciences, Department of Epidemiology and Biostatistics, Chan Zuckerberg Biohub, University of California San Francisco San FranciscoUnited States

[11 ]Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute, UNSW Cellular Genomics Futures Institute, University of New South Wales SydneyAustralia

[12 ]Institute of Computational Biology, Helmholtz Zentrum München NeuherbergGermany

[13 ]Department of Mathematics, Technical University of Munich Garching bei MünchenGermany

[14 ]Leiden Computational Biology Center, Leiden University Medical Center LeidenNetherlands

[15 ]Delft Bioinformatics Lab, Delft University of Technology DelftNetherlands

[16 ]Department of Informatics, Technical University of Munich Garching bei MünchenGermany

eLife United Kingdom

Stanford University United States

INRA France

Author notes

[†]

These authors contributed equally to this work.

Author information

MGP van der Wijst https://orcid.org/0000-0003-1520-3970

HE Groot https://orcid.org/0000-0002-8265-3085

G Trynka https://orcid.org/0000-0002-6955-9529

MJ Bonder https://orcid.org/0000-0002-8431-3180

MC Nawijn https://orcid.org/0000-0003-3372-6521

Y Idaghdour https://orcid.org/0000-0002-2768-9376

P van der Harst https://orcid.org/0000-0002-2713-686X

FJ Theis https://orcid.org/0000-0002-2419-1943

A Mahfouz https://orcid.org/0000-0001-8601-2149

M Heinig https://orcid.org/0000-0002-5612-1720

L Franke https://orcid.org/0000-0002-5159-8802

Article

Publisher ID: 52155

DOI: 10.7554/eLife.52155

PMC ID: 7077978

PubMed ID: 32149610

SO-VID: a527d9be-46cc-443c-939b-46e09bbb4219

License:

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

History

Date received : 24 September 2019

Date accepted : 03 March 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;

Award ID: NWO-Veni 192.029

Award Recipient : MGP van der Wijst

Funded by: FundRef http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;

Award ID: ZonMW-VIDI 917.14.374

Award Recipient : L Franke

Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;

Award ID: ERC Starting grant Immrisk 637640

Award Recipient : L Franke

Funded by: Oncode Institute;

Award Recipient : L Franke

Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;

Award ID: Investigator grant 1175781

Award Recipient : J Powell

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

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Author impact statement The single-cell eQTLGen consortium aims to pinpoint the cellular contexts in which disease-causing genetic variants affect gene expression and its regulation.

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ScienceOpen disciplines: Life sciences

Keywords: single-cell,gene regulatory network,eqtl,human,pbmc,science forum

Data availability:

ScienceOpen disciplines: Life sciences

Keywords: single-cell, gene regulatory network, eqtl, human, pbmc, science forum

The single-cell eQTLGen consortium

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Abstract

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Most cited references 66

Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis

Accounting for technical noise in single-cell RNA-seq experiments.

A cellular census of human lungs identifies novel cell states in health and in asthma

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