Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus

Perez, Richard; Gordon, M. Grace; Subramaniam, Meena; Kim, Min Cheol; Hartoularos, George C.; Targ, Sasha; Sun, Yang; Ogorodnikov, Anton; Bueno, Raymund; Lu, Andrew; Thompson, Michael; Rappoport, Nadav; Dahl, Andy; Lanata, Cristina; Matloubian, Mehrdad; Maliskova, Lenka; Kwek, Serena S.; Li, Tony; Slyper, Michal; Waldman, Julia; Dionne, Danielle; Rozenblatt-Rosen, Orit; Fong, Lawrence; Dall’Era, Maria; Balliu, Brunilda; Regev, Aviv; Yazdany, Jinoos; Criswell, Lindsey A.; Zaitlen, Noah; Ye, Chun Jimmie

doi:10.1126/science.abf1970

Cited by 190 publications

(229 citation statements)

References 73 publications

Supporting

Mentioning

219

Contrasting

Order By: Relevance

“…We showed that the number of significantly detected co-eQTLs can be greatly increased by either increasing the number of individuals or by increasing the number of cells per individual (Figure 4c, d). believe that future larger single-cell datasets such as two very recent studies (49,50) and the sc-eQTLGen consortium (51) will improve statistical power to identify more robust co-eQTLs. Furthermore, we showed that a sophisticated filtering strategy of tested SNP-genegene triplets is essential to maximize the number of reliable co-eQTLs.…”

Section: Discussionmentioning

confidence: 99%

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Schmid

Korshevniuk

et al. 2022

Preprint

View full text Add to dashboard Cite

BackgroundExpression quantitative trait loci (eQTL) studies have shown how genetic variants affect downstream gene expression. To identify the upstream regulatory processes, single-cell data can be used. Single-cell data also offers the unique opportunity to reconstruct personalized co-expression networks—by exploiting the large number of cells per individual, we can identify SNPs that alter co-expression patterns (co-expression QTLs, co-eQTLs) using a limited number of individuals.ResultsTo tackle the large multiple testing burden associated with a genome-wide analysis (i.e. the need to assess all combinations of SNPs and gene pairs), we conducted a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets from three studies (reflecting 173 unique participants and 1 million cells) using a novel filtering strategy followed by a permutation-based approach. Before analysis, we evaluated the co-expression patterns to be used for co-eQTL identification using different external resources. The subsequent analysis identified a robust set of cell-type-specific co-eQTLs for 72 independent SNPs that affect 946 gene pairs, which we then replicated in a large bulk cohort. These co-eQTLs provide novel insights into how disease-associated variants alter regulatory networks. For instance, one co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell-activation and autoimmune disease. Among these genes, we identified enrichment for targets of five T-cell-activation-related transcriptional factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases.ConclusionOur co-eQTL results highlight the importance of studying gene regulation at the context-specific level to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy—combined with our technical guidelines—will soon identify many more co-eQTLs, further helping to elucidate unknown disease mechanisms.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Schmid

Korshevniuk

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Clinical studies 11 and large-scale single cell RNA sequencing analysis 12 confirmed the inverse association between type I IFN activity and the abundance of circulating lymphocytes or naïve CD4 + T cells in SLE patients. However, the type I IFN signaling pathway in CD4 + T cells remains incompletely understood and it is unclear how type I IFN overactivation may be linked to T cell lymphopenia in lupus.…”

Section: Introductionmentioning

confidence: 86%

“…The prevailing theory on the pathogenesis of the lymphopenia in lupus has been centered on antilymphocyte antibodies 51 . But a large-scale multiplex single cell RNA-seq analysis demonstrated a clear inverse correlation between type I IFN activity and circulating naïve CD4 + T cells in SLE patients 12 . Our study corroborates with such observation indicating that elevated type I IFN production may be another contributing factor for the lymphopenia phenotype in lupus partly by potentiating mTORC2 activation.…”

Section: It Has Long Been Established That Exuberant Type I Ifn Activ...mentioning

confidence: 97%

mTORC2 contributes to murine lupus

Zhou

et al. 2021

Preprint

View full text Add to dashboard Cite

The development of systemic lupus erythematosus (SLE) is associated with overactivation of the type I interferon (IFN) pathway, lymphopenia, and increased follicular helper T (Tfh) cell differentiation. However, the cellular and molecular mechanisms of action of type I IFN in SLE remains incompletely understood. Here we show that type I IFN activates the mechanistic target of rapamycin complex 2 (mTORC2) in T cells to promote T cell lymphopenia. mTORC2 also promotes Tfh differentiation and disrupts Treg homeostasis. Inactivation of mTORC2 greatly ameliorated the immunopathology in a lupus-prone mouse model, associated with reduced Tfh differentiation, normalization of Treg homeostasis and reduced T cell glucose metabolism. These data indicate that mTORC2 acts downstream of type I IFN and costimulatory receptor ICOS, to promote T cell lymphopenia and Tfh differentiation in murine lupus development, suggesting that inhibition of mTORC2 could limit lupus disease progression.

show abstract

“…Recently, Perez et al. also reported larger-scale single-cell RNA-seq profiles (>1.2 million single cells) in the PBMC of 162 SLE patients and healthy controls both from European and Asian ancestries ( 15 ). They revealed a reduction in naïve CD4 + T cells and an increase of GZMH + CD8 + T cells in SLE.…”

Section: Single-cell Rna-seq Of Blood Immune Cells In Slementioning

confidence: 99%

“…Also, using single-cell RNA-seq data of SLE patients, Perez et al. performed eQTL analysis in the eight most abundant cell types ( 15 ). Their analysis identified 3331 genes with at least one eQTL in a cell type.…”

Section: Dynamic Eqtl Analysis Of Autoimmune Diseasesmentioning

confidence: 99%

Lessons From Transcriptome Analysis of Autoimmune Diseases

2022

View full text Add to dashboard Cite

Various immune cell types, including monocytes, macrophages, and adaptive immune T and B cells, play major roles in inflammation in systemic autoimmune diseases. However, the precise contribution of these cells to autoimmunity remains elusive. Transcriptome analysis has added a new dimension to biology and medicine. It enables us to observe the dynamics of gene expression in different cell types in patients with diverse diseases as well as in healthy individuals, which cannot be achieved with genomic information alone. In this review, we summarize how transcriptome analysis has improved our understanding of the pathological roles of immune cells in autoimmune diseases with a focus on the ImmuNexUT database we reported. We will also discuss the common experimental and analytical design of transcriptome analyses. Recently, single-cell RNA-seq analysis has provided atlases of infiltrating immune cells, such as pro-inflammatory monocytes and macrophages, peripheral helper T cells, and age or autoimmune-associated B cells in various autoimmune disease lesions. With the integration of genomic data, expression quantitative trait locus (eQTL) analysis can help identify candidate causal genes and immune cells. Finally, we also mention how the information obtained from these analyses can be used practically to predict patient prognosis.

show abstract

Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus

Cited by 190 publications

References 73 publications

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

mTORC2 contributes to murine lupus

Lessons From Transcriptome Analysis of Autoimmune Diseases

Contact Info

Product

Resources

About