Session Type: ACR Poster Session B
Session Time: 9:00AM-11:00AM
Background/Purpose: While most of the confirmed SLE-risk loci are in or near genes with immune system function, a major unanswered question is how these loci influence diverse immune cell subsets. In this study we performed a single cell eQTL analysis in human monocytes to determine impact of some well-established SLE-risk loci in single human monocytes.
Methods: CD14++CD16- classical monocytes (CL) and CD14dimCD16+ non classical (NCL) monocytes from SLE patients were purified by magnetic separation. The Fluidigm C1 System was used for single cell capture and target gene pre-amplification and equal numbers of classical and non-classical monocytes were studied. Real time PCR was used to quantify expression of 90 monocyte-related genes, and the same SLE patients were genotyped for 7 SLE-risk SNPs to enable eQTL analysis. Non-parametric analyses were used with the single cell data in CL and NCL populations separately.
We observed a large number of significant eQTL associations that surpassed the 5% FDR, supporting the idea that single cell gene expression data allows for robust eQTL discovery. The SLE-associated SNPs demonstrated more eQTLs in NCLs as compared to CLs (p=2.5×10-8). For a given SNP, the eQTL associated transcripts differed between cell types (p<0.001 for all 7 SNPs for discordance), suggesting that the same SNP resulted in different cellular events between the two monocyte subsets. When comparing eQTL lists between the different SLE-associated SNPs, there was a greater degree of sharing observed in NCLs as compared to CLs. Loci which shared a significant proportion of eQTL associations with each other in NCLs included TNFAIP3, IRF5, IRF7, PTPN22, and SPP1. In CLs, TNFAIP3 shared a large number of eQTLs with SPP1 and ITGAM, although SPP1 and ITGAM showed more limited overlap with each other. Thus, SLE-associated risk loci exert coordinated effects on gene expression within individual human monocytes, and the risk loci interact in different ways in different cell types.
Conclusion: This study emphasizes the strength of single cell gene expression strategy for eQTL discovery. Our study revealed striking differences in the occurrence and interaction between of SLE risk associated eQTLs within different but closely related cell types. This suggests pleiotropic effects from each locus across various immune cell types, and a high degree of complexity when considering how these loci impact the immune system.
To cite this abstract in AMA style:Ghodke-Puranik Y, Jin Z, Fan W, Jensen MA, Dorschner JM, Vsetecka D, Amin S, Makol A, Ernste FC, Osborn T, Moder K, Chowdhary V, Niewold TB. Single Cell Expression Quantitative Trait Loci (eQTL) Analysis of Established SLE-Risk Loci in Lupus Patient Monocytes [abstract]. Arthritis Rheumatol. 2016; 68 (suppl 10). https://acrabstracts.org/abstract/single-cell-expression-quantitative-trait-loci-eqtl-analysis-of-established-sle-risk-loci-in-lupus-patient-monocytes/. Accessed May 20, 2019.
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ACR Meeting Abstracts - https://acrabstracts.org/abstract/single-cell-expression-quantitative-trait-loci-eqtl-analysis-of-established-sle-risk-loci-in-lupus-patient-monocytes/