Single-cell expression quantitative trait loci (eQTL) analysis of SLE-risk loci in lupus patient monocytes

• Published in: Arthritis research & therapy Vol. 23; no. 1; pp. 1 - 290
• Main Authors: Ghodke-Puranik, Yogita, Jin, Zhongbo, Zimmerman, Kip D, Ainsworth, Hannah C, Fan, Wei, Jensen, Mark A, Dorschner, Jessica M, Vsetecka, Danielle M, Amin, Shreyasee, Makol, Ashima, Ernste, Floranne, Osborn, Thomas, Moder, Kevin, Chowdhary, Vaidehi, Langefeld, Carl D, Niewold, Timothy B
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 30.11.2021; BioMed Central; BMC
• Subjects: Arthritis; Gene expression; Gene loci; Genetic aspects; Health aspects; Immune system; Influence; Lupus; Medical research; Medicine, Experimental; Microscopy; Monocytes; Patients; Quantitative trait loci; Risk factors; Systemic lupus erythematosus; United States
• ISSN: 1478-6362; 1478-6354; 1478-6362
• DOI: 10.1186/s13075-021-02660-2

Abstract Background We performed expression quantitative trait locus (eQTL) analysis in single classical (CL) and non-classical (NCL) monocytes from patients with systemic lupus erythematosus (SLE) to quantify the impact of well-established genetic risk alleles on transcription at single-cell resolution. Methods Single-cell gene expression was quantified using qPCR in purified monocyte subpopulations (CD14 ++ CD16 − CL and CD14 dim CD16 + NCL) from SLE patients. Novel analysis methods were used to control for the within-person correlations observed, and eQTLs were compared between cell types and risk alleles. Results The SLE-risk alleles demonstrated significantly more eQTLs in NCLs as compared to CLs ( p  = 0.0004). There were 18 eQTLs exclusive to NCL cells, 5 eQTLs exclusive to CL cells, and only one shared eQTL, supporting large differences in the impact of the risk alleles between these monocyte subsets. The SPP1 and TNFAIP3 loci were associated with the greatest number of transcripts. Patterns of shared influence in which different SNPs impacted the same transcript also differed between monocyte subsets, with greater evidence for synergy in NCL cells. IRF1 expression demonstrated an on/off pattern, in which expression was zero in all of the monocytes studied from some individuals, and this pattern was associated with a number of SLE risk alleles. We observed corroborating evidence of this IRF1 expression pattern in public data sets. Conclusions We document multiple SLE-risk allele eQTLs in single monocytes which differ greatly between CL and NCL subsets. These data support the importance of the SPP1 and TNFAIP3 risk variants and the IRF1 transcript in SLE patient monocyte function.