OP0342 Altered frequency and function of mait cells in systemic sclerosis revealed by high dimensional mass cytometry and transcriptome analysis. (12th June 2018)

• Record Type: Journal Article
• Authors: Paleja, B.; Low, A.; Kumar, P.; Saidin, S.; Lajam, A.; D/O Thanna Bathi, L.; Lai, L.; Chua, C.; Albani, S.
• Is Part Of: Annals of the rheumatic diseases. Volume 77(2018)Supplement 2
• Page Start: 216
• Page End: 217
• Publication Date: 2018-06-12
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2018-eular.6082
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Systemic sclerosis (SSc) is an autoimmune disease characterised by excessive fibrosis of skin and internal organs, and vascular dysfunction 1 . Association of T and B cell subsets have been reported in SSc, however there is lack of systematic studies of functional relations between immune cell subsets in this disease 2, 3, 4 . This lack of mechanistic knowledge hampers targeted intervention. Objectives: In the current study we ought to determine differential immune cell composition and heterogeneity in peripheral blood of SSc patients and its impact on disease severity and progression. Methods: Mononuclear cells from blood of SSc patients with interstitial lung disease (ILD, n=10) or No ILD (n=10) and healthy controls (n=10) were analysed by mass cytometry using a 36 marker (cell-surface and intracellular) panel to aid in identification of major PBMC lineages including T cells, B cells, monocytes and NK cells and their subsets. Transcriptome analysis (m-RNA sequencing) was performed on sorted T and B cell subsets. Unsupervised clustering of mass cytometry data was performed using in-house developed analysis software MARVIS. This software combines dimension reduction and clustering steps to identify all possible cellular subsets. Further, custom R scripts helped in identifying nodes that were differentially expressed between the study groups and also phenotype of these nodes. Results: Unsupervised clustering performed revealed significant differences in the frequencies of T cell and B cell subsets. Most strikingly we identify a 3 fold decrease in frequencies of Va7.2+CD161+mucosal associated invariant T cells (MAIT) in SSc patients and 2 fold increase in total B cells, particularly CD19 +CD27 naive cells. A subset of memory CD8 +T cell, expressing CXCR3 was found to be increased in SSc patients as compared to healthy controls. Transcriptome analysis of sorted B cell and T cell subsets showed decrease in genes related to survival and increased expression of apoptotic genes in CD4, CD8 T and MAIT cells from SSc patients. Genes related to exhaustion and leukocyte migration were highly expressed in T cells from patients. Conclusions: This study provides an in depth analysis of systemic immune composition in SSc with the potential to delineate mechanisms of pathogenesis and identify diagnostic and/or therapeutic targets. This is the first demonstration of dysfunction of MAIT cells in SSc and further characterisation of their function in this context is required. References: [1] Denton CP, Khanna D. 'Systemic sclerosis.'Lancet2017;390(10103):1685–1699. [2] Fuschiotti P. Current perspectives on the role of CD8+ T cells in systemic sclerosis. Immunol Lett2017. [3] Liu M, et al. New insights into CD4(+) T cell abnormalities in systemic sclerosis.'Cytokine Growth Factor Rev2016;28:31–36. [4] Sanges S, et al. 'Role of B cells in the pathogenesis of systemic sclerosis.'Rev Med Interne2017;38(2):113–124. Disclosure of Interest: None declared