Center of Experimental Rheumatology, University Hospital of Zurich Zürich, Switzerland
Alexandra Khmelevskaya1, Miranda Houtman2, Kristina Buerki3, Chantal Pauli4, Sam Edalat3, Mojca Frank-Bertoncelj3, Oliver Distler5, Adrian Ciurea6, Caroline Ospelt7 and Raphael Micheroli8, 1Center of Experimental Rheumatology, University Hospital of Zurich, Zürich, Switzerland, 2University of Zurich, Schlieren, Switzerland, 3Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, Zürich, Switzerland, 4Department of Pathology, University Hospital Zurich, Zürich, Switzerland, 5Department of Rheumatology, University Hospital Zurich, University of Zurich, Zürich, Switzerland, 6University Hospital Zurich, Zürich, Switzerland, 7Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Zürich, Switzerland, 8University Hospital Zurich, Department of Rheumatology, Zürich, Switzerland
Background/Purpose: Rheumatoid arthritis (RA), psoriatic arthritis (PsA), peripheral spondyloarthritis (pSpA), and undifferentiated arthritis (UA) are chronic immune-mediated conditions characterized by joint inflammation. The specific role and differences of synovial T cells in these diseases remain unknown.
Methods: Ultrasound guided synovial biopsies were performed in 10 RA, 4 PsA, 4 pSpA, and 3 UA patients fulfilling the respective classification criteria. Three osteoarthritis (OA) samples were obtained from joint replacement surgery. Synovial pathotype (pauci-immune, diffuse-myeloid and lympho-myeloid), Krenn score, and presence of neutrophils were assessed by histology. We dissociated fresh synovial tissues and targeted the encapsulation of up to 6000 cells. ScRNA-seq libraries were generated according to 10X Genomics workflow (v3.1) and subsequently sequenced on NovaSeq 6000. Cell Ranger and Seurat R packages were used for data analysis. P-values were calculated using Welch t-test with Bonferroni correction. T cells were identified by high CD3D gene expression. Cells with mitochondrial content greater than 10% were excluded. The repertoire of T cell receptors (TCR) was analyzed through V-gene usage.
Results: Unsupervised graph-based clustering identified one CD8 positive cluster and four CD4 positive clusters with respective marker gene in box brackets: effector memory cells re-expressing CD45RA (EMRA) [GZMK],T follicular helper cells (Tfh) [CXCL13], regulatory T cells (Treg) [FOXP3], and resting naive/central memory T cells (naive/CM) [CCR7]. The subset proportions were similar between diseases, with EMRA and Tfh populations being the most frequent, representing together more than 50% of T cells. However, the proportion was strikingly different in OA, with EMRA occupying more than 75% of all T cells. Total numbers of T cells in OA were low, reflecting the degenerative, non-immune mediated nature of the disease.
T cell subset proportions were similar between histological pathotypes across all diseases suggesting a comparable relative increase in all subpopulations in the lympho-myeloid pathotype.
Further clustering of CD4 EMRA cells revealed two subpopulations differing in expression of genes associated with activation (GZMK, GZMA, DUSP2, DUSP4, CCL5). HLA-B*27-positive patients had significantly reduced (mean 31% vs 60%, p-value=0.0001) and patients with histologically present neutrophils significantly higher proportions of activated CD4 EMRA (mean 65% vs 42%, p-value=0.0038). In addition, activated EMRA subpopulation correlated positive with Krenn score (R=0.4, p-value=0.003).
TCR repertoire was polyclonal with high Simpson diversity in all conditions. No bias in V-gene usage between the conditions was observed.
Conclusion: This analysis of T cells in different chronic inflammatory diseases shows an association of activated EMRA T cells with histological presence of neutrophils, HLA-B*27 negativity and Krenn Score. Deeper analyses are required to find disease specific T cell characteristics and to understand whether this T cell activation influences clinical outcomes such as treatment efficacy and overall prognosis.
Disclosures: A. Khmelevskaya, None; M. Houtman, None; K. Buerki, None; C. Pauli, None; S. Edalat, None; M. Frank-Bertoncelj, None; O. Distler, AbbVie/Abbott, Amgen, GlaxoSmithKlein(GSK), Novartis, Roche, UCB, Kymera, Mitsubishi Tanabe, Boehringer Ingelheim, 4P-Pharma, Acceleron, Alcimed, Altavant Sciences, AnaMar, Arxx, AstraZeneca, Blade Therapeutics, Bayer, Corbus Pharmaceuticals, CSL Behring, Galapagos, Glenmark, Horizon, Inventiva, Lupin, Miltenyi Biotec, Merck/MSD, Prometheus Biosciences, Redx Pharma, Roivant, Sanofi, Topadur, Pfizer, Janssen, Medscape, Patent issued “mir-29 for the treatment of systemic sclerosis” (US8247389, EP2331143), FOREUM Foundation, ERS/EULAR Guidelines, EUSTAR, SCQM (Swiss Clinical Quality Management in Rheumatic Diseases), Swiss Academy of Medical Sciences (SAMW), Hartmann Müller Foundation; A. Ciurea, AbbVie, Novartis, Merck/MSD; C. Ospelt, None; R. Micheroli, None.
