THU0013 INTEGRATED ANALYSIS OF SYNOVIAL SINGLE CELL RNA SEQUENCING DATA DEEPENS THE CURRENT KNOWLEDGE OF SYNOVIAL PATHOLOGY IN ARTHRITIS. (2nd June 2020)

• Record Type: Journal Article
• Authors: Edalat, S. G.; Micheroli, R.; Kuret, T.; Buerki, K.; Pauli, C.; Sodin-Šemrl, S.; Ciurea, A.; Distler, O.; Ospelt, C.; Rot, G.; Frank-Bertoncelj, M.
• Is Part Of: Annals of the rheumatic diseases. Volume 79(2020)Supplement 1
• Page Start: 219
• Page End: 219
• Publication Date: 2020-06-02
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2020-eular.5716
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: The heterogeneity of synovial tissues from patients with arthritis could contribute to the interpatient variability in disease course, prognosis and treatment response. Single-cell RNA sequencing (scRNA-seq) permits in-depth analysis of tissue heterogeneity, which could facilitate drug discovery and patient stratification for precision medicine. Objectives: To construct a comprehensive landscape of synovial cell types and molecular pathways in arthritis by integrating our and published scRNA-seq data, generated across different scRNA-seq technologies [Smart-seq2, Drop-seq], cell preparation protocols [dissociated unsorted, sorted cells] and types of arthritis [undifferentiated (UA), rheumatoid arthritis, osteoarthritis]. Methods: Synovial tissues were obtained by ultrasound-guided biopsy from patients with UA [not fulfilling the classification criteria for a specific arthritis, n=3]. Biopsies were disintegrated [enzymatic and mechanical disruption] and cell viability assessed with trypan blue. ScRNA-seq libraries [2 per patient] were prepared with 10X Genomics Drop-Seq and sequenced on NovaSeq6000. Bioinformatics analysis of our and published [n=35] datasets 1-3 was performed using Seurat protocol 4 with correction for batch effects and filtering low-quality cells. Functional enrichment analysis of marker genes in clusters was done with STRING Protein-Protein networks. Synovitis was assessed with ultrasound and histology. Results: Our tissue disintegration protocol resulted in good cell yield and viability (92%, 72%, 100%). The synovial cellular heterogeneity detected by scRNA-seq reflected the histological findings [Krenn score, pathotype]. These were supported with the ultrasound and clinically assessed disease activity. The integrated analysis of 41 datasets from 38 donors yielded 41845 scRNA-seq cell profiles, 50% contributed by our dataset. An independent analysis of our data and their integration with published data showed that different scRNA-seq methods and protocols can identify all the major synovial cell types and their activation states (Figure 1 ) with large heterogeneity between donors. We identified a previously undescribed synovial cell population, which was located near the fibroblast cluster, was negative for canonical cell markers, but highly enriched in cell division genes (80% of marker genes). These cells comprised a mixed population of CD34-, podoplanin (PDPN) high or PDPN low cells that were mostly negative for the sub-lining fibroblast marker THY. Furthermore, they appeared to be highly secretory (extracellular matrix components) and their gene expression profile was inclined towards cell migration, vascular development and insulin growth factor-dependent processes. Conclusion: By integrating synovial scRNA-seq data from 41845 cells, we identified a previously undescribed, highly proliferative and secretory synovial cell population in arthritis. We increased the number of known scRNA-seq synovial cell profiles in arthritis by two-fold and demonstrated the robustness of synovial scRNA-seq data outputs across different technologies and protocols. This broadens the current knowledge of synovial tissue heterogeneity and pathology in arthritis. References: [1]Stephenson W. et al. Nat Commun 2017. [2]Mizoguchi F. et al. Nat Commun 2017. [3]Zhang F. et al. Nat Immunol 2018. [4]Stuart T et al. Cell 2019 Acknowledgments: This work is supported by Vontobel Foundation and medAlumni University of Zurich Disclosure of Interests: Sam G. Edalat: None declared, Raphael Micheroli: None declared, Tadeja Kuret: None declared, Kristina Buerki: None declared, Chantal Pauli: None declared, Snežna Sodin-Šemrl: None declared, Adrian Ciurea Consultant of: Consulting and/or speaking fees from AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Merck Sharp & Dohme, Novartis and Pfizer., Oliver Distler Grant/research support from: Grants/Research support from Actelion, Bayer, Boehringer Ingelheim, Competitive Drug Development International Ltd. and Mitsubishi Tanabe; he also holds the issued Patent on mir-29 for the treatment of systemic sclerosis (US8247389, EP2331143)., Consultant of: Consultancy fees from Actelion, Acceleron Pharma, AnaMar, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, Catenion, ChemomAb, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi and UCB, Speakers bureau: Speaker fees from Actelion, Bayer, Boehringer Ingelheim, Medscape, Pfizer and Roche, Caroline Ospelt Consultant of: Consultancy fees from Gilead Sciences., Gregor Rot: None declared, Mojca Frank-Bertoncelj: None declared