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Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry

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Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium, Zhang, Fan, Wei, Kevin, Slowikowski, Kamil, Fonseka, Chamith Y., Rao, Deepak A., Kelly, Stephen, Goodman, Susan M., Tabechian, Darren, Hughes, Laura B., Salomon-Escoto, Karen, Watts, Gerald F. M., Jonsson, A. Helena, Rangel-Moreno, Javier, Meednu, Nida, Rozo, Cristina, Apruzzese, William, Eisenhaure, Thomas M., Lieb, David J., Boyle, David L., Mandelin, Arthur M., Boyce, Brendan F., DiCarlo, Edward, Gravallese, Ellen M., Gregersen, Peter K., Moreland, Larry, Firestein, Gary S., Hacohen, Nir, Nusbaum, Chad, Lederer, James A., Perlman, Harris, Pitzalis, Costantino, Filer, Andrew, Holers, V. Michael, Bykerk, Vivian P., Donlin, Laura T., Anolik, Jennifer H., Brenner, Michael B., Raychaudhuri, Soumya·2019·Nature Immunology

Published version of a preprint

Abstract

To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA sequencing (RNA-seq) and flow cytometry to T cells, B cells, monocytes, and fibroblasts from 51 samples of synovial tissue from patients with RA or osteoarthritis (OA). Utilizing an integrated strategy based on canonical correlation analysis of 5,265 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics revealed cell states expanded in RA synovia: THY1(CD90)+HLA-DRAhi sublining fibroblasts, IL1B+ pro-inflammatory monocytes, ITGAX+TBX21+ autoimmune-associated B cells and PDCD1+ peripheral helper T (TPH) cells and follicular helper T (TFH) cells. We defined distinct subsets of CD8+ T cells characterized by GZMK+, GZMB+, and GNLY+ phenotypes. We mapped inflammatory mediators to their source cell populations; for example, we attributed IL6 expression to THY1+HLA-DRAhi fibroblasts and IL1B production to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis. Defining cell types and their activation status in rheumatoid arthritis (RA) is critical to understanding this disease. Raychaudhuri and colleagues leverage several single-cell -omics approaches to define the cellular processes and pathways in the human RA joint.

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Research works printed but not linked: 49

  1. Gibofsky, A. Epidemiology, pathophysiology, and diagnosis of rheumatoid arthritis: A Synopsis. Am. J. Manag. Care 20, S128–S135 (2014).
  2. Orr, C. et al. Synovial tissue research: a state-of-the-art review. Nat. Rev. Rheumatol. 13, 463–475 (2017). · DOI · Paper page
  3. Wolfe, F. et al. The mortality of rheumatoid arthritis. Arthritis Rheum. 37, 481–494 (1994). · DOI · Paper page
  4. Namekawa, T., Wagner, U. G., Goronzy, J. J. & Weyand, C. M. Functional subsets of CD4 T cells in rheumatoid synovitis. Arthritis Rheum. 41, 2108–2116 (1998). · DOI
  5. Gizinski, A. M. & Fox, D. A. T cell subsets and their role in the pathogenesis of rheumatic disease. Curr. Opin. Rheumatol. 26, 204–210 (2014). · DOI
  6. Reparon-Schuijt, C. C. et al. Secretion of anti-citrulline-containing peptide antibody by B lymphocytes in rheumatoid arthritis. Arthritis Rheum. 44, 41–47 (2001). · doi:10.1002/1529-0131(200101)44:1<41::aid-anr6>3.0.co;2-0
  7. Mulherin, D., Fitzgerald, O. & Bresnihan, B. Synovial tissue macrophage populations and articular damage in rheumatoid arthritis. Arthritis Rheum. 39, 115–124 (1996). · DOI · Paper page
  8. Kinne, R. W., Bräuer, R., Stuhlmüller, B., Palombo-Kinne, E. & Burmester, G. R. Macrophages in rheumatoid arthritis. Arthritis Res. 2, 189–202 (2000). · DOI
  9. Müller-Ladner, U. et al. Synovial fibroblasts of patients with rheumatoid arthritis attach to and invade normal human cartilage when engrafted into SCID mice. Am. J. Pathol. 149, 1607–1615 (1996).
  10. Pap, T., Müller-Ladner, U., Gay, R. E. & Gay, S. Fibroblast biology. Role of synovial fibroblasts in the pathogenesis of rheumatoid arthritis. Arthritis Res. 2, 361–367 (2000). · DOI
  11. Dennis, G. et al. Synovial phenotypes in rheumatoid arthritis correlate with response to biologic therapeutics. Arthritis Res. Ther. 16, R90 (2014). · DOI · Paper page
  12. Orange, D. E. et al. Identification of three rheumatoid arthritis disease subtypes by machine learning integration of synovial histologic features and RNA sequencing data. Arthritis Rheumatol. 70, 690–701 (2018). · DOI · Paper page
  13. Lindberg, J. et al. Variability in synovial inflammation in rheumatoid arthritis investigated by microarray technology. Arthritis Res. Ther. 8, R47 (2006). · DOI
  14. Stephenson, W. et al. Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation. Nat. Commun. 9, 791 (2018). · DOI · Paper page
  15. Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018). · DOI · Paper page
  16. Schelker, M. et al. Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nat. Commun. 8, 2032 (2017). · DOI · Paper page
  17. Fonseka, C. Y. et al. Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis. Sci. Transl. Med. 10, eaaq0305 (2018). · DOI · Paper page
  18. Mizoguchi, F. et al. Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis. Nat. Commun. 9, 789 (2018). · DOI · Paper page
  19. Donlin, L. T. et al. Methods for high-dimensional analysis of cells dissociated from cryopreserved synovial tissue. Arthritis Res. Ther. 20, 139 (2018). · DOI
  20. Becher, B. et al. High-dimensional analysis of the murine myeloid cell system. Nat. Immunol. 15, 1181–1189 (2014). · DOI · Paper page
  21. De Maesschalck, R., Jouan-Rimbaud, D. & Massart, D. L. The Mahalanobis distance. Chemometrics Intellig. Lab. Syst. 50, 1–18 (2000). · DOI · Paper page
  22. Krenn, V. et al. Grading of chronic synovitis—a histopathological grading system for molecular and diagnostic pathology. Pathol. Res. Pract. 198, 317–325 (2002). · DOI · Paper page
  23. Maaten, Lvander & Hinton, G. Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
  24. Rubtsov, A. V. et al. CD11c-expressing B cells are located at the T cell/B cell border in spleen and are potent APCs. J. Immunol. 195, 71–79 (2015). · DOI · Paper page
  25. Pillai, S. Now you know your ABCs. Blood 118, 1187–1188 (2011). · DOI
  26. Ellebedy, A. H. et al. Defining antigen-specific plasmablast and memory B cell subsets in human blood after viral infection or vaccination. Nat. Immunol. 17, 1226–1234 (2016). · DOI · Paper page
  27. Wang, S. et al. IL-21 drives expansion and plasma cell differentiation of autoreactive CD11chi T-bet+ B cells in SLE. Nat. Commun. 9, 1758 (2018). · DOI · Paper page
  28. Pitzalis, C., Kelly, S. & Humby, F. New learnings on the pathophysiology of RA from synovial biopsies. Curr. Opin. Rheumatol. 25, 334–344 (2013). · DOI · Paper page
  29. Filer, A. The fibroblast as a therapeutic target in rheumatoid arthritis. Curr. Opin. Pharmacol. 13, 413–419 (2013). · DOI
  30. Westra, H.-J. et al. Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes. Nat. Genet. 50, 1366–1374 (2018). · DOI
  31. Al-Mayouf, S. M. et al. Loss-of-function variant in DNASE1L3 causes a familial form of systemic lupus erythematosus. Nat. Genet. 43, 1186–1188 (2011). · DOI · Paper page
  32. Snelling, S. J. B. et al. Dickkopf-3 is upregulated in osteoarthritis and has a chondroprotective role. Osteoarthritis Cartilage 24, 883–891 (2016). · DOI
  33. Lee, E. B. et al. Tofacitinib versus methotrexate in rheumatoid arthritis. N. Engl. J. Med. 370, 2377–2386 (2014). · DOI · Paper page
  34. Zizzo, G., Hilliard, B. A., Monestier, M. & Cohen, P. L. Efficient clearance of early apoptotic cells by human macrophages requires M2c polarization and MerTK induction. J. Immunol. 189, 3508–3520 (2012). · DOI · Paper page
  35. Frara, N. et al. Transgenic expression of osteoactivin/gpnmb enhances bone formation in vivo and osteoprogenitor differentiation ex vivo. J. Cell. Physiol. 231, 72–83 (2016). · DOI
  36. Jenks, S. A. et al. Distinct effector B cells induced by unregulated toll-like receptor 7 contribute to pathogenic responses in systemic lupus erythematosus. Immunity 49, 725–739.e6 (2018). · DOI · Paper page
  37. Smolen, J. S. How well can we compare different biologic agents for RA? Nat. Rev. Rheumatol. 6, 247–248 (2010). · doi:10.1038/nrrheum.2010.58
  38. McInnes, I. B. et al. The role of interleukin-15 in T-cell migration and activation in rheumatoid arthritis. Nat. Med. 2, 175 (1996). · DOI · Paper page
  39. McInnes, I. B. & Liew, F. Y. Cytokine networks—towards new therapies for rheumatoid arthritis. Nat. Clin. Pract. Rheumatol. 1, 31 (2005). · DOI
  40. Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19, 562–578 (2018). · DOI · Paper page
  41. Parkhomenko, E., Tritchler, D. & Beyene, J. Sparse canonical correlation analysis with application to genomic data integration. Stat. Appl. Genet. Mol. Biol. 8, 1 (2009).
  42. Witten, D. M., Tibshirani, R. & Hastie, T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10, 515–534 (2009). · DOI · Paper page
  43. Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016). · DOI · Paper page
  44. Bjornson, Z. B., Nolan, G. P. & Fantl, W. J. Single-cell mass cytometry for analysis of immune system functional states. Curr. Opin. Immunol. 25, 484–494 (2013). · DOI · Paper page
  45. Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483–494 (2013). · DOI · Paper page
  46. González, I., Déjean, S., Martin, P. & Baccini, A. CCA: an R package to extend canonical correlation analysis. J. Stat. Software 23, 1–14 (2008). · DOI · Paper page
  47. Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941 (2005). · DOI · Paper page
  48. Reynolds, A. P., Richards, G., De La Iglesia, B. & Rayward-Smith, V. J. Clustering rules: a comparison of partitioning and hierarchical clustering algorithms. J. Math. Model. Algorithms 5, 475–504 (2006). · DOI · Paper page
  49. Rousseeuw, P. J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). · DOI · Paper page

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Metadata

Authors
Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium, Zhang, F., Wei, K., Slowikowski, K., Fonseka, C. Y., Rao, D. A., Kelly, S., Goodman, S. M., Tabechian, D., Hughes, L. B., Salomon-Escoto, K., Watts, G. F. M., Jonsson, A. H., Rangel-Moreno, J., Meednu, N., Rozo, C., Apruzzese, W., Eisenhaure, T. M., Lieb, D. J., Boyle, D. L., Mandelin, A. M., Boyce, B. F., DiCarlo, E., Gravallese, E. M., Gregersen, P. K., Moreland, L., Firestein, G. S., Hacohen, N., Nusbaum, C., Lederer, J. A., Perlman, H., Pitzalis, C., Filer, A., Holers, V. M., Bykerk, V. P., Donlin, L. T., Anolik, J. H., Brenner, M. B., & Raychaudhuri, S.
Venue
Nature Immunology
Published
2019-05-06
Volume
20
Issue
7
Pages
928-942
ISSN
  • 1529-2908 (ISSN-L)
  • 1529-2916
OpenAlex
W2949243209
Cited by
1,367
References
18
FWCI
93.86
Type
journal article
Language
en
Open access
bronze
Licence
www.springer.com/tdm (tdm)

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Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP RA/SLE) Consortium, Zhang, F., Wei, K., Slowikowski, K., Fonseka, C. Y., Rao, D. A., Kelly, S., Goodman, S. M., Tabechian, D., Hughes, L. B., Salomon-Escoto, K., Watts, G. F. M., Jonsson, A. H., Rangel-Moreno, J., Meednu, N., Rozo, C., Apruzzese, W., Eisenhaure, T. M., Lieb, D. J., . . . Raychaudhuri, S. (2019). Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nature Immunology. https://doi.org/10.1038/s41590-019-0378-1

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