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CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis

Nature Immunology volume 25, pages 682–692 (2024)Cite this article

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Abstract

Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200–CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.

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Fig. 1: Study design and patient characteristics.
Fig. 2: FAP-tracer uptake in patients with arthritis.
Fig. 3: Cd200+ fibroblasts arise during resolution of inflammation.
Fig. 4: All fibroblasts express FAP, but receptor internalization controls FAPI signaling.
Fig. 5: Cd200+ fibroblasts sustain type 2 fate and survival of pro-resolving ILCs.
Fig. 6: CD200+/DKK3+ fibroblasts in human synovial biopsy samples from patients with RA or PsA.

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Data availability

Single-cell sequencing data supporting the results of this study have been deposited in the GEO under accession numbers GSE228982, GSE228629 and GSE230145 or are available on request from the authors. Publicly available datasets reanalyzed in the study can be accessed under accession numbers: GSE145286, GSE129087 (both mouse joint scRNA-seq), BioProject PRJEB40089, GSE200815, Synapse syn52297840 (all three human synovial tissue scRNA-seq), BioProject PRJNA580481 and Immport SDY2213 (both human spatial transcriptomic data of synovial tissue). Source data are provided with this paper.

Code availability

All the methods and algorithms used in this paper are from previously published studies and are cited in Methods. Selection criteria, thresholds and other essential parameters are stated in Methods. No new method for data analysis was developed. Additional scripts to reproduce the analyses are available from the authors upon request.

References

  1. Davidson, S. et al. Fibroblasts as immune regulators in infection, inflammation and cancer. Nat. Rev. Immunol. 21, 704–717 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Croft, A. P. et al. Distinct fibroblast subsets drive inflammation and damage in arthritis. Nature 570, 246–251 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wei, K. et al. Notch signalling drives synovial fibroblast identity and arthritis pathology. Nature 582, 259–264 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Armaka, M. et al. Single-cell multimodal analysis identifies common regulatory programs in synovial fibroblasts of rheumatoid arthritis patients and modeled TNF-driven arthritis. Genome Med. 14, 78 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yan, M. et al. ETS1 governs pathological tissue-remodeling programs in disease-associated fibroblasts. Nat. Immunol. 23, 1330–1341 (2022).

    Article  CAS  PubMed  Google Scholar 

  6. Floudas, A. et al. Distinct stromal and immune cell interactions shape the pathogenesis of rheumatoid and psoriatic arthritis. Ann. Rheum. Dis. annrheumdis-2021-221761 (2022).

  7. Mizoguchi, F. et al. Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis. Nat. Commun. 9, 789 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ospelt, C. & Gay, S. The role of resident synovial cells in destructive arthritis. Best. Pract. Res. Clin. Rheumatol. 22, 239–252 (2008).

    Article  CAS  PubMed  Google Scholar 

  9. Komatsu, N. & Takayanagi, H. Mechanisms of joint destruction in rheumatoid arthritis - immune cell-fibroblast-bone interactions. Nat. Rev. Rheumatol. 18, 415–429 (2022).

    Article  CAS  PubMed  Google Scholar 

  10. Friščić, J. et al. The complement system drives local inflammatory tissue priming by metabolic reprogramming of synovial fibroblasts. Immunity 54, 1002–1021 (2021).

    Article  PubMed  Google Scholar 

  11. Schett, G., McInnes, I. B. & Neurath, M. F. Reframing immune-mediated inflammatory diseases through signature cytokine hubs. N. Engl. J. Med. 385, 628–639 (2021).

    Article  CAS  PubMed  Google Scholar 

  12. McInnes, I. B. & Schett, G. Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet 389, 2328–2337 (2017).

    Article  CAS  PubMed  Google Scholar 

  13. Schmidkonz, C. et al. Fibroblast activation protein inhibitor imaging in nonmalignant diseases: a new perspective for molecular imaging. J. Nucl. Med. 63, 1786–1792 (2022).

    Article  CAS  PubMed  Google Scholar 

  14. Kuwert, T., Schmidkonz, C., Prante, O., Schett, G. & Ramming, A. FAPI PET opens a new window to understanding immune-mediated inflammatory diseases. J. Nucl. Med. 63, 1136–1137 (2022).

    Article  PubMed  Google Scholar 

  15. Lindner, T. et al. Development of quinoline-based theranostic ligands for the targeting of fibroblast activation protein. J. Nucl. Med. 59, 1415–1422 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Loktev, A. et al. Development of fibroblast activation protein-targeted radiotracers with improved tumor retention. J. Nucl. Med. 60, 1421–1429 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jansen, K. et al. Selective inhibitors of fibroblast activation protein (FAP) with a (4-quinolinoyl)-glycyl-2-cyanopyrrolidine sScaffold. ACS Med. Chem. Lett. 4, 491–496 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Jansen, K. et al. Extended structure-activity relationship and pharmacokinetic investigation of (4-quinolinoyl)glycyl-2-cyanopyrrolidine inhibitors of fibroblast activation protein (FAP). J. Med. Chem. 57, 3053–3074 (2014).

    Article  CAS  PubMed  Google Scholar 

  19. Dorst, D. N. et al. Targeting of fibroblast activation protein in rheumatoid arthritis patients: imaging and ex vivo photodynamic therapy. Rheumatology 61, 2999–3009 (2022).

    Article  CAS  PubMed  Google Scholar 

  20. Ge, L. et al. Preclinical evaluation and pilot clinical study of [18F]AlF-NOTA-FAPI-04 for PET imaging of rheumatoid arthritis. Eur. J. Nucl. Med. Mol. Imaging 49, 4025–4036 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Buechler, M. B. et al. Cross-tissue organization of the fibroblast lineage. Nature 593, 575–579 (2021).

    Article  CAS  PubMed  Google Scholar 

  23. Collins, F. L. et al. Taxonomy of fibroblasts and progenitors in the synovial joint at single-cell resolution. Ann. Rheum. Dis. 82, 428–437 (2022).

    Article  PubMed  Google Scholar 

  24. Burkhardt, D. B. et al. Quantifying the effect of experimental perturbations at single-cell resolution. Nat. Biotechnol. 39, 619–629 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Reshef, Y. A. et al. Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics. Nat. Biotechnol. 40, 355–363 (2022).

    Article  CAS  PubMed  Google Scholar 

  26. Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D. & Marioni, J. C. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat. Biotechnol. 40, 245–253 (2022).

    Article  CAS  PubMed  Google Scholar 

  27. Lange, M. et al. CellRank for directed single-cell fate mapping. Nat. Methods 19, 159–170 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Omata, Y. et al. Group 2 innate lymphoid cells attenuate inflammatory arthritis and protect from bone destruction in mice. Cell Rep. 24, 169–180 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Rauber, S. et al. Resolution of inflammation by interleukin-9-producing type 2 innate lymphoid cells. Nat. Med. 23, 938–944 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods 3, 100498 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Qiu, X. et al. Mapping transcriptomic vector fields of single cells. Cell 185, 690–711 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Penkava, F. et al. Single-cell sequencing reveals clonal expansions of pro-inflammatory synovial CD8 T cells expressing tissue-homing receptors in psoriatic arthritis. Nat. Commun. 11, 4767 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Carlberg, K. et al. Exploring inflammatory signatures in arthritic joint biopsies with spatial transcriptomics. Sci. Rep. 9, 18975 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Smith, M. H. et al. Drivers of heterogeneity in synovial fibroblasts in rheumatoid arthritis. Nat. Immunol. 24, 1200–1210 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39, 1375–1384 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lories, R. J., Luyten, F. P. & de Vlam, K. Progress in spondylarthritis. Mechanisms of new bone formation in spondyloarthritis. Arthritis Res Ther. 11, 221 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Andreev, D. et al. Regulatory eosinophils induce the resolution of experimental arthritis and appear in remission state of human rheumatoid arthritis. Ann. Rheum. Dis. 80, 451–468 (2021).

    Article  CAS  PubMed  Google Scholar 

  38. Chen, Z., Bozec, A., Ramming, A. & Schett, G. Anti-inflammatory and immune-regulatory cytokines in rheumatoid arthritis. Nat. Rev. Rheumatol. 15, 9–17 (2019).

    Article  CAS  PubMed  Google Scholar 

  39. Filer, A. The fibroblast as a therapeutic target in rheumatoid arthritis. Curr. Opin. Pharmacol. 13, 413–419 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Gorczynski, R. M., Chen, Z., Yu, K. & Hu, J. CD200 immunoadhesin suppresses collagen-induced arthritis in mice. Clin. Immunol. 101, 328–334 (2001).

    Article  CAS  PubMed  Google Scholar 

  41. Schmidkonz, C. et al. Disentangling inflammatory from fibrotic disease activity by fibroblast activation protein imaging. Ann. Rheum. Dis. 79, 1485–1491 (2020).

    Article  CAS  PubMed  Google Scholar 

  42. Aletaha, D. et al. 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 62, 2569–2581 (2010).

    Article  PubMed  Google Scholar 

  43. Taylor, W. et al. Classification criteria for psoriatic arthritis: development of new criteria from a large international study. Arthritis Rheum. 54, 2665–2673 (2006).

    Article  PubMed  Google Scholar 

  44. Rudwaleit, M. et al. The development of Assessment of SpondyloArthritis International Society classification criteria for axial spondyloarthritis (part II): validation and final selection. Ann. Rheum. Dis. 68, 777–783 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Toms, J. et al. Targeting fibroblast activation protein: radiosynthesis and preclinical evaluation of an 18F-labeled FAP inhibitor. J. Nucl. Med. 61, 1806–1813 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. Keffer, J. et al. Transgenic mice expressing human tumour necrosis factor: a predictive genetic model of arthritis. EMBO J. 10, 4025–4031 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Nussbaum, J. C. et al. Type 2 innate lymphoid cells control eosinophil homeostasis. Nature 502, 245–248 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kouskoff, V. et al. Organ-specific disease provoked by systemic autoimmunity. Cell 87, 811–822 (1996).

    Article  CAS  PubMed  Google Scholar 

  49. Liu, F., Song, Y. & Liu, D. Hydrodynamics-based transfection in animals by systemic administration of plasmid DNA. Gene Ther. 6, 1258–1266 (1999).

    Article  CAS  PubMed  Google Scholar 

  50. Zhang, G., Budker, V. & Wolff, J. A. High levels of foreign gene expression in hepatocytes after tail vein injections of naked plasmid DNA. Hum. Gene Ther. 10, 1735–1737 (1999).

    Article  CAS  PubMed  Google Scholar 

  51. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  52. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

    Article  PubMed  Google Scholar 

  55. Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol. 23, 27 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Gayoso, A. et al. A Python library for probabilistic analysis of single-cell omics data. Nat. Biotechnol. 40, 163–166 (2022).

    Article  CAS  PubMed  Google Scholar 

  60. Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A. & Theis, F. J. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2019).

    Article  PubMed  Google Scholar 

  61. Mikolajewicz, N. et al. Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline. Commun. Biol. 5, 1142 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Borcherding, N. et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Commun. Biol. 4, 122 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).

  66. Andreatta, M. & Carmona, S. J. UCell: robust and scalable single-cell gene signature scoring. Comput Struct. Biotechnol. J. 19, 3796–3798 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  69. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  70. Amezquita, R. A. et al. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17, 137–145 (2020).

    Article  CAS  PubMed  Google Scholar 

  71. Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Kolde, R. Pheatmap: pretty heatmaps. R package version 1.0.10. https://cran.r-project.org/web/packages/pheatmap/index.html (2019).

  73. Zanotelli, V. R. & Bodenmiller, B. ImcSegmentationPipeline: a pixel-classification-based multiplexed image segmentation pipeline. https://doi.org/10.5281/zenodo.3841961 (2022).

  74. Ashhurst, T. M. et al. Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre. Cytometry A 101, 237–253 (2022).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank M. Rose, C. Pfaff, J. Tu and V. Fedorchenko for excellent technical assistance. We thank U. Appelt and M. Mroz from the FAU ‘Core Unit für Zellsortierung und Immunomonitoring’ for cell sorting. We acknowledge the FAU NGS core facility for sequencing. We acknowledge S. Bauer, C. Renner (both former scientists of UZH Zurich, Switzerland) and U. Haberkorn (Department of Nuclear Medicine, University Hospital Heidelberg, Germany) for supplying the HT1080-FAP transfected cell line. We acknowledge iTheranostics, a Delaware corporation, for providing the FAPI-04 precursor. We acknowledge W. Baum (Department of Medicine 3, University Hospital Erlangen, Germany) for providing K/BxN serum. The work was supported by the German Research Foundation (DFG) to A.R. (RA 2506/4-1, RA 2506/4-2, RA 2506/6-1, RA 2506/7-1), to M.G.R. (Clinician Scientist Program NOTICE), to A.S. (SO 1735/2-1) and to G.S. (SCHE 1583/7-1); CRC1181 to G.S. (projects A01/Z03) and A.R. (project C06); CRC/TRR 369 DIONE to G.S. and A.R.; Gottfried Wilhelm Leibniz Prize 2023 to G.S. The work was supported by the European Research Council (853508 BARRIER BREAK) to A.R. and EC project Nanoscope 4D to G.S. The work was supported by the Federal Ministry of Education and Research (BMBF) to T.K., G.S. and A.R. (MASCARA). This work was supported by the Innovative Medicines Initiative projects RTCure and HIPPOCRATES to G.S. and A.R. The work was supported by Novartis Pharma to A.R. The work was supported by the Interdisciplinary Centre for Clinical Research (IZKF) Erlangen (D034 to A.R., P049 and J106 to M.G.R., J107 to S.R.). The present work was performed in partial fulfillment of the requirements for obtaining the degree rer. biol. hum. at the FAU Erlangen-Nürnberg.

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Author notes

  1. These authors contributed equally: Simon Rauber, Hashem Mohammadian.

Authors and Affiliations

  1. Department of Internal Medicine 3 – Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany

    Simon Rauber, Hashem Mohammadian, Alina Soare, Mario R. Angeli, Maria G. Raimondo, Cong Xu, Kai-Ting Yang, Le Lu, Hannah Labinsky, Mina S. A. Saad, Charles A. Gwellem, Jiyang Chang, Kaiyue Huang, Eleni Kampylafka, Johannes Knitza, Jörg H. W. Distler, Georg Schett & Andreas Ramming

  2. Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany

    Simon Rauber, Hashem Mohammadian, Alina Soare, Mario R. Angeli, Maria G. Raimondo, Cong Xu, Kai-Ting Yang, Le Lu, Hannah Labinsky, Mina S. A. Saad, Charles A. Gwellem, Jiyang Chang, Kaiyue Huang, Eleni Kampylafka, Johannes Knitza, Jörg H. W. Distler, Georg Schett & Andreas Ramming

  3. Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany

    Christian Schmidkonz, Armin Atzinger, Manuel Geisthoff, Simone Maschauer, Torsten Kuwert & Olaf Prante

  4. Department of Industrial Engineering and Health, Technical University Amberg-Weiden, Institute of Medical Engineering, Weiden, Germany

    Christian Schmidkonz

  5. Institute of Radiology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany

    Christoph Treutlein, Frank W. Roemer & Tobias Bäuerle

  6. Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK

    Samuel Kemble, Christopher B. Mahony & Adam P. Croft

  7. NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK

    Samuel Kemble, Christopher B. Mahony & Adam P. Croft

  8. Department of Histology, Cytology, Embryology, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine

    Rostyslav Bilyy

  9. Institute of Cellular Biology and Pathology ‘Nicolae Simionescu’, Bucharest, Romania

    Rostyslav Bilyy

  10. Clinic for Rheumatology, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany

    Jörg H. W. Distler

  11. Hiller Research Center, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany

    Jörg H. W. Distler

  12. Molecular Rheumatology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland

    Megan M. Hanlon & Ursula Fearon

  13. EULAR Centre for Arthritis & Rheumatic Diseases, St. Vincent’s University Hospital, University College Dublin, Dublin, Ireland

    Douglas J. Veale

  14. Rudolf-Virchow-Center for Integrative and Translational Imaging, University of Würzburg, Würzburg, Germany

    Hans M. Maric

  15. Institute of Human Genetics, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany

    Arif B. Ekici

  16. Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK

    Christopher D. Buckley

  17. Hospital Clínic and IDIBAPS, Barcelona, Spain

    Juan D. Cañete

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  1. Simon Rauber
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  2. Hashem Mohammadian
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Design of the study: S.R., H.M., C.S., G.S. and A.R.; acquisition of data: S.R., H.M., C.S., A.A., A.S., C.T., S.K., C.B.M., M.G., M.R.A., M.G.R., C.X., K.-T.Y., L.L., H.L., M.S.A.S., C.A.G., J.C., K.H., E.K., J.K., R.B., M.H., U.F., D.J.V., F.W.R., T.B., S.M., A.B.E., A.P.C., O.P., G.S. and A.R.; interpretation of data: S.R., H.M., C.S., A.A., A.S., C.T., F.W.R., T.B., S.M., C.D.B., A.P.C., O.P., J.D.C., G.S. and A.R.; support of material: R.B., J.H.W.D., U.F., D.J.V., H.M.M., S.M., A.P.C., T.K., O.P., J.D.C. and G.S.; paper preparation: S.R., H.M., G.S. and A.R.

Corresponding author

Correspondence to Andreas Ramming.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Kevin Wei and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 FAP-tracer uptake at axial and peripheral joints.

(a, b) FAP-tracer uptake at axial joints as assessed by PET and compared with MRI scans performed in parallel with the PET scans (only a). (c, d) 68Ga-FAPI-04 SUVmax values of axial and peripheral joints (N = 505) (c), and big (N = 55), small joints (N = 72) and tendons/entheses (N = 26) (d). Violins show median + quartiles. (e) Quantification of lesions with (i) inflammation without FAP-tracer accumulation (68Ga-FAPI-04−/MRI+), (ii) inflammation with signs of FAP-tracer accumulation (68Ga-FAPI−04+/MRI+); (iii) FAP-tracer accumulation without signs of inflammation (68Ga-FAPI−04+/MRI−) from respective PET and consecutive MRI scans of the hands of 30 patients with RA, PsA and axSpA. (f) Pearson correlation analysis (median + quartiles) between FAP-tracer uptake (maximum standardised uptake value; SUVmax) and the composite score DAS28 (disease activity score of 28 joints in patients with rheumatoid arthritis). (g) Occurrence of 68Ga-FAPI-04+/MRI− lesions in RA, PsA and axSpA. (h) 68Ga-FAPI-04 SUVmax scores (median + quartiles) of the lesions shown in (e). Statistical testing: unpaired one-way ANOVA with Tukey post hoc test.

Source data

Extended Data Fig. 2 FAP-tracer uptake in experimental arthritis correlates with disease activity.

(a) Representative images of FAP-PET imaging of wildtype mice and hTNFtg mice treated with Il23 mc for 9 days prior to imaging. (b) Representative H&E stained ankle sections, (c) micro-computed tomography (µCT) scans and (d) FAP-tracer uptake in the ankle joints of the above mice (median + quartiles; N = 3 in untreated wildtype, N = 3 in untreated hTNFtg, N = 3 in Il23mc-treated wildtype and N = 4 in Il23mc-treated hTNFtg mice). Statistical testing: unpaired one-way ANOVA with Tukey post hoc test. (e) Quantification of the number of nuclei/mm2 in ankle sections of untreated and Il23mc-treated wildtype mice (N = 7 and N = 5, respectively) and hTNFtg mice (N = 6 and N = 8, respectively). Violins show median + quartiles; statistical testing: unpaired one-way ANOVA with Tukey post hoc test. (f) µCT-based erosion and bone proliferation scores (median + quartiles) of untreated and Il23mc-treated wildtype mice (N = 6 and N = 4, respectively) and hTNFtg mice (N = 4 and N = 5, respectively). Statistical testing: unpaired one-way ANOVA with Tukey post hoc test (g) Representative images of FAP-PET imaging of wildtype and hTNFtg mice treated with Il23mc for 9 days receiving either no treatment, or inhibition TNFi or IL-17i. (h) Specific uptake of FAP-tracer in ankle joints of wildtype and hTNFtg mice treated with Il23mc for 9 days receiving either no treatment, TNFi or IL-17i (median + quartiles; N = 3 (untreated wildtype), N = 4 (hTNFtg + Il23mc untreated), N = 4 (hTNFtg + Il23mc + TNFi), N = 4 (hTNFtg + Il23mc + IL-17i). Statistical testing: unpaired one-way ANOVA with Tukey post hoc test. (i) Clinical score (paw swelling) over time for animals corresponding to (g). Mean + SD; N = 12 for all groups.

Source data

Extended Data Fig. 3 Detailed analysis of fibroblast subtypes during resolution of inflammation.

(a) Expression of the most relevant chondrocyte marker genes among each fibroblast cluster from the scRNA-seq dataset. (b) Expression of the most relevant osteoblast marker genes among each fibroblast cluster from the scRNA-seq dataset. (c) Expression of Lrrc15 across fibroblast clusters. (d) Interleukin-17 inhibition (IL-17i) associated relative likelihood of each fibroblast cluster in the scRNA-seq dataset generated from Il23mc treated hTNFtg mice with or without IL-17i as determined by MELD. (e) Velocity streams and absorption probabilities of terminal states within the scRNA-seq dataset generated from Il23mc treated hTNFtg mice with or without IL-17i are visualised over the U-map. (f) Mean UCell scores of the gene signature of Cd200+ fibroblasts across different subsets of sublining fibroblasts from hTNFtg + Il23mc and hTNFtg + Il23mc + IL-17i (N = 3 / condition, median + quartiles + min-max). P-values determined with two-sided t-test. (g) Cd200 expression in the ‘Cd200−’ fraction of sublining fibroblasts, that is Mmp3+, Il6+ and Pi16+, before and after IL-17i treatment in the hTNFtg + Il23mc model, assessed by pseudo-bulk (N = 3 / condition, median + quartiles + min-max). P-values determined with Deseq2 and adjusted for multiple testing using BH method. (h) Phase portraits of Cd200 show unspliced counts on the y-axis and spliced counts on the x-axis. The purple curve shows the scVelo dynamical fit and the dashed magenta line is scVelo’s inferred steady-state ratio. (i) Abundancy of PDPN+ PDGFRα+ CD200+ THY1+ CD49f− CD200+ sublining fibroblasts in ankle joints of untreated wildtype animals or animals from the hTNFtg + Il23mc model with or without IL-17i treatment assessed by flow cytometry (median + quartiles, N = 15, 9, 11, respectively, from three independent experiments). Statistical testing: unpaired one-way ANOVA with Tukey post hoc test.

Source data

Extended Data Fig. 4 Fibroblast subsets at different stages of spontaneously resolving STA.

(a) Proportion of different fibroblast subsets at different stages of spontaneously resolving STA. Stages correspond to days after serum transfer: untreated (d = 0), peak (d = 9), resolving (d = 15), resolved (d = 22). (N = 3/time point, median + quartiles + min-max). (b) Mean UCell scores of the gene signature of Cd200+ fibroblasts during different stages of spontaneously resolving STA (N = 3/time point, median + quartiles + min-max). P-Values determined with two-sided t-test.

Source data

Extended Data Fig. 5 FAP expression across fibroblast subtypes.

(a) Pseudobulk-based calculation of Fap expression on lining and sublining fibroblasts of a scRNA-seq dataset generated from sorted fibroblasts from wildtype animals and hTNFtg treated with Il23mc for 9 days receiving either no treatment or inhibition of IL-17 (N = 3 / condition, median + quartiles + min-max). p-values are calculated using DEseq2 and corrected for multiple testing using BH method. (b) Fap expression across conditions for osteoblasts. (c) Gating strategy to sort Fap+ Pdpn+ stromal cells from joints for scRNA-seq. FCS file for display is derived directly from the sorter. Abundance of fibroblast clusters between CD45− CD31− and Fap+ Pdpn+ CD45− CD31− stromal cells. Differential abundance is tested using edgeR and p-values are corrected for multiple testing using BH method. (N = 5 Fap+ Pdpn+ CD45− CD31− and N = 9 CD45− CD31−, median + quartile + min-max). (d) Gating strategy to identify synovial fibroblasts in joints of arthritic mice. (e) Markers for lining (CD49f+ (ITGA6)) and sublining (THY1+) fibroblasts expressed on synovial fibroblasts in mice. Strategy to identify CD200+ and CD200− synovial sublining fibroblasts. Heatmaps showing expression of THY1 and FAP among synovial fibroblasts.

Source data

Extended Data Fig. 6 Interactome analysis of ILC2s and fibroblasts.

(a) Log-fold changes of the enrichment scores for relevant inflammatory, anti-inflammatory, and damage-related GO and KEGG terms using ssGSEA shown for osteoblasts. Two-sided Wilcoxon Rank Sum test is used to find significantly negatively or positively enriched terms in each cluster (adj. p-value < 0.05; LFC > first quartile). p-values are corrected for multiple testing using BH method. (b) U-map of fibroblasts from healthy controls and serum transfer arthritis (STA) treated with interleukin-23 (IL-23) minicircle DNA (Il23mc), both sorted from R5-fate mapping (ILC2 reporter) animals. (c) Violin plots of Cd200 expression in the scRNA-seq dataset of healthy controls (N = 2) and the STA + Il23mc model (N = 1) from the R5-fate mapping animals. (d) Representation of highly correlated gene modules. Gene co-expression networks were computed using hdWGCNA, tree-cut algorithm was used to identify highly correlated gene modules (coloured), grey sections are non-correlated genes. (e) Scores for each module of co-expressing genes stratified by condition and cell type calculated by UCell. (f) Comparison of ILC2 and ILC3 markers in untreated wild type mice joints or upon IL-33- or CD200R1-induced ILC2 in vivo activation (mean + SD, N = 7, 10, 8, respectively). Statistical testing: unpaired one-way ANOVA with Tukey post hoc test (g) Expression levels of characteristic ILC2 and ILC3 genes in R5-fate mapped ILCs from healthy and arthritic joints. (h) Paw swelling of wildtype mice, wildtype treated with Il23mc, hTNFtg treated with Il23mc and hTNFtg treated with Il23mc and CD200-R1 agonistic antibody (mean + SD; N = 7, 6, 7, 7 mice, respectively). Statistical testing: unpaired one-way ANOVA with Tukey post hoc test (i) Abundancy of different ILC subsets in healthy and arthritic animals with or without CD200R1-induced resolution in joints (mean + SD, N = 6, 6, 6, respectively). Statistical testing: two-way ANOVA with Bonferroni post hoc test.

Source data

Extended Data Fig. 7 CD200R1 expression in human.

(a) CD200R1 expression on human PBMC subsets in PsA patients (N = 43). Dashed lines show median. (b) Mean log2 CPM expression (per sample) of CD200R1 expression on the identified T, NK, ILC and myeloid cell subsets in the human single cell atlas of synovial cells from RA and OA (AMPRA2 study, Synapse: syn5229784041) (N = 82 donors, median + quartile + min-max). (c) Expression of ILC2 marker genes in human PBMC-derived CD200R1+ ILCs upon CD200-Fc stimulation for 36 h under inflammatory conditions (N = 15). Statistical testing: paired two-tailed t-test. (d) IL-9 released into the supernatant of cultured CD200R1+ ILCs upon CD200-Fc stimulation for 36 h under inflammatory conditions (N = 10). Statistical testing: paired two-tailed t-test.

Source data

Extended Data Fig. 8 Map of human RA and PsA synovium.

(a) U-map of human rheumatoid arthritis (RA) (cyan) and psoriatic arthritis (red) scRNA-seq data set highlighting the cells by disease. (b) U-map of the fibroblasts from integrated human RA / PsA and hTNFtg mice treated with Il23mc showing the unsupervised identified clusters. (c) U-map of fibroblasts from integrated human RA / PsA and hTNFtg mice treated with Il23mc highlighting the previously identified mouse fibroblast subsets. (d) Bar plot quantifying the percentage of Cd200+, Il6+ and Mmp3+ mouse cells in each of the unsupervised clusters identified, showing the transcriptional similarity between each cluster of human and mouse fibroblasts. (e) Magnifications of the ROIs for better visualisation of the H.E. stained Visium slide of one PsA synovium shown in Fig. 6e. (f) ST slides of human synovial tissue from an RA patient (Bioproject PRJNA580481) with highlighted spot location and T cell rich areas, along with their corresponding spatial cluster maps identified using BayesSpace. (g) Hierarchically clustered heatmaps of cell type specific gene module scores across the spatial clusters for the three individual tissue cuts, calculated with UCell on the BayesSpace enhanced gene expression. (h) Visium slides of human synovial tissue from RA patients (n = 2; Immport SDY2213)34 along with their BayesSpace identified spatial clusters; one representative slide per patient is shown (i) Hierarchically clustered heat map of cell type specific gene module scores across the spatial clusters identified in (h), calculated with UCell on the BayesSpace enhanced gene expression.

Supplementary information

Source data

Source Data Fig. 2

Imaging source data.

Source Data Fig. 3

Source data of fibroblasts of experimental arthritis.

Source Data Fig. 4

Source flow cytometric data of subsets of fibroblasts, internalization scores.

Source Data Fig. 5

Source flow cytometric data of CD200R1 and effects of CD200-Fc.

Source Data Fig. 6

Source data of human CD200+/DKK3+ fibroblasts.

Source Data Extended Data Fig. 1

Source data of FAP-tracer uptake at axial and peripheral joints.

Source Data Extended Data Fig. 2

Source data of FAP-tracer uptake in experimental arthritis.

Source Data Extended Data Fig. 3

Source data of of fibroblast subtypes during resolution of inflammation.

Source Data Extended Data Fig. 4

Source data of fibroblast subsets at different stages of spontaneously resolving STA.

Source Data Extended Data Fig. 5

Source data of FAP expression across fibroblast subtypes.

Source Data Extended Data Fig. 6

Source data of the Interactome analysis of ILC2s and fibroblasts.

Source Data Extended Data Fig. 7

Source data of CD200R1 expression in human.

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Rauber, S., Mohammadian, H., Schmidkonz, C. et al. CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis. Nat Immunol 25, 682–692 (2024). https://doi.org/10.1038/s41590-024-01774-4

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