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

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.

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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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Authors and Affiliations

Authors

Contributions

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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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-FAPI04+/MRI+); (iii) FAP-tracer accumulation without signs of inflammation (68Ga-FAPI04+/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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