Notch signalling drives synovial fibroblast identity and arthritis pathology

Abstract

The synovium is a mesenchymal tissue composed mainly of fibroblasts, with a lining and sublining that surround the joints. In rheumatoid arthritis the synovial tissue undergoes marked hyperplasia, becomes inflamed and invasive, and destroys the joint1,2. It has recently been shown that a subset of fibroblasts in the sublining undergoes a major expansion in rheumatoid arthritis that is linked to disease activity3,4,5; however, the molecular mechanism by which these fibroblasts differentiate and expand is unknown. Here we identify a critical role for NOTCH3 signalling in the differentiation of perivascular and sublining fibroblasts that express CD90 (encoded by THY1). Using single-cell RNA sequencing and synovial tissue organoids, we found that NOTCH3 signalling drives both transcriptional and spatial gradients—emanating from vascular endothelial cells outwards—in fibroblasts. In active rheumatoid arthritis, NOTCH3 and Notch target genes are markedly upregulated in synovial fibroblasts. In mice, the genetic deletion of Notch3 or the blockade of NOTCH3 signalling attenuates inflammation and prevents joint damage in inflammatory arthritis. Our results indicate that synovial fibroblasts exhibit a positional identity that is regulated by endothelium-derived Notch signalling, and that this stromal crosstalk pathway underlies inflammation and pathology in inflammatory arthritis.

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Fig. 1: Single-cell RNA sequencing reveals fibroblast positional identity.
Fig. 2: Endothelial cells establish sublining fibroblast positional identity.
Fig. 3: Notch  signalling drives the differentiation of THY1-expressing fibroblasts.
Fig. 4: NOTCH3 blockade attenuates inflammatory arthritis.

Data availability

The RNA-seq data that support this work are available at ImmPort (https://www.immport.org) under accession code SDY1599 for human studies, and at the Gene Expression Omnibus (GEO) under accession code GSE145286 for mouse studies. UMAP projections and normalized counts for the human and mouse scRNA-seq data are available at https://portals.broadinstitute.org/single_cell/study/SCP469.

Code availability

All code required for data analyses and figure preparation are available at http://github.com/immunogenomics/notch.

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Acknowledgements

This work was supported by R01 AR063709, R01 AR073833 and T32 AR007530-31 (to M.B.B.); R01 AR063759, U01 HG009379 and UH2 AR067677 (to S.R.); Rheumatology Research Foundation’s Scientist Development Award (to K.W.); KL2 award (an appointed KL2 award) from Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health Award KL2 TR002542) (to K.W.); and a Joint Biology Consortium Microgrant (to K.W.). M.B.B., S.R., I.K., J.L.M. and K.W. were funded as part of a collaborative research agreement with F. Hoffmann-La Roche Ltd (Basel, Switzerland). This report includes independent research supported by the National Institute for Health Research through the Birmingham Biomedical Research Center and Wellcome Trust Clinical Research Facility at University Hospitals Birmingham NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, our funding bodies or the Department of Health. Funding was also provided by the Versus Arthritis RACE Rheumatoid Arthritis Pathogenesis Centre of Excellence (grant 20298) and Versus Arthritis Programme grant to C.D.B. and A.F. (grant 19791). A.P.C. was supported by a Wellcome Trust Clinical Career Development Fellowship no. WT104551MA. C.D.B. and T.M. are supported by funding from the Kennedy Trust for Rheumatology Research as part of the Arthritis Therapy Acceleration Programme. We thank the BWH Single Cell Genomics Core for assistance in scRNA-seq experiments. We thank members of the Brenner and Raychaudhuri laboratory for discussions. We acknowledge the University of Birmingham Medical School Imaging Suite and the assistance of R. Shaw.

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Contributions

K.W. and I.K. conceptualized the project, performed experiments, analysed data and co-wrote the manuscript. J.L.M. and T.M. performed immunofluorescence microscopy and analysed data. A.G., G.F.M.W. and J.W. performed experiments and analysed data. M.B.B. and S.R. conceived the project, supervised the work, and co-wrote the manuscript. C.D.B., A.P.C., A.F. and K.R. participated in study design, sample acquisition and analysis and supervised the confocal and digital imaging platform in Birmingham. P.E.B., J.K.L., L.T.D. and T.S.T. participated in study design and sample acquisition. C.W.S. generated the NOTCH3 blocking antibody and assisted in study design. All authors discussed the results and commented on the manuscript.

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Correspondence to Soumya Raychaudhuri or Michael B. Brenner.

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Peer review information Nature thanks Sakae Tanaka, Shannon Turley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Characterization of synovial stromal cells by scRNA-seq and flow cytometry.

a, UMAP projection of scRNA-seq data of 35,153 stromal synovial cells from 12 donors before Harmony integration (left) and after Harmony integration (right). Each cell is coloured by donor source. b, Mean expression of stromal markers (colour) and percentage of cells expressing these markers (size) among lining fibroblasts, sublining fibroblasts, mural cells and endothelial cells. c, Expression of lining marker PRG4 and sublining marker THY1 in UMAP projection. d, UMAP embedding of fibroblasts and mural cells coloured by their inferred position along positional axis from 0 (perivascular pole) to 100 (lining pole). e, Representative flow-cytometric gating scheme for the analysis of synovial stromal cells. Intact cells are identified on the basis of forward scatter (FSC-A) and side scatter (SSC-A) characteristics. Dead cells (PI+), red blood cells (CD235a+) and leukocytes (CD45+) are excluded from analysis. Endothelial cells (CD31+CD146+) and mural cells (CD31CD146+) can be distinguished from synovial fibroblasts on the basis of CD146 expression. Within the CD31CD146 gate, lining fibroblasts (PDPN+CD90) and sublining fibroblasts (PDPN+ CD90+) can be identified. The red window indicates intermediates between cell types. f, Flow cytometric quantification of stromal cell populations in the synovial tissues of patients with rheumatoid arthritis (n = 9) and osteoarthritis (n = 11). The mean percentage of endothelial cells (red), mural cells (blue), lining fibroblasts (tan), and sublining fibroblasts (green) of the total stromal (CD45) cells are shown. g, Density plot showing fibroblast content in the synovial tissues of patients with rheumatoid arthritis (orange) and osteoarthritis (blue) (n = 6) along the positional axis. h, Archetypal analysis assigned each cell a probability distribution over the six biologically defined archetypes as well as the archetype representing ambient mRNA (background). The confusion matrix represents the probability that a cell with known type (y axis) is assigned to one of seven archetypes (x axis). Each row is normalized to sum to 1. i, The positional identity (x axis) of each fibroblast plotted against the probability that the cell was classified to the ambient RNA archetype (y axis). j, Heat map showing the transcriptional gradients along the positional axis. k, Pathway enrichment of 71 genes in the intermediate group in j, using Gene Ontology biological process terms. Source data

Extended Data Fig. 2 Spatial analysis of synovial fibroblast positional markers.

a, Normalized expression (log(CP10K)) of position-associated fibroblast markers CD55 (left), GGT5 (middle) and PDPN (right) along the positional axis. b, Representative microscopic images of synovial tissues in which CD55 (yellow), CD90 (green), CD146 (blue) and VWF (red) are visualized by immunofluorescence staining. c, Cells labelled as endothelial cells are coloured in red and fibroblasts are coloured from low CD90:CD55 ratio (grey) to high CD90:CD55 ratio (blue). d, Spatial correlation between the fibroblast CD90:CD55 ratio and the distance from the nearest endothelial cell. e, Representative microscopic image of synovial tissues in which PDPN (red), GGT5 (green) and CD31 (blue) are visualized by immunofluorescence staining. f, Cells labelled as endothelial cells are coloured in red and fibroblasts are coloured from low GGT5:PDPN ratio (grey) to high GGT5:PDPN ratio (blue). g, Spatial correlation between the fibroblast GGT5:PDPN ratio and the distance from the nearest endothelial cells. For bd, synovial tissue samples were analysed from n = 4 patients with rheumatoid arthritis and n = 5 patients with osteoarthritis. For eg, synovial tissueswere obtained from n = 7 patients with rheumatoid arthritis. See Supplementary Data for individual images and spatial analysis. For d and g, Spearman correlation values and significance were computed with the base R cor.test function. Cells were binned by frequency into groups of 50 cells along the x axis and data are presented as mean and 95% confidence interval. See Supplementary Data 24 for all images and spatial analysis performed. Source data

Extended Data Fig. 3 Endothelial cells mediate the differentiation of THY1-expressing fibroblasts.

a, Fibroblast positional identity scores of CD90+ (red) and CD90 (blue) fibroblasts at the indicated passage number (fresh, n = 7 and n = 15; passage 1, n = 7 and n = 16; passage 2, n = 4 and n = 8). Box plots summarize the median, interquartile range and 95% quantiles of the positional values. b, Positional identity of fibroblasts from the fibroblast-only (top, n = 4,336 cells) and fibroblast + endothelial cell (bottom, n = 2,076 cells) organoids. Cells are coloured by their inferred position along positional axis from 0 (perivascular pole) to 100 (lining pole). Perivascular fibroblasts (red) were defined as cells with a position of less than 20, and intermediate fibroblasts were defined as being located at positions between 20 and 80. c, Flow cytometric quantification of synovial CD90+ fibroblasts and CD31+ endothelial cells from 22 synovial tissues. d, Percentage of CD90+ fibroblasts based on Doppler ultrasound scores of patients with rheumatoid arthritis (n = 20) from the AMP-RA/SLE consortium. e, THY1 expression measured by PCR with reverse transcription in fibroblasts after stimulation with the indicated recombinant proteins (n = 3 replicates for WNT3A and WNT5A, n = 4 replicates for other conditions). THY1 expression is shown as fold-change compared with the vehicle control for each condition. Significance is determined by a one-sample t-test. fh, scRNA-seq analysis of synovial organoids (n = 22,164 cells from 3 technical replicates). f, Normalized expression of defining marker gene for cell types: MKI67 for proliferating fibroblasts, VWF for endothelial cells, and THY1 for the THY1high and THY1low fibroblast groups. g, Representation of four major cell types by proportion of cells in each organoid condition. h, Notch activation score in fibroblasts derived from organoids, separated by organoid condition. Significance is determined by Spearman correlation (c, d) and one-sample Student’s t-test (e). Source data

Extended Data Fig. 4 NOTCH3 signalling in synovial fibroblasts and mural cells.

a, b, Expression of NOTCH1 and NOTCH3 in synovial stromal cells as assessed by scRNA-seq. a, Expression of NOTCH1 (top) and NOTCH3 (bottom) in lining fibroblasts (orange), mural cells (red), sublining fibroblasts (green) and endothelial cells (blue) from the human primary synovial tissue scRNA-seq dataset (n = 35,153 cells from the synovial tissues of 6 patients with rheumatoid arthritis and 6 patients with osteoarthritis). b, Mural cells were subdivided into two major mural subpopulations: vascular smooth muscle cells (orange) and pericytes (blue). No difference in the expression of NOTCH3 was observed (Wilcoxon rank-sum test P = 0.86) between these two subpopulations. c, Representative synovial tissue sections (n = 6) showing RNAscope staining for NOTCH3 in purple, immunohistochemistry staining (IHC) for CD90 in brown and nuclei in blue. The arteriole is marked with a triangle. d, The mean fluorescence intensity (MFI) of NOTCH3 in synovial mural cells (n = 18), sublining fibroblasts (n = 40), lining fibroblasts (n = 20), endothelial cells (n = 15) and leukocytes (n = 19). Box plots summarize the median, interquartile range, and 95% quantile range, and significance is determined by Spearman correlation. e, Representative immunohistochemistry image showing the staining of the NOTCH3 intracellular domain (NOTCH3-ICD) in the synovial tissue of a patient with rheumatoid arthritis (n = 3). A, arterial endothelium; V, venous endothelium. f, Immunoblot of synovial fibroblast lysates treated with antibodies against the NOTCH3 intracellular domain (NICD3, top) or GAPDH (bottom). The treatments were as follows: 10 mmol EDTA (Notch activation), plate-coated DLL4 (5 μg ml−1), or plate-coated JAG1 (5 μg ml−1), in the presence or absence of 10 μM DAPT (γ-secretase inhibitor). n = 3 independent experiments. For gel source data, see Supplementary Data 1. Source data

Extended Data Fig. 5 Endothelial cells induce the expression of NOTCH3 and JAG1 in fibroblasts.

a, Fine-cluster analysis of synovial tissue endothelial cell scRNA-seq data: arterial PODXL+ (orange) and venous DARC+ (blue) subtypes are highlighted in UMAP projection. All non-vascular endothelial cells are coloured grey. n = 35,153 cells from the synovial tissues of 6 patients with rheumatoid arthritis and 6 with osteoarthritis. b, Mean expression (colour) and percentage of cells with non-zero expression (size) of top gene markers that distinguish arterial and venous endothelial cells. c, Confocal microscopy images of synovial tissues in which NOTCH3 (red), CD90 (green) and PODXL (blue) are visualized by immunofluorescence staining. d, Images from c were then processed to segment cells and output their spatial location and mean intensity of all three markers. Cells labelled as arterial vascular endothelial cells are coloured in red, and non-arterial endothelial cells are coloured according to NOTCH3 expression, from low (grey) to high (blue). e, Spatial correlation between the expression of NOTCH3 and the distance from the nearest arterial vascular endothelial cell. Non-arterial cells in each image were used to analyse the function between arterial distance, measured by distance to the nearest arterial vascular endothelial cells, and NOTCH3 expression. Spearman correlation values and significance were computed with the base R cor.test function. Cells were binned by frequency into groups of 50 cells along the x axis. f, g, Flow cytometric analysis of fibroblasts and endothelial cell co-culture experiments. Cells are gated on CD31CD90+ fibroblasts to exclude endothelial cells (n = 4 replicates). f, Representative flow plot of NOTCH3, CD90 and JAG1 expression in the fibroblast-only culture (left) and the fibroblast + endothelial cell co-culture (right). Colour indicates the level of expression of JAG1 in fibroblasts. g, Mean fluorescence intensity showing the expression of JAG1 in fibroblasts in the presence and absence of endothelial cells (EC), the γ-secretase inhibitor DAPT, or after pre-treatment with control siRNA or siRNA against NOTCH3 (si-NOTCH3). Box plots summarize the median, interquartile range and 95% quantile range. h, Biaxial plots of normalized JAG1 and NOTCH3 expression (in CP10K) from the scRNA-seq data of synovial tissues (top, n = 12 donors) and synovial organoids (bottom, n = 3 organoids).

Extended Data Fig. 6 NOTCH3 expression in mouse synovia and the effects of inhibition of NOTCH3 or NOTCH1.

a, Serial sections of the synovia of arthritic mice (n = 5) showing representative haematoxylin and eosin staining (top) and immunofluorescence staining (bottom) of CD45 (red), NOTCH3 (green) and DAPI (blue). b, Representative haematoxylin and eosin staining of mouse joints from wild-type mice (top, n = 6) and Notch3−/− mice (bottom, n = 6). S, synovium. c, scRNA-seq of wild-type, Notch3−/−, isotype control-, or anti-NRR3-treated mouse synovial cells (total of n = 18,491 cells from n = 10 mice per group). Cells were clustered and labelled in a joint analysis. In the seven identified stromal populations, differential gene expression was performed for anti-NRR3 compared with isotype control and for Notch3−/− compared with wild type. Each gene was plotted as a dot, representing the log-transformed fold-change. d, e, Clinical index (d) and paw swelling (e) in IgG control antibody-treated (dark blue, n  = 20) or anti-NRR1-treated (light blue, n = 20) mice after serum transfer from K/BxN mice. The significance of each treatment was determined by mixed effects linear models, controlling for time as a categorical fixed effect and mouse as a random effect. f, g, UMAP projections of normalized expression of NOTCH1 in human primary tissue scRNA-seq data (n = 35,153 cells) (f) and Notch1 in mouse scRNA-seq data (n = 18,491 cells) (g).

Extended Data Fig. 7 Characterization of intermediate fibroblast positional gene signature.

a, Gene enrichment scores for intermediate position-specific genes (x) plotted against Notch activation score (y) in synovial organoids (n = 22,164 cells; n = 3 organoids per condition). DAPT indicates that organoids were cultured in the presence of 10 μM γ-secretase inhibitor DAPT. The fibroblast + endothelial cell organoids (yellow) show evidence of increased Notch signalling but little enrichment in intermediate zone genes, as compared to fibroblasts that were cultured alone (blue) or co-cultured in the presence of DAPT (red). b, Enrichment of intermediate gene score in mouse synovial fibroblasts. Enrichment is lowest (red) in the previously identified lining and sublining zones and highest (blue) in the fibroblasts positioned in between the two zones, in the intermediate zone. c, Enrichment of pre-defined synovial fibroblast populations in the AMP-RA/SLE scRNA-seq dataset (n = 1,844 cells).

Supplementary information

Supplementary Information

This file contains: 1 uncropped scans and size marker indications. Control (GAPDH) were run the same gel as sample processing controls; 2, individual confocal microscopy images used in spatial analysis; 3, fibroblast positional marker intensity in each individual image; 4, Spatial analysis and correlation between distance to endothelial cells and fibroblast positional marker intensity in each individual image.

Reporting Summary

Supplementary Table 1

Differential expression results for major populations identified in synovial tissue and organoid scRNAseq datasets. Dataset refers to the tissue and organoid datasets. Celltype identifies the cell population, as assigned through clustering analysis. Symbol denotes the gene name. AUC is the area under the receiver operator curve, reflecting overexpression (>0.5) or under-expression (<0.5) in the population. FDR is the signed corrected p value, in log scale, assigned a sign based on positive or negative differential expression. logFoldChange and average Expression are the log fold change and mean normalized expression level of the gene in the specified cell population. percent Non-zero denotes the percent of cells in this population that had at least 1 UMI of the gene expressed.

Supplementary Table 2

950 genes clustered along the fibroblast positional trajectory. The second column refers to the hierarchically clustered groups, as colored in Extended Data Fig. 1h. The third column describes the positional association of that gene: sublining, lining, or intermediate zone.

Supplementary Table 3

Data used for quantified imaging analysis of CD90:PRG4 ratio. Each row represents a segmented cell. Sample and Image.number denote the donor and image number from that donor. center_x and center_y are the x and y spatial coordinates of the cell's center in the image. THY1 (CD90), PRG4, VWF, and MCAM represent per-image scaled intensity values of the corresponding markers. Region was curated manually in the Definiens software, based on morphology. Disease is the clinical status of the donor, either RA for rheumatoid arthritis or OA for osteoarthritis. ncells_image is the number of cells segmented in that image. thy1_prg4_ratio is the scaled ratio of CD90 to PRG4 intensity. is_ec reports whether the cell was labeled an endothelial cell (1) or not an endothelial cell (0 or -1). dist_EC is that cell's distance to its nearest endothelial cell. This distance is only defined for non-endothelial cells.

Supplementary Table 4

Top 1000 genes associated with the scRNAseq transcriptional gradient. For each gene, we constructed a linear model of gene expression vs gradient position. The standard output from this linear model is reported in columns 2 to 6 here. The final column, associated_with, labels the genes as more associated with the Perivascular or Lining end of the transcriptional gradient.

Supplementary Table 5

Data used for quantified imaging analysis for of CD90:CD55 ratio. Each row represents a segmented cell. Sample and Image.number denote the donor and image number from that donor. center_x and center_y are the x and y spatial coordinates of the cell's center in the image. CD90, CD55, VWF, and CD146 represent per-image scaled intensity values of the corresponding markers. Region was curated manually in the Definiens software, based on morphology. Disease is the clinical status of the donor, either RA for rheumatoid arthritis or OA for osteoarthritis. ncells_image is the number of cells segmented in that image. ratio is the scaled ratio of CD90 to CD55 intensity. is_ec reports whether the cell was labeled an endothelial cell (1) or not an endothelial cell (0 or -1). dist_EC is that cell's distance to its nearest endothelial cell. This distance is only defined for non-endothelial cells.

Supplementary Table 6

Data used for quantified imaging analysis of GGT5:PDPN ratio. Each row represents a segmented cell. Sample and Image.number denote the donor and image number from that donor. center_x and center_y are the x and y spatial coordinates of the cell's center in the image. GGT5, PDPN, and CD31 represent per-image scaled intensity values of the corresponding markers. Region was curated manually in the Definiens software, based on morphology. Disease is the clinical status of the donor, either RA for rheumatoid arthritis or OA for osteoarthritis. ncells_image is the number of cells segmented in that image. ratio is the scaled ratio of GGT5 to PDPN intensity. is_ec reports whether the cell was labeled an endothelial cell (1) or not an endothelial cell (0 or -1). dist_EC is that cell's distance to its nearest endothelial cell. This distance is only defined for non-endothelial cells.

Supplementary Table 7

Differential expression results used in the ligand receptor analyses. Each row is a pair of genes known to participate in a ligand-receptor signaling interaction. For each ligand-receptor pair, we report the differential expression analysis results of ligands upregulated in endothelial cells and receptors upregulated in perivascular cells. We report the percent of non-zero expressing cells as well as Benjamini Hochberg corrected FDR from a Wilcoxon rank sum differential expression test. Finally, we report the ligand and receptor percent expressing and FDR statistics for both the synovial tissue and co-cultured organoid datasets separately.

Supplementary Table 8

Data used for quantified analysis for images in Extended Data Fig. 5. Each row represents a segmented cell. Sample and Image.number denote the donor and image number from that donor. center_x and center_y are the x and y spatial coordinates of the cell's center in the image. CD90, NOTCH3, and PODXL represent per-image scaled intensity values of the corresponding markers. Region was curated manually in the Definiens software, based on morphology. Disease is the clinical status of the donor, either RA for rheumatoid arthritis or OA for osteoarthritis. ncells_image is the number of cells segmented in that image. is_ec reports whether the cell was labeled an endothelial cell (1) or not an endothelial cell (0 or -1). dist_EC is that cell's distance to its nearest arterial endothelial cell. This distance is only defined for non-arterial-endothelial cells.

Supplementary Table 9

Differential expression results for in vitro JAG1 and DLL4 fibroblast stimulation. Columns 2 to 7 refer to JAG1 stimulation; columns 8 to 13 refer to DLL4 stimulation. The columns are the standard output from limma differential expression analysis: logFC is log fold change of stimulated vs unstimulated samples, AveExpr is the mean expression of the gene in all samples, t is the moderated student's t statistic, P.Value is the nominal p value, adj.P.Val is the Benjamini Hochberg adjusted FDR, B is the moderated B statistic.

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Wei, K., Korsunsky, I., Marshall, J.L. et al. Notch signalling drives synovial fibroblast identity and arthritis pathology. Nature (2020). https://doi.org/10.1038/s41586-020-2222-z

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