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Rheumatoid arthritis is a systemic autoimmune disease that affects up to 1% of the population3. It is characterized by inflammation of synovial joint tissue and extra-articular manifestations that lead to pain, joint damage and disability1. The clinical course of rheumatoid arthritis has been transformed by targeted therapies, including those aimed at TNF, IL-6, B cells, T cell co-stimulation and the JAK–STAT pathway1. However, many patients are refractory to these therapies and do not achieve remission2. Thus, there is a clinical need for new treatment targets and for predictors of patient-specific responses to treatment. Genetic diversity and variable responses to targeted therapies suggest that rheumatoid arthritis is a heterogeneous disease4. However, genetic and clinical differences in disease duration or activity do not reliably predict the treatment response or druggable targets1,5.

A more granular understanding of cell states and synovial phenotypes in inflamed joints could inform prognosis and therapeutic targets. Encouragingly, clinical trials using histologic or bulk RNA-sequencing (RNA-seq) analysis of synovial tissue suggest that treatment response may depend on synovial cellular composition6,7. Previous studies have identified effector cell states in rheumatoid arthritis pathophysiology that represent promising treatment targets, including HBEGF+IL1B+ macrophages, SLAMF7+ super-activated macrophages, MERTK+ macrophages, CD11c+ autoimmune-associated B cells (ABCs), PD-1hi T peripheral helper (TPH) cells, granzyme K+CD8+ T cells and NOTCH3+ synovial fibroblasts8,9,10,11,12,13,14,15,16. To determine whether some states are enriched only in specific subsets of patients, we analysed cell-state composition in a clinically diverse set of patients with active rheumatoid arthritis. As rheumatoid arthritis shares disease-associated tissue cell states and genetic risk loci with other autoimmune diseases17,18, these analyses may offer insights into other diseases that feature tissue inflammation.

Recruitment and multi-modal analysis of tissue

We obtained a total of 82 synovial tissue samples from patients exhibiting moderate to high disease activity (clinical disease activity index (CDAI) ≥ 10). To capture a clinical spectrum of rheumatoid arthritis, we collected biopsies from treatment-naive patients (n = 28) early in their disease course, methotrexate (MTX)-inadequate responders (n = 27), and anti-TNF agent-inadequate responders (n = 15) as well as from patients with osteoarthritis (n = 9) (Fig. 1a–d, Supplementary Table 1).

Fig. 1: Overview of the multi-modal single-cell synovial tissue pipeline and cell-type abundance analysis that reveals distinct rheumatoid arthritis CTAPs.
figure 1

a–d, Description (a) of the patient recruitment, clinical and histologic metrics, synovial sample processing pipeline and computational analysis strategy, including identification of major cell types and fine-grained cell states (b), definition of distinct rheumatoid arthritis CTAPs (c), and cell neighbourhood associations with each CTAP or with clinical or histologic parameters for each major cell type (d). OA, osteoarthritis; RA, rheumatoid arthritis; sig., significant. e, Integrative uniform manifold approximation and projection (UMAP) based on mRNA and protein discriminated major cell types, f, Hierarchical clustering of cell-type abundances captures six rheumatoid arthritis subgroups, referred to as CTAPs. The nine osteoarthritis samples are shown as a comparison. Each bar represents one synovial sample, coloured by the proportion of each major cell type. g, PCA of major cell-type abundances. Each dot represents a sample, plotted based on its PC1 and PC2 projections and coloured by CTAPs. h, Representative synovial tissue fragments from each of the CTAPs. Top row, haematoxylin and eosin (H&E) staining. Middle row, immunofluorescence microscopy for CD3, CD34, CD68, CD90, CLIC5 and HLA-DR. Bottom row, immunofluorescence microscopy for CD3, CD20 and CD138. Scale bars: 100 μm (CTAP-EFM) and 250 μm (all other images). Single-colour images are presented in Supplementary Fig. 4. A total of 150 fragments from 36 donors were stained in batches and analysed as a single cohort. Parts of Fig. 1a were generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.

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We simultaneously characterized the transcriptome and surface expression of 58 proteins (Supplementary Table 2) in a total of 314,011 cells (more than 3,800 cells per sample) after quality control (Supplementary Fig. 1). We integrated surface marker and RNA data using canonical correlation analysis, corrected batch effects and defined six major cell types: T, B and plasma (B/plasma), natural killer (NK), myeloid, stromal and endothelial cells (Fig. 1e, Extended Data Fig. 1a, Supplementary Fig. 2 and Supplementary Table 3).

Stratifying synovium by cell-type abundance

To define potentially distinct tissue inflammatory phenotypes, we hierarchically clustered synovial samples on the basis of the frequency of the six major cell lineages (Fig. 1f,g). On the basis of in-group similarity with bootstrapping, we arrived at six different categories that we call CTAPs, which are largely robust to adjustment for treatment and disease duration (Extended Data Fig. 1b–e). We named the CTAPs on the basis of relatively enriched cell type(s): (1) endothelial, fibroblast and myeloid cells (EFM); (2) fibroblasts (F); (3) T cells and fibroblasts (TF); (4) T and B cells (TB); (5) T and myeloid cells (TM); and (6) myeloid cells (M) (Extended Data Fig. 1d and Supplementary Table 4). Alternative clustering schemes using highly variable genes, all transcriptional states, or separating plasma cells from non-plasma B cells led to similar results (Supplementary Fig. 3). Post hoc mapping of the osteoarthritis samples demonstrates that they most resemble CTAP-EFM and CTAP-F (Extended Data Fig. 1f). Categorization by effector functions using pseudo-bulk expression of 55 cytokines, chemokines and growth factors was similar to the cell lineage-based CTAP categorization (Extended Data Fig. 1g,h).

CTAP patterns are consistent across fragments

To examine the robustness of CTAPs across paired biopsy fragments from the same joint, we performed immunofluorescence microscopy staining on synovial tissue fragments from a subset of patients (n = 36) (Fig. 1h and Supplementary Fig. 4). We compared cell-type proportions in individual high-density biopsy fragments with the disaggregated cellular indexing of transcriptomes and epitopes (CITE-seq)-based cell frequencies (Extended Data Fig. 1i,j). The proportions of cell types followed the patterns predicted by the CITE-seq-based CTAP assignment. For example, CD20+ (that is, non-plasma) B cells were most frequent in CTAP-TB, whereas CD68+ myeloid cells were most frequent in CTAP-M and CTAP-TM. As the histology analysis was performed on synovial tissue fragments separate from those used for CITE-seq, these findings support the consistency of CTAP assignments across a joint.

A rheumatoid arthritis synovial cell-state atlas

We defined finer-grained cell states and quantified cluster abundances within cell types (Fig. 2 and Extended Data Fig. 2) using canonical variates from canonical correlation analysis reflecting both RNA and protein for T and B cells and mRNA principal components for myeloid, stromal and endothelial cell states (Supplementary Figs. 5 and 6 and Supplementary Table 3). In total we defined 77 cell states: 24 T cell clusters (n = 94,046 cells), 9 B/plasma cell clusters (n = 30,691), 14 NK clusters (n = 8,495), 15 myeloid clusters (n = 76,181), 5 endothelial clusters (n = 25,043) and 10 stromal clusters (n = 79,555) (Fig. 2 and Supplementary Table 5). Cell states associated with rheumatoid arthritis versus osteoarthritis in a previous study of more than 5,000 synovial cells were also associated with rheumatoid arthritis in this dataset (Supplementary Fig. 7 and Supplementary Table 6).

Fig. 2: Cell-type-specific single-cell analysis captures 77 distinct cell states in rheumatoid arthritis synovium.
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a–f, Cell-type-specific reference UMAPs for T cells (a) B/plasma cells (b), NK cells (c), myeloid cells (d), stromal cells (e) and endothelial cells (f), coloured by fine-grained cell-state clusters. MT, mitochondrial; MZ, marginal zone; pDC, plasmacytoid dendritic cell.

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The 24 T cell clusters spanned innate-like states and CD4+ and CD8+ adaptive lineages, including states implicated in autoimmunity, such as regulatory CD4+ T cells (Treg) (T-8 and T-9) and CXCL13- and IL21-expressing T follicular helper (TFH) and TPH cells17,19 (T-3 and T-7) (Fig. 2a and Extended Data Figs. 2 and 3). T-7 exclusively comprised TPH cells and expressed more ICOS, IFNG and GZMA, whereas T-3 contained TFH and TPH (TFH/TPH) cells expressing the lymphoid homing marker gene CCR7. CD8+ subsets expressed different combinations of GZMB and GZMK, reflecting differential cytotoxic potential. Using cell surface protein data, we resolved T cell clusters that were not observed in our earlier study8, including CD4+GNLY+ (T-12), double-negative (CD4−CD8−) γδ T cells expressing TRDC (T-22 and T-23) and double-negative and CD8+ T cells expressing ZBTB16 (which encodes PLZF) that resemble NK T cells and mucosal-associated innate T (MAIT) cells (T-21).

CD20 (encoded by MS4A1)-expressing B cells comprised six clusters, including IgM+IGHD+TCL1A+ naive (B-2), CD24hiCD27+IgM+ unswitched memory (B-1) and CD24+CD27+CD11b+ (CD11b is also known as ITGAM) switched memory (B-0) B cells (Fig. 2b and Extended Data Figs. 2 and 4). CD11c+CXCR5low (CD11c is also known as ITGAX) ABCs (B-5) expressed LAMP1, HLA-DR and CIITA, indicating B cell antigen presentation20,21,22. Unexpectedly, we observed CD1C+ B cells (B-3) with CD27 and IGHD expression, consistent with recirculating extrasplenic marginal zone B cells. These and other non-plasma B cells expressed IL6 and TNF (Extended Data Fig. 4d). We identified AICDA+BCL6+ germinal centre-like B cells (B-4), consistent with ectopic germinal centre formation in synovium23. Plasma cell populations included HLA-DR+IgG+ plasmablasts (B-7) expressing MKI67, IgM+ plasma cells (B-6) and mature IGHG1+IGHG3+ plasma cells (B-8), possibly reflecting both in situ generation and recruitment from the circulation.

We also captured innate lymphocytes, including CD56hiCD16− NK (eight clusters), CD56lowCD16+ NK (four clusters) and CD56lowCD16−IL7R+ innate lymphoid cells (ILCs) (two clusters) (Fig. 2c and Extended Data Figs. 2 and 5). CD56hiCD16− NK cells were more abundant (mean 48% per donor) than CD56lowCD16+ NK cells (36%) and ILCs (13%). CD56hiCD16− NK clusters expressed GZMK, with variable expression of cytotoxicity genes such as GZMB and GNLY. CD56lowCD16+ NK cells exhibited universally high expression of GZMB, GNLY and PRF1. Several NK cell clusters highly expressed IFNG (Extended Data Fig. 5d). ILCs, identified by the absence of CD56 and CD16 with high CD127 (also known as IL-7Rα) protein, included group 3 ILCs (RORC+ NK-12) and group 2 ILCs24 (CD161+GATA3+ NK-13).

We identified 15 myeloid clusters (Fig. 2d). CD68 and CCR2 discriminated tissue macrophages from infiltrating monocytes (Extended Data Figs. 2 and 6). Three tissue macrophage clusters (M-0, M-1 and M-2) were abundant in both osteoarthritis and rheumatoid arthritis synovium and expressed the phagocytic factors CD206 (also known as macrophage mannose receptor (MMR)) and CD163 and MERTK (Extended Data Fig. 6b–d), suggesting a homeostatic debris-clearing function25,26. LYVE1 expression (M-0) is likely to indicate a perivascular function12,27. Infiltrating monocytes included a previously described IL1B+FCN1+HBEGF+ pro-inflammatory subset (M-7), probably derived from classical CD14hi monocytes8,12 and a STAT1+CXCL10+ subset (M-6) that expresses interferon-response genes. MERTK+HBEGF+ (M-3) and SPP1+ (M-4) subsets expressed SPP1 (osteopontin) and other factors consistent with wound-healing responses28,29. Four dendritic cell (DC) populations corresponded to subsets described by Villani et al.30. CLEC10Ahi DC2 and DC3 (M-9 and M-10) and CLEC9A+THBD+ DC1 (M-12) are likely to activate CD4+ and CD8+ T cells, respectively, whereas DC4 (M-11) expressed CD16+ monocyte factors and an interferon signature (Extended Data Fig. 6d). A fifth DC subset (M-14) highly expressed the endosomal marker LAMP331.

Fibroblasts segregated broadly into lining (PRG4hi) and sublining (THY1+PRG4low) subsets and NOTCH3+MCAM+(CD146) mural cells (Fig. 2e and Extended Data Figs. 2 and 7a–f). As previously described, lining fibroblasts (F-0 and F-1) were depleted in rheumatoid arthritis relative to osteoarthritis and subdivided into PRG4+CLIC5+ (F-0), PRG4+ (F-1) and RSPO3+ (F-8) populations, the last exhibiting an intermediate lining–sublining phenotype. Sublining fibroblasts separated into HLA-DRA+, CD34+ and DKK3+ groups8,32,33. The CD34+ sublining fibroblast cluster (F-2) highly expressed PI16 and DPP4 (CD26), suggesting an undifferentiated, progenitor-like state34. CXCL12+ fibroblasts included an inflammatory CD74hiHLAhi cluster (F-5) and a CXCL12+SFRP1+ cluster (F-6) with the highest levels of IL6, which encodes a proven drug target in rheumatoid arthritis.

Synovial endothelial cells separated into lymphatic endothelial cells and blood endothelial cells. Lymphatic endothelial cells (E-4), identified on the basis of high expression of the lymphatic markers LYVE1 and PROX1, exhibited high expression of CCL21 and FLT435,36 (Fig. 2f and Extended Data Figs. 2 and 7g,k). Among blood endothelial cells, we observed several clusters along an arterial-to-venous axis, including NOTCH4+ arteriolar (E-3), SPARC+ capillary (E-0) and CLU+ venular (E-1 and E-2) cells. Arteriolar cells expressed high levels of CXCL12, LTBP4, NOTCH4 and the NOTCH ligand DLL4. SPARC+ capillary cells expressed collagen and extracellular matrix genes. Venular cells further subdivided into LIFR+ (E-1) and ICAM1+ (E-2) and had high expression of inflammatory genes such as IL6 and HLA genes, along with genes that facilitate leukocyte transmigration, such as ICAM1 and SELE (E-selectin) (Extended Data Fig. 7i).

CTAPs are defined by specific cell states

We used co-varying neighbourhood analysis (CNA) to identify single-cell-resolution ‘neighbourhoods’ associated with individual CTAPs. We use ‘expanded’ and ‘depleted’ to refer to differences in relative abundance within a cell type, accounting for age, sex and cell count per sample. Of note, this may not reflect a difference relative to total synovial cells. We tested each cell type for associations with all CTAPs, recognizing that even less enriched cell types may contain critical subsets.

We observed skewed T and B cell neighbourhoods in CTAP-TB (permutation P = 0.046 and 0.03, respectively) (Fig. 3a, Extended Data Fig. 3e, Supplementary Tables 7 and 8). T cell neighbourhoods among CD4+ TFH/TPH (T-3) and CD4+ TPH (T-7) cells were expanded, whereas neighbourhoods among cytotoxic CD4+GNLY+ (T-12) and CD8+GZMB+ cells (T-15) were depleted. Among B cells, we observed expanded neighbourhoods in memory B (B-0 and B-1) and ABC (B-5) clusters, whereas IgG1+IgG3+ and IgM+ plasma cells (B-8 and B-6) were relatively depleted (Fig. 3b and Extended Data Fig. 4e). We note that although plasma cells are depleted among B/plasma cells in CTAP-TB, plasma cells are enriched among total cells in CTAP-TB (4.1% compared with 0.6–3.1% in other CTAPs) (Extended Data Fig. 4e,f). Although TPH (T-7), TFH/TPH (T-3) and ABC (B-5) cells are enriched in CTAP-TB, they are present in all six CTAPs (Extended Data Figs. 3e and 4e). By contrast, germinal centre cells (B-4) were almost exclusively found in CTAP-TB (Extended Data Fig. 4e). Consistent with a role for TFH/TPH and IL-21 in ABC generation37, the frequency of ABCs (B-5) amongst B/plasma cells correlated with the proportion of TPH (T-7) and TFH/TPH (T-3) among T cells (Pearson r = 0.50, P = 3.7 × 10−6 and Pearson r = 0.24, P = 0.034, respectively) (Fig. 3c and Extended Data Fig. 4g).

Fig. 3: Different T cell, B cell and NK cell populations are associated with rheumatoid arthritis CTAPs.
figure 3

a, Associations of T cell neighbourhoods with CTAP-TB and CTAP-TF. P values are from the CNA test for each CTAP within T cells. b, Associations of B/plasma cell neighbourhoods with CTAP-TB. c, Percentage of TPH (T-7) as a proportion of T cells and CD11c+ LAMP1+ ABCs (B-5) as a proportion of B/plasma cells for each donor sample. R and P values are calculated from Pearson correlation and two-sided t-tests, respectively. The shaded region represents 95% confidence interval. d, Plasmablast count (left), ABC count (centre) or percentage of annexin+ cells (right) stratified by co-cultured T cell subset. Points represent samples and shapes correspond to samples from the same donor, which were tested in independent experiments (n = 3). Data are mean ± s.d. e, Associations of NK cell neighbourhoods with CTAP-TF. a,b,d, For all CNA results, cells in UMAPs are coloured red (positive) or blue (negative) if their neighbourhood is significantly associated with the CTAP (false discovery rate (FDR) < 0.05), and grey otherwise. Distributions of neighbourhood correlations are shown for clusters with more than 50% of neighbourhoods correlated with the CTAP at FDR < 0.05. Global P values were obtained based on permutation testing from the CNA package.

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We hypothesized that the preferential enrichment of TPH and TFH cells in CTAP-TB reflected the ability of these subsets to sustain and activate B cells. To test this hypothesis, we sorted TPH and TFH cells and other memory CD4+ T cells, as well as CD45RA+ effector memory CD8+ T (TEMRA) cells and CD45RO+ memory CD8+ T cells, which are enriched for GZMB+ and GZMK+ CD8+ T cells, respectively16 from blood and co-cultured them with B cells and staphylococcal enterotoxin B superantigen (Fig. 3d, Extended Data Fig. 4h and Supplementary Fig. 8). TPH and TFH cells efficiently induced B cell differentiation into plasmablast and ABC phenotypes. Notably, non-TFH/TPH memory CD4+ T cells were also able to induce ABC differentiation, but not plasmablast differentiation. CD8+ T cells did not induce B cell differentiation despite being functionally potent in cytotoxicity assays.

T cell neighbourhoods enriched in CTAP-TF (permutation P = 0.036) consisted mainly of cytotoxic CD4+GNLY+ (T-12) and CD8+GZMB+ cells (T-15) as well as naive CD4+ and CD8+ T cells (T-4 and T-16) (Fig. 3a, Extended Data Fig. 3e and Supplementary Tables 7 and 8). GZMB-expressing CD56lowCD16+ NK cells (NK-0–3) were also enriched in CTAP-TF, and the proportion of GZMB+ NK cells (NK-0–3) correlated with the proportion of GZMB+ T cells (T-15) (Pearson r = 0.63, P = 4.87 × 10−10; Fig. 3e and Extended Data Fig. 5g). Conversely, GZMK+ CD8+ T cells (T-13 and T-14) correlated with GZMK+ NK cells (NK-4–8, Pearson r = 0.51, P = 1.41 × 10−6), suggesting that GZMB- and GZMK-expressing CD8+ T and NK cells share a transcriptional programme influenced by their tissue environments.

CTAP-TF also exhibited specific expansion among CXCL12+SFRP1+ sublining fibroblasts (F-6), which expressed IL6 but not HLA-DR genes (Fig. 4a and Extended Data Fig. 7c). By contrast, CTAP-M demonstrated enrichment of CD74hiHLAhi sublining fibroblast neighbourhoods (F-5) among stromal cells (permutation P = 10−3). We also observed that SPARC+ capillary cells (E-0) were expanded among endothelial cells in CTAP-M (permutation P = 7 × 10−3; Extended Data Fig. 7l).

Fig. 4: Different stromal, myeloid and endothelial cell populations are associated with rheumatoid arthritis CTAPs.
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a, Association of stromal cell neighbourhoods with CTAP-TF, CTAP-M and CTAP-F. b, Association of myeloid cell neighbourhoods with CTAP-EFM, CTAP-M and CTAP-F for all CNA results. Cells in UMAPs are coloured red (positive) or blue (negative) if their neighbourhood is significantly associated with the CTAP (FDR < 0.05), and grey otherwise. Distributions of neighbourhood correlations are shown for clusters with more than 50% of neighbourhoods correlated with the CTAP at FDR < 0.05. Global P values were obtained based on the permutation testing from the CNA package.

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Among myeloid populations, cell neighbourhoods within SPP1+ (M-4) and MERTK+HBEGF+ (M-3) macrophages were enriched in CTAP-M, suggesting recruitment of inflammatory monocytes and transition to macrophage function (Fig. 4b). Pro-inflammatory IL1B+ macrophages (M-7), known to be expanded in patients with rheumatoid arthritis in general8, were less frequent in CTAP-EFM relative to other CTAPs.

Of note, CTAP-M and CTAP-F exhibited contrasting cell enrichments and depletions across three cell types. (Fig. 4a,b and Extended Data Fig. 7l). Specifically, lining (F-0 and F-1) and CD34+ sublining (F-2) fibroblasts (permutation P = 3 × 10−3), MERTK+LYVE1+ (M-0) and MERTK+S100A8+ (M-2) macrophages (permutation P = 10−3), and LIFR+ venular (E-1) and ICAM1+ venular (E-2) endothelial cells were expanded in CTAP-F (permutation P = 3 × 10−3) and depleted in CTAP-M.

Given their high plasticity, we hypothesized that monocytes entering synovial tissue are shaped by the network of cell types and soluble factors associated with each CTAP. We tested this concept for CTAP-M and CTAP-TM by exposing human blood CD14+ monocytes to factors enriched in these tissues and then examining which CTAP-associated myeloid state these cells resembled (Extended Data Fig. 6g). We found that activated CD8+ T cell factors that mark CTAP-TM induced a set of genes that mark the STAT1+CXCL10+ macrophage state that is enriched in CTAP-TM (Extended Data Fig. 6h,i). Conversely, factors enriched in CTAP-M, including M-CSF, TGFβ and fibroblasts, drove monocytes towards the MERTK+HBEGF+ phenotype that is enriched in CTAP-M.

Cell states are associated with histology

We used CNA to test for cell neighbourhoods associated with histologic features of rheumatoid arthritis synovium, including Krenn scores and discrete histologic cell density and aggregate scores reflecting inflammatory cell infiltration and organization (Fig. 5a, Supplementary Fig. 9a and Methods). Several T cell states were associated with aggregate scores (permutation P = 0.0088), including neighbourhoods among CD4+ TFH/TPH (T-3), GZMK+CD8+ T cells, and some memory CD4+ T cells (Fig. 5a, Supplementary Fig. 9b and Supplementary Table 7). A GZMK+ NK cell cluster, NK-4, was associated with both density and aggregate scores (permutation P = 3 × 10−4 and 10−4, respectively) (Supplementary Fig. 9b). Neighbourhoods within STAT1+CXCL10+ (M-6), SPP1+ (M-4) and inflammatory DC3 (M-9) (Fig. 5a and Supplementary Fig. 9b) were associated with both aggregate and density scores (permutation P = 0.006 and P = 0.005, respectively). Among B cells, IgM+ plasma cells (B-6), plasmablasts (B-7) and ABCs (B-5) were associated with aggregate scores (permutation P = 0.007) (Fig. 5a and Supplementary Fig. 9b). These disparate cell-state associations with aggregate scores probably reflect the diverse composition of aggregates, which can be T cell-dominant, plasma cell-dominant or T and B cell follicles38,39.

Fig. 5: Single-cell CNA reveals significant association of cell states with disease indicators, genetic factors and treatment response.
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a, Heat map of CNA associations of specific cell states with each rheumatoid arthritis CTAP. Colours represent the percentage of cell neighbourhoods from each cell state with local (neighbourhood-level) phenotype correlations passing FDR < 0.05 significance from white to pink (expanded) or green (depleted). Cell types significantly associated globally (at cell-type level) with a phenotype at permutation P < 0.05 are boxed in black. b, Alluvial plot showing CTAP classification of samples prior to and at week 16 after starting treatment with either tocilizumab or rituximab (n = 45). c, Associations between clinical response and CTAPs after correcting for sex, age, treatment and CCP status in the baseline (week 0) samples from the R4RA study (n = 133). The percentage of variance explained by CTAPs alone and P value are calculated with ANOVA tests. Dots represent odds ratios and bars represent 95% confidence intervals. d, Significance of correlations between rheumatoid arthritis risk gene expression and CTAP-associated cells. Significance levels are shown in red (P < 0.01), yellow (0.01 < P < 0.05), and white (P > 0.05). Genes with low counts (more than one unique molecular identifier among less than 5% of cells with a given cell type) were not analysed in that cell type (grey boxes). Bottom, UMAPs displaying normalized expression levels of selected genes in T cells (IL6R and LEF1), B cells (WDFY4) and endothelial cells (PRKCH).

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After accounting for age, sex, cell count and clinical collection site (Methods), we found that CTAPs account for 18% of variance of histologic density (P = 0.0035) and 18% of variance for aggregates (P = 0.0059), with CTAP-TB and CTAP-TF having the highest scores for both (Extended Data Fig. 8a,b). Consistent with these observations, CTAPs are associated with Krenn inflammation scores (P = 4 × 10−4), but not with Krenn lining scores (P = 0.11) (Extended Data Fig. 8a,b). Ultrasound measurements in the biopsied joint did not vary by CTAP (Extended Data Fig. 8b). In our dataset, we observed no association between Krenn inflammation and power doppler scores, consistent with some previous studies40,41,42 (Extended Data Fig. 8c).

CTAPs are largely independent of clinical metrics

Cyclic citrullinated peptide (CCP) autoantibodies are known to confer a higher risk of severe disease and radiographic progression43. CCP titre values differed across CTAPs (P = 0.023, 18% variance), with CTAP-M having the lowest CCP titres, even after restricting the analysis to seropositive patients (P = 0.0047) (Extended Data Fig. 8a,d). HLA-DRB1 is the strongest genetic rheumatoid arthritis risk factor for seropositive disease, yet we did not find that HLA-DRB1 risk alleles were associated with a particular CTAP, although there was a trend toward association with CTAP-TB (Extended Data Fig. 8e and Methods).

We did not find a significant association between CTAPs and disease activity score-28 for rheumatoid arthritis with C-reactive protein (DAS28-CRP) or CDAI (Extended Data Fig. 8b), although our patient cohort is not ideal for testing such associations because it only includes patients with high disease activity. CTAPs were also independent of other clinical factors, smoking history and sex, and mostly independent of anatomic category and clinical site (Extended Data Fig. 8b,f–l and Supplementary Table 9). Patients with CTAP-EFM had statistically nonsignificant trends to be older, have longer-standing rheumatoid arthritis and be inadequate responders to TNF inhibitors (Extended Data Fig. 8m–p).

CTAPs have disease-relevant cytokine profiles

We next analysed transcript levels of cytokines, chemokines, and their receptors, recognizing that these transcripts are often sparse in single-cell RNA-seq data (Supplementary Fig. 10). Most cytokines and chemokines are detected predominantly in one cell type, although some key cytokines were produced by multiple cell types (Extended Data Fig. 9a,b). For example, we detected TNF in roughly equal numbers of T cells and myeloid cells, whereas fibroblasts, endothelial cells and B cells dominated among cells with detectable IL6.

Next, we correlated CTAP neighbourhood association scores with the expression of key cytokines and receptors to identify soluble factors produced by CTAP-associated cell states. For example, as predicted, CTAP-TB, enriched for TFH/TPH cell states, had T cell neighbourhood association scores that correlated with expression of the TFH/TPH marker CXCL13 (Fig. 3a and Extended Data Fig. 9c). By contrast, CTAP-TF-associated GZMB+ T and NK cell neighbourhoods had association scores correlating with the expression of IFNG and TNF (Fig. 3a,e and Extended Data Fig. 9c), suggesting that these cytokines may be key molecular drivers of CTAP-TF.

In some CTAPs, this analysis revealed potential cytokine networks. For example, in CTAP-M, myeloid neighbourhood association scores correlated with expression of angiogenic factor VEGFA, whereas endothelial cell neighbourhood association scores correlated with expression of KDR (also known as VEGFR2), potentially explaining the observed enrichment of capillaries in this CTAP (Extended Data Figs. 7l and 9c). By contrast, in CTAP-F, enriched LIFR+ and ICAM1+ venular endothelial cell neighbourhoods expressed high levels of CCL14, whose cognate receptor CCR1 was highly expressed by MERTK+ macrophage neighbourhoods, which are also enriched in CTAP-F (Fig. 4b and Extended Data Fig. 7l and Fig. 9c). Cell–cell communication analysis confirmed these putative interactions (Supplementary Fig. 11).

Our study included three patients with replicate biopsies obtained from the same joint 98 to 190 days after the initial biopsy. Cell-type composition of repeat biopsies was similar to the initial biopsy (permutation P = 0.073) (Supplementary Fig. 12a,b), but more samples are needed to understand how dynamic CTAPs are.

Mapping CTAPs to other patient cohort data

To enable investigation of these and other CTAP-related questions in larger studies, we examined whether samples can be classified into CTAPs using lower-resolution technologies such as flow cytometry and bulk tissue RNA-seq. We first built a nearest-neighbour classifier for flow cytometry data and were able to accurately replicate CITE-seq-based CTAP assignments (accuracy = 87%; Extended Data Fig. 9d, Supplementary Fig. 12c,d and Supplementary Table 10).

We next developed a method to classify CTAPs using bulk RNA-seq data of intact synovial tissue from a recent clinical trial6. CTAP classification based on bulk RNA-seq agreed with the CITE-seq-based CTAP assignment for 6 out of 7 samples in the present study that were also analysed with bulk RNA-seq (Extended Data Fig. 10a).

We applied our CTAP classification algorithm to bulk RNA-seq profiles from the R4RA clinical trial comparing rituximab and tocilizumab for the treatment of patients with rheumatoid arthritis with inadequate response to TNF inhibitor therapy44 (n = 133). The distribution of CTAPs differs between these datasets, probably reflecting differences in cohort recruitment criteria (Extended Data Fig. 10b). As in our cohort, we found no association between CTAP assignment and disease activity or between treatment response and disease activity (Extended Data Fig. 10c,d), supporting our hypothesis that CTAPs reflect distinct inflammatory phenotypes driving arthritis rather than differences in clinical disease activity.

To investigate whether CTAPs change over time, we applied our CTAP classification algorithm to 45 patients from the R4RA trial who had synovial tissue biopsies before and 16 weeks after starting treatment. CTAPs were dynamic during this period, with 30 out of 45 (67%) patients changing to a different CTAP (Fig. 5b and Extended Data Fig. 10e). Patients in the tocilizumab and rituximab treatment arms exhibited similar frequencies of CTAP change (20 out of 29 (69%) and 10 out of 16 (63%) patients, respectively) (Extended Data Fig. 10f–i). Among patients who changed CTAPs, CTAP-F was the most common CTAP at week 16 (16 out of 30 (53%)), consistent with rituximab and tocilizumab targeting inflammatory cells and pathways.

Response to biologic therapy varies by CTAP

To determine whether CTAPs can predict the response to these treatments, we used our algorithm to determine the CTAPs of pre-treatment bulk RNA-seq for R4RA samples (n = 133). We then compared the frequencies of responders (defined as at least 50% improvement in CDAI) versus non-responders among the CTAPs (Extended Data Fig. 10j,k). We found that responses varied by CTAP (P = 0.0105), with CTAP-F having the poorest response to both treatments, even after controlling for covariates (odds ratio = 0.2619, P = 0.0403; Fig. 5c).

CTAP-enriched cell states express risk genes

We next tested whether genes implicated by recent multi-ancestry rheumatoid arthritis genetic studies are preferentially expressed by cell states associated with specific CTAPs45,46. We identified 71 genes that were likely to be causal, all of which were detected in one or more cell types in our dataset (Methods, Supplementary Fig. 13a and Supplementary Table 11).

We identified 48 genes with expression that was significantly positively correlated with CNA loadings for one or more CTAPs for a cell type (P < 0.05, controlling for expression level), indicating that cell states expanded in that CTAP specifically express the rheumatoid arthritis risk gene (Fig. 5d). This is significantly higher than predicted by chance (median = 34, permutation P < 0.01; Supplementary Fig. 13b,c). Some cell types expressed different rheumatoid arthritis genes in different subsets of cells (for example, LEF1 in CTAP-TF-associated naive states and IL6R in CTAP-TB-associated TFH/TPH states). HLA-DRB1 expression was correlated with CTAP-associated cell states in several cell types (Fig. 5d). CTAP-associated rheumatoid arthritis risk genes may also be expressed agnostic of CTAP in a given cell type, such as IL6R in myeloid cells (Supplementary Fig. 13d).

Some genes point to signalling pathways that may be important in a specific CTAP, such as VEGF in CTAP-M (Extended Data Fig. 9c). PRKCH—which encodes protein kinase C (PKC)-η, a mediator of VEGF-induced endothelial cell differentiation47—is highly expressed in endothelial cell states expanded in CTAP-M, which has high expression of VEGF receptor genes KDR and FLT1 among expanded endothelial cell states and VEGFA among expanded myeloid cell states (Fig. 5d and Supplementary Fig. 13e–g).

Discussion

We constructed a comprehensive rheumatoid arthritis synovial tissue reference of more than 314,000 single cells which revealed diverse cellular composition that we characterized into six CTAPs. Previously identified pathogenic cell states in rheumatoid arthritis are expanded in specific CTAPs. For example, CD4+ TFH and TPH cells, which are enriched among T cells in rheumatoid arthritis compared with osteoarthritis11, are present in synovium of all CTAPs but are most expanded in CTAP-TB. Our work also suggests the presence of extra-follicular activation pathways, especially in CTAP-TB, given the rarity of germinal centre dark-zone B cells and abundance of ABCs. Our study also provided more granular insights into previously identified pathogenic cells. For example, inflammatory sublining fibroblast subsets CXCL12+ and CD74hiHLAhi cells were enriched in CTAP-TF and CTAP-M, respectively. MERTK+HBEGF+ and SPP1+ macrophages were also enriched in CTAP-M, probably reflecting different inflammatory axes. These and other instances of co-enriched populations (for example, GZMK+ versus GZMB+CD8+ T and NK cells) inspire new questions about cell–cell interactions underlying inflammatory phenotypes in rheumatoid arthritis and other tissues and diseases.

We found that CTAPs are associated with histologic and serologic (CCP) parameters, in line with studies48 that report increased lymphocyte infiltration (suggesting CTAP-TB, CTAP-TF or CTAP-TM) in CCP-positive synovium compared with CCP-negative synovium. Our finding that CTAP-M, and not CTAP-F or CTAP-EFM, was associated with CCP-negative status warrants further investigation in future studies.

CTAPs can be inferred from single-cell RNA-seq, bulk RNA-seq or flow cytometry data to provide cellular and molecular insights in clinical trials. Even within the more limited clinical diversity of the R4RA cohort44, we found that CTAPs can change over time with treatment, and that CTAP-F was associated with poor clinical response. The dynamic heterogeneity of rheumatoid arthritis synovitis may explain the observation that clinical measures of patients treated with TNF inhibitors do not fall into a bimodal distribution of responders and non-responders49. It is possible that specific CTAPs are more likely to respond to specific therapies that preferentially target infiltrating cell types and relevant pathways. We anticipate that future longitudinal studies will investigate the association of CTAP changes with treatment effects across a larger array of treatments.

The CTAP paradigm provides a tissue classification system that captures coarse cell-type and fine cell-state heterogeneity. This model has the potential to serve as a powerful prototype to classify other types of tissue inflammation, including other immune-mediated diseases. A deeper understanding of the heterogeneity of tissue inflammation in rheumatoid arthritis and other autoimmune diseases may provide new insights into disease pathogenesis and reveal new treatment targets, and key elements of precision medicine.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.