# Spatial mapping of rheumatoid arthritis synovial niches reveals a LYVE1+ macrophage network associated with response to therapy

Julien de Lima, Marie-Astrid Boutet, Olivier Bortolotti, Laure-Agnès Chépeaux, Yaël Glasson, Anne-Sophie Dumé, Rachel Lau, Paul Humbert, Sophie Allain, Adrien Le Pluart, et al.

► To cite this version:

Julien de Lima, Marie-Astrid Boutet, Olivier Bortolotti, Laure-Agnès Chépeaux, Yaël Glasson, et al.. Spatial mapping of rheumatoid arthritis synovial niches reveals a LYVE1+ macrophage network asso- ciated with response to therapy. Annals of the Rheumatic Diseases, 2025, 10.1016/j.ard.2025.07.019 .
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<figure><img src="image_1.png" /></figure>
(TYRO3, AXL, MERTK) receptors. Functionally, LYVE1⁺ macrophages demonstrated a regulatory, anti-inflammatory phenotype in vitro, contrasting with proinflammatory myeloid cells.

**Conclusions:** This study provides an unprecedented spatial and dynamic blueprint of the RA synovium's response to therapy. We identify the LYVE1⁺ macrophage network as a pivotal component of synovial homeostasis and its restoration as a hallmark of clinical remission. These findings unveil novel cellular and molecular targets, paving the way for more active therapeutic strategies.

## WHAT IS ALREADY KNOWN ON THIS TOPIC

* Single-cell transcriptomic analyses have brought unprecedented insights into synovial cell heterogeneity, including macrophages. The proportion of macrophage subtypes varies in response to disease-modifying antirheumatic drugs (DMARD) treatment but their spatial interactions with other synovial cells are understudied.

## WHAT THIS STUDY ADDS

* Using single-cell high-dimensional imaging mass cytometry, we identified that tissue-resident LYVE1⁺ macrophages in perivascular sublining synovial niches were disturbed in active rheumatoid arthritis (RA) but recovered after conventional synthetic DMARD treatment. Several molecular pathways implicated in the restoration of these spatial cell interactions were found to be associated with clinical remission.

## HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

* The combination of transcriptomic and spatial proteomic approaches allows a better elucidation of the interconnections between the stromal microenvironment and the immune cell landscape. This study forms the basis for the development of more targeted, hence more active therapeutic strategies for RA.

# INTRODUCTION

Rheumatoid arthritis (RA) affects 0.3% to 1% of the general population worldwide and is characterised by joint inflammation associated with debilitating bone and cartilage erosions. Despite the availability of a large therapeutic arsenal, including conventional synthetic (cs) disease-modifying antirheumatic drugs (DMARDs; eg, methotrexate, MTX), biologic (b) DMARDs (eg, tumour necrosis factor [TNF] inhibitors) and targeted synthetic (ts) DMARDs (eg, JAK inhibitors), around 40% of patients do not adequately respond to these treatments, and RA remains a major public health challenge [1,2]. RA is a prototypic synovitis-driven autoimmune disease and the in-depth characterisation of the synovium has broadened our understanding of RA pathogenesis and led to the development of targeted therapies. There is currently no predictive biomarker of treatment response; consequently, treatment allocation is based on 'trial and error' [3,4]. Recent evidence supports the concept of integrating synovial cellular/molecular signatures into clinical algorithms to help predict response to specific DMARDs and enhance clinical outcome, towards precision medicine [1,5].

Transcriptomic profiling of dissociated tissues using single-cell RNA sequencing (scRNAseq) approaches has brought unprecedented insights into synovial cell heterogeneity, including fibroblasts, macrophages, T and B cells [6–8]. Importantly, key cellular crosstalk and dysregulated signalling pathways involved in RA pathogenesis have been highlighted. A particular role for tissue-resident macrophages (TRM) expressing *MERTK*, *CD163*, *TREM2*, and *TIMD4* (and *Cx3cr1* in mice) has been

proposed as a protective tight-junction-forming cell layer that secludes the synovium, and avoids the infiltration of inflammatory cells from the joint cavity [7,9]. Recently, LYVE1⁺CX3CR1⁺ TRM were shown to form a sentinel unit around fenestrated capillaries at the lining-sublining interface. In response to systemic immune complex challenge, these macrophages orchestrate neutrophil recruitment and activation of nociceptor neurons [10]. Overall, TRM represent the majority of lining and sublining synovial macrophages in healthy donors and patients with RA in remission, whereas several inflammatory macrophage (IM) subsets, characterised by toll-like receptor activation, interferon signature, alarmin production, or antigen presentation, are enriched in the synovial tissue of patients with active RA [11].

Although these findings have been outstandingly informative, tissue dissociation represents a major limitation inherent to single-cell profiling approaches, as critical cells and spatial information are lost during the sample preparation. Both the stromal microenvironment and immune cell infiltrate spatial function/identity are increasingly recognised as key players of pathogenesis and treatment response in rheumatology, as in oncology [5,12–14]. Recently, spatial transcriptomic revealed that pathogenic and tolerogenic dendritic cells interact with T cells within lining and sublining niches, providing novel opportunities to restore immune homeostasis in RA [15]. However, such single-cell spatial studies remain very sparse in rheumatology.

Here, we performed single-cell high-dimensional imaging mass cytometry (IMC) to spatially analyse and compare the expression of 33 proteins in the synovial tissue from healthy individuals, patients with osteoarthritis (OA) who lack severe inflammatory responses, and matched samples from patients with active early treatment-naïve RA at baseline and at 6 months after starting a csDMARD treatment. IMC analyses were combined with whole-tissue RNA-seq and IF staining validation in large cohorts of RA patients, as well as ligand-receptor investigation of public scRNAseq datasets and functional *in vitro* coculture assays. Overall, this study provides insights into the spatial distribution and the pathogenic connections between synovial niches. It also highlights key pathways relevant for better understanding RA pathogenesis and for the development of novel therapeutic strategies.

# METHODS

All methods are described in the **supplementary methods file**.

## Patient and public involvement statement

Although there was no specific patient and public involvement (PPI) when the study here presented was initially conceived, participants have been subsequently involved in surveys to assess the quality and burden of research visits and interventions. Furthermore, patients recruited in Pathobiology of Early Arthritis Cohort (PEAC) are regularly invited to PPI local events
where they are asked to provide feedback on research themes and questions.

# RESULTS

## Highplexed IMC highlights a high heterogeneity of TRM within the synovial lining and sublining layers

To comprehensively profile the cellular composition and spatial organisation of RA synovium, we took advantage of the various cluster-specific gene markers recently proposed by scRNAseq studies [6,7,16] and developed the workflow presented in Figure 1A. Thirty-three antibodies labelled with unique metal isotopes were validated to identify monocytes and macrophages, including TRM and IM, lymphoid and stromal cells (Fig 1A,B, Supplementary Fig S1A and Supplementary Table S1). Forty high-dimensional images were analysed for a total of 26.85 mm² of synovial tissue from 4 healthy donors, 4 patients with OA exhibiting a low inflammatory profile, and 4 patients with active anticitrullinated protein antibody positive (ACPA⁺) RA (Disease Activity Score—Erythrocyte Sedimentation Rate [DAS28-ESR] ≥ 3.9) at baseline before the initiation of treatment (Supplementary Table S2). For 3 patients with RA, we also included matched posttreatment biopsies at 6 months (Supplementary Fig S1B).

All images were then segmented into 115,411 total cells and an unsupervised clustering approach classified synovial TRM, IM, lymphoid and stromal cells (Fig 1C). By calculating the distance between each cell type from the vascular and lining cells, we obtained a comprehensive spatial cell organisation allowing for the annotation of lining (L-) and sublining (SL-) cell subsets (Fig 1D). IMC subsets were also mapped onto public scRNAseq datasets covering 23,759 synovial cells dissociated from the synovium of patients with RA (n = 11), and similar clusters were identified (Fig 1E,F, Supplementary Fig S2A) [7,17].

Importantly, we distinguished 3 distinct TRM subsets based on their differential expression of TIM4, MERTK, and LYVE1. Lining TRM1 (L-TRM1) were CD68⁺CD163⁺TIM4⁺MERTKlow, whereas L-TRM2 were CD68⁺CD163⁺TIM4⁻MERTKhigh, and both expressed the apoptosis marker cleaved-caspase 3 (Fig 1C and Supplementary Fig S3A), which is presumably linked to their efferocytosis capacity [18]. In line with previously published evidence, scRNAseq confirmed that L-TRM1 expressed higher levels of TIM4 and TREM2 compared to L-TRM2, and both cell subsets expressed CD163 and low levels of MERTK (Fig 1F) [7]. These L-TRM1 and L-TRM2 populations align with the TREM2high and TREM2low macrophages, respectively, identified by Alivernini et al. [7]. In the sublining, SL-TRM3 were positive for CD206 and LYVE1 (Fig 1B-F), corresponding to LYVE1pos macrophages [7] or the recently characterised perivascular TRM in mice and patients with RA [10,19]. Three distinct tissue-infiltrating CD14⁺ IM clusters were defined based on varying expression of the alarmin S100A12 (SL-IM1), CD11c and HLA-DR (IM2, antigen-presenting cells) or osteopontin (SPP1, IM3) (Fig 1C-F). These clusters shared features with the S100A12pos, CLEC10Apos, and SPP1pos macrophages previously reported (Supplementary Fig S2B) [7]. A small proportion of IM3 was also positive for KI67 (Fig 1C and Supplementary Fig S3B), suggesting cell proliferation linked to tissue fibrosis [20]. A fourth IM cluster (L-IM4), characterised by the expression of CCR2, IFI6, and granzyme B expression presented an interferon signature, akin to the ISG15pos macrophage cluster [7]. Regarding stromal cells, L-fibroblasts were defined by the expression of podoplanin (PDPN⁺CD90⁻), whereas SL-fibroblasts exhibited

CD90/THY1 expression (Fig 1B-D), consistent with previous characterisations [16,21,22]. At the transcriptomic level, L-fibroblasts expressed PDPN, PRG4, VCAM, and CD55, whereas SL-fibroblasts were characterised by the expression of THY1, DKK3, and CXCL12 (Supplementary Fig S2A). Due to the close proximity between those cells, both smooth muscle cells (aSMA⁺) and endothelial cells (CD31⁺) were collectively identified as vascular cells (Fig 1B-E). Overall, our results unveiled distinct spatial organisation within the lining and sublining layers of the synovium.

## Synovial tissues from patients with active RA are characterised by a significant disruption of SL-TRM3 cellular network

Following the identification of synovial cell subsets, we compared the stromal and immune cell density across patients' groups. As expected, a significant increase in both lymphoid cell subsets (B, plasma, CD4 and/or CD8 T cells) and L-fibroblast cell density was observed in the synovium of patients with RA as compared to healthy donors (Fig 2A-D and Supplementary Fig S4A) [21]. Inversely, the density of TRM, specifically L-TRM2, was decreased in RA compared to OA synovial tissues (Fig 2A-D and Supplementary Fig S4A), and the IM/TRM ratio was significantly increased in active RA compared to healthy donors (Supplementary Fig S4B). Altogether, we highlighted a concomitant infiltration of lymphoid cells and IM, and a reduction of synovial TRM in patients with active early treatment-naïve RA.

To further characterise the communications between individual synovial cells, a permutation test was performed and, within the 5 nearest cell neighbours ($k = 5$) and a distance threshold of 20 µm, we identified interactions and avoidance behaviours between cell cluster pairs (Fig 2E,F) [23]. In healthy individuals, the main synovial cellular network mostly involved sublining cells, including SL-TRM3, SL-fibroblasts and vascular cells. In OA tissues, the interactions involving SL-TRM3 were reduced whereas the interactions involving L-TRM2 and CD4⁺ T cells were enhanced, especially with L-TRM1 and CD8⁺ T cells (Fig 2E,F). Importantly, in the synovium of patients with early, active, and treatment-naïve RA, several cell-cell interactions were disrupted compared to healthy tissues, especially the ones involving SL-TRM3 (Fig 2E,F). Furthermore, in RA, disease-specific interactions were revealed, especially involving IM, lymphoid cells, and L-fibroblasts (Fig 2E-F). These analyses therefore highlighted that the synovial infiltration at early stages of RA creates a dense cellular network in which the homeostatic social network involving SL-TRM3 is disrupted.

## The definition of spatial multicellular neighbourhoods or niches reveals the dynamic and functional synovial interactions of SL-TRM3

We next explored whether multicellular structural analysis, rather than pairwise cell-cell interactions, would provide further insights into the organisation and dynamic changes of RA synovial tissue. Cells were clustered based on the identity of their neighbours within a radius of 20 µm [24,25], and 9 cellular neighbourhoods (herein called 'niches') were identified, of which 3 were localised in the lining (L1 to L3) and 6 in the sublining (SL1 to 4, ELS1 and 2) (Fig 3A-E). Lining niches were composed of distinct proportions of L-TRM1, L-TRM2, and L-fibroblasts. The 4 sublining niches (SL1-4 niches) were mostly composed of vascular cells, SL-fibroblasts, and SL-TRM3, which was reminiscent of the principal cell network determined in healthy donors (Fig 2E,F). Two additional niches also localised
<figure><img src="image_2.png" /><figcaption>**Figure 1.** A multiplexed imaging mass cytometry (IMC) antibodies panel to characterise the synovial stromal and immune landscape. (A) Schematic representation of the data acquisition workflow used for IMC and single-cell RNA sequencing (scRNAseq). Combined analysis of spatial cell neighbourhoods and ligand-receptor interactions in the different patients' groups. Created with BioRender. (B) Representative pseudocoloured IMC raw images (top panels) and corresponding cluster assignments after cell segmentation (bottom panels) of the synovium samples from healthy individuals (n = 4), patients with osteoarthritis (OA, n = 4), early treatment-naïve rheumatoid arthritis (RA, n = 4) and matched conventional synthetic (cs) disease-modifying antirheumatic drug (DMARD)-treated responder patients with RA (n = 3). Scale bar, 50 µm. (C) Scatter plot representing the mean intensity of each marker (colour code) and proportion of expressing cells (dot size) generated by Phenograph after cell segmentation of the IMC images. IM, inflammatory macrophages; TRM, tissue-resident macrophages; L-, lining-; SL-, sublining-. (D) Distribution of each cell type in the synovium according to the minimum distance calculated to vascular cells and lining cells (L-TRM1 and L-fibroblasts). (E) Uniform Manifold Approximation and Projection (UMAP) plot of the different cell clusters identified from public scRNAseq datasets [7,17] in the synovial tissue of patients with RA with active disease (n = 8, including 5 treatment-naïve patients and 3 DMARDs-resistant patients) or in remission (n = 3, Disease Activity Score [DAS28] < 2.6 for more than 6 months). (F) Scatter plot representing the mean expression of each marker (colour code) and the proportion of cells expressing each marker (dot size) in the different macrophage clusters identified from scRNAseq datasets.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Synovial cell densities and interactions are altered in patients with active treatment-naïve rheumatoid arthritis (RA). (A) t-distributed stochastic neighbor embedding (t-SNE) plot of the different cell subsets represented in the synovial tissue of healthy individuals (n = 4), patients with osteoarthritis (OA, n = 4) and active treatment-naïve rheumatoid arthritis (active RA, n = 4) patients. Macrophages (red), lymphoid (blue), and stromal (green) cell types are encircled. (B,C) Representative pseudocoloured imaging mass cytometry (IMC) images of stromal (aSMA, CD31, PDPN, and CD90) and immune cell markers (CD20, CD138, CD4, CD8, TIM4, LYVE1, and S100A12) of the synovium samples from healthy individuals, patients with osteoarthritis (OA) and active RA (B), and corresponding subset assignments after cell segmentation (C). Scale bar, 50 µm. (D) Cell densities of tissue-resident macrophages (TRM), inflammatory macrophages (IM), lymphoid cells, and stromal cells are presented for patients belonging to the healthy, OA and active RA (act RA) group. One-way analysis of variance (Kruskal-Wallis) with Dunn's correction was performed: $P$ adjusted value < .05 for total TRM and total lymphoid cells; $\#$, adjusted $P$ value < .05 compared with active RA; $*$, adjusted $P$ value < .05 compared with healthy. (E) Heatmaps showing the cell-cell interactions as the sum of the significant values calculated for each patient (sum_sigval) belonging to the healthy (n = 4), OA (n = 4) and active RA (n = 4) group. Brown squares indicate interactions; blue squares indicate avoidance between each cell pair. Interactions implicating SL-TRM3 are highlighted in green. (F) Schematic representation of the pairwise cell interactions, considering cluster densities (circle size) and sum_sigval values (arrow thickness) as calculated in (E). RA-specific interactions compared to both healthy and OA synovium are shown in red, interactions implicating SL-TRM3 cells that were lost in active RA synovium compared to healthy are shown in green.</figcaption></figure>
<figure><img src="image_4.png" /></figure>

**Figure 3.** The spatial organisation of the synovial niches is altered in patients with active treatment-naïve rheumatoid arthritis (RA). (A) Heatmap representing the neighbourhood enrichment score of each cell subset across 10 niches defined in the 15 synovial tissues analysed. Lining niches are highlighted in light blue, sublining niches in purple, and ectopic lymphoid structures (ELS) niches in orange. (B) Representative hematoxylin and eosin (H&E) staining (top panels) and corresponding images of the lining (middle panels) or sublining and ELS (bottom panels) niches in the 3 patients' groups. (C) Niche densities are presented in the patients belonging to the healthy (n = 4), osteoarthritis (OA, n = 4) and active RA (active RA, n = 4) group. One-way analysis of variance (Kruskal-Wallis) with Dunn's correction was performed: *, adjusted *P* value < .05 compared to healthy; #, adjusted *P* value < .05 compared to OA. (D) Ratio of ELS1-2 densities to SL1-3 densities in the 3 patients' groups. (E) Distribution of each niche in the synovium according to the minimum distance calculated to the lining cells (L-TRM1 and L-fibroblasts). (F) Schematic representation of the niches' spatial organisation, considering the niche densities, distances to the lining and their interactions. Basal niche interactions in healthy and OA samples are provided in black and grey, additional interactions between niches observed specifically in active RA are shown in red. (G) Schematic representation of the cell type composition and proportions in each niche. Created with BioRender. Representative images of the cell subsets in each niche are shown as insets.

in the sublining compartment were composed of lymphoid cells and IM tightly packed together, forming ectopic lymphoid structures (ELS) [5,26,27]. While the ELS1 niche was mostly composed of lymphoid cells, namely B cells, CD4 and CD8 T cells,

the ELS2 niche included B cells, plasma cells, but also the 3 IM subtypes (Fig 3A). Representative images of the niches localisation are shown in Figure 3B. Importantly, ELS1, ELS2, and L3 niches were significantly more abundant in active RA samples,
compared to healthy donors or patients with OA (Fig 3C and Supplementary Fig S5A). According to the changes in density of SL1, SL2, and SL3 niches, the ELS1-2/SL1-3 ratio was significantly increased in active RA compared to healthy donors (Fig 3D).

While each niche can represent the site of unique and local cellular interactions, distinct niches may also interact to support additional biological events [25]. Spatial niche interaction graphs were generated for each patient group (Supplementary Fig S6A) and provided a comprehensive locoregional niches map based on the strength of the interactions between niches, their density, and their precise tissue location (Fig 3E-G). In healthy donors, the main interactions were observed between L1, L2, SL1, SL2, SL3, and SL4 niches. Of interest, the cells in these niches (TRM, fibroblasts and vascular cells) are considered tissue resident or structural cells, thus highlighting the features of a homeostatic synovial interaction map. In OA synovium, a similar cartography was observed but a central position of the ELS1 niche, interacting with L2, SL2, and SL4 niches, was underlined (Fig 3F). In patients with active RA, the L3, ELS1, and ELS2 synovial niches composed of all infiltrating proinflammatory immune cells and L-fibroblasts, established novel interactions between them and with the homeostatic niches, therefore highlighting profound alterations compared to the homeostatic picture (Fig 3F). Furthermore, in the RA cellular interactive map, the loss of L1 and SL3 niches involvement, as well as the disruption of specific interactions, such as L2 with SL2 or with SL4, were observed (Fig 3F,G). Altogether, these results indicated that the cellular infiltration characterising the RA synovial tissue has a profound impact on the whole synovial architecture and the underlying cellular communications. As observed at the individual cell level (Fig 2), these results notably confirmed that the perivascular SL3 niche, mainly composed of SL-TRM3, was profoundly altered in patients with early active and treatment-naïve RA.

### SL-TRM3 are reestablished following efficient csDMARD treatment

To study the impact of csDMARD treatment on the altered synovial cellular network observed in patients with active RA, we next performed paired analyses of patients with matched RA (n = 3) before therapeutic intervention and 6 months following efficient csDMARD treatment with MTX and sulfasalazine (SZP). The density of SL-TRM3 was significantly increased following treatment as assessed by IMC (Fig 4A and Supplementary Fig S4C). Whole-tissue RNA-seq performed on the tissues analysed by IMC confirmed that the expression of *LYVE1* was significantly increased in synovial biopsies posttreatment (n = 3, Fig 4B). Importantly, the upregulation of *LYVE1* was further validated in a larger cohort of csDMARD (MTX, SZP or hydroxychloroquine) responder lympho-myeloid and ACPA⁺ patients from the PEAC (n = 11 paired baseline and 6-month samples, Fig 4C) [26,28]. Noteworthy, this increase of synovial *LYVE1* expression was not observed in poor-responder patients (Fig 4C). Similar results were observed when including patients with other pathotypes and ACPA⁻ status (n = 54 paired samples, Supplementary Fig S7A). Baseline *LYVE1* expression was consistently lower in synovial biopsies with a lympho-myeloid pathotype compared to dif-fuse-myeloid or pauci-immune pathotypes, and it correlated negatively with disease activity and other clinic-radiographic parameters (Supplementary Fig S7B, C).

Six months following efficient MTX/SZP treatment, the cellular interactions involving SL-TRM3 cells were partially

reestablished, for example, with L-fibroblasts, L-TRM2 and vascular cells (Fig 4D,E). In addition, MTX/SZP treatment induced novel interactions, such as between SL-TRM3 and plasma cells, whereas preexisting interactions persisted posttreatment, such as with SL-fibroblasts and CD4 T cells (Fig 4D,E). These cellular interactions were visualised on raw images of LYVE⁺ SL-TRM3 in their tissue microenvironment and their corresponding spatial graphs, highlighting clear proximities between SL-TRM3, SL-fibroblasts, vascular cells and T cells, especially in patients with RA 6 months following MTX/SZP treatment (Fig 4F,G). Interestingly, upon MTX/SZP treatment and along with the increase of SL1 and SL3 niches proportion (Fig 4H,I and Supplementary Fig S5B), SL3 reestablished significant connections with SL1 and SL4 (Fig 4I,J and Supplementary Fig S6B), as observed in healthy conditions (Fig 3F). Overall, good response to MTX/SZP therapy was associated with a partial recovery of the basal healthy synovial cartography, with a particular involvement of SL-TRM3.

### scRNAseq ligand-receptor analysis underlines SL-TRM3 cellular pathways linked to disease activity

To further elucidate the pathways governing SL-TRM3 communications within the synovial niches in relation to disease activity, we next performed IF staining of SL-TRM3 (sublining CD68⁺, MERTK⁺, TIM4⁻ cells). Synovia were collected from an independent cohort of patients with established ACPA⁺ RA with active disease (DAS28 > 2.6, n = 13) or in remission (DAS28 < 2.6, n = 7) following cs- and/or b-DMARD treatments (Supplementary Table S2). A significantly higher SL-TRM3 density was evident in patients with RA in remission compared to those with active RA (Fig 5A). This pattern held true when including patients with ACPA⁻ RA or when restricting the analysis to patients with RA treated solely with csDMARDs (Supplementary Fig S7D, E).

We further analysed public scRNAseq datasets from synovial cells dissociated from the synovium of patients with ACPA⁺ active (n = 8) and remission (n = 3) RA introduced previously (Fig 1E,F, Supplementary Table S2) [7,17]. Several ligand-receptor interactions involving SL-TRM3 were induced in the synovial tissue of patients with RA in remission when compared to active RA (Fig 5B,C and Supplementary Fig S8). Of note, SL-TRM3 from patients with RA in remission were found to display enhanced response to chemokines (CXCL12, macrophage migration inhibitory factor [MIF]), annexins (ANXA1), amyloid-beta precursor protein (APP), and colony stimulating factor 1 (CSF1); these ligands being expressed by the main SL-TRM3 neighbours highlighted above (Fig 4), such as L- and SL-fibroblasts, vascular cells, plasma cells and CD4 T cells (Fig 5B). Inversely, SL-TRM3 from patients with active RA were highly stimulated by collagens and fibronectin (FNI) (Supplementary Fig S9A).

Conversely, SL-TRM3 from patients with RA in remission were major producers of chemokines (CCL2, CCL3, CXCL12, CXCL16), TNF family members (TNFSF12, TNFSF13B), Growth Arrest Specific 6 (GAS6), and C-type lectin domain family 2 member B (CLEC2B), with potent effects on their main neighbours such as L-TRM2, CD4 T cells, plasma cells, vascular cells and fibroblasts (Fig 5C). Of note, SL-TRM3 from patients with active RA were characterised by the production of fibronectin and alarmin (S100A8) (Supplementary Fig S9B).

Differential expression of selected ligands and receptors by SL-TRM3 from active versus remission RA was confirmed in a larger cohort of patients (Fig 5D and Supplementary S9C). A majority of genes upregulated in remission RA SL-TRM3 were
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Partial recovery of the synovial architecture following efficient conventional synthetic disease-modifying antirheumatic drug (csDMARD) treatment. (A) Synovium samples were obtained from patients with active treatment-naïve rheumatoid arthritis (RA) at baseline (n = 3) and 6 months following efficient treatment with csDMARD (methotrexate [MTX]/sulfasalazine [SZP]) from the same patients (n = 3). Paired analysis of SL-TRM3 densities in patients with RA at baseline and 6 months post-csDMARD treatment was performed on the imaging mass cytometry (IMC) images. Paired Student's t-tests were performed. (B, C) Paired plots for baseline and 6-month LYVE1 bulk RNA-seq counts in the 3 patients analysed previously (B) and in all lympho-myeloid and anticitrullinated protein antibody positive (ACPA⁺) patients who achieved poor or good clinical responses to csDMARD treatment (C) (n = 21, delta Disease Activity Score—Erythrocyte Sedimentation Rate [DAS28-ESR] ≥ 2.8) in the Pathobiology of Early Arthritis Cohort (PEAC). Paired Student's t-tests were performed in (B). In (C), differences between LYVE1 bulk RNA-seq counts in pretreatment and posttreatment samples were assessed using a negative binomial generalised linear mixed effects model with time and response as fixed effects and patient as random effects (*p*Time × response). Coloured points show regression line of fitted mixed-effect model (estimated marginal means), with error bars showing 95% CIs (fixed effects). Grey points and lines show raw paired count data. (D) Heatmaps generated using the IMC images and showing the cell-cell interactions as the sum of the significant values calculated for each patient (sum_sigval) belonging to the active RA at baseline (n = 3) and 6 months post-csDMARD treatment (n = 3) group. Brown squares indicate interactions; blue squares indicate avoidance between each cell pair. Inter-actions implicating SL-TRM3 are highlighted in green. (E) Schematic representation of the pairwise cell interactions, considering cluster densities (circle size) and sum_sigval values (arrow thickness) as calculated in (D). Interactions implicating SL-TRM3 that were induced 6 months post-csDMARD treatment compared to baseline are shown in green. (F) Representative IMC raw images in the synovium samples from healthy individuals (n = 4)</figcaption></figure>
highly expressed in their healthy/OA counterparts, indicating
these genes were diminished in active RA and recovered upon
remission (Supplementary Fig S9D). Pathway analyses of the
top 200 upregulated genes in SL-TRM3 from remission RA ver-
sus active RA revealed distinct biological processes related to
antigen processing and presentation (MHC-II genes, CD74), reg-
ulation of TNF production (GAS6, TNFAIP3) and negative regu-
lation of immune/inflammatory response (TREM2, CD46),
suggesting an important role in the response to treatment
(Fig 5E). Inversely, in active RA, SL-TRM3 were characterised
by high expression of genes implicated in the recognition of
pathogens (viral process, CD209 and MRC1 genes) and purine
metabolic process (PGK1, NDUFB3), suggesting an active role in
immune response (Supplementary Fig S9E). Genes upregulated
in remission RA versus healthy/OA SL-TRM3 were involved in
antigen processing, regulation of memory T cell and germinal
centre formation, suggesting an adaptive immune regulatory
role (Supplementary Fig S9F). Furthermore, genes upregulated
in both healthy/OA and remission RA versus active RA SL-
TRM3 implicated regulation of the immune system and response
to corticosteroid (TGFB1, FOS, FOXO3), suggesting recovery of a
protective phenotype in remission.

To experimentally probe the functional attributes of these
synovial macrophage subsets, we generated LYVE1<sup>+</sup>
MERTK<sup>+</sup>HLA-DR<sup>low</sup> (SL-TRM3 surrogate) and LYVE1<sup>-</sup>
MERTK<sup>-</sup>HLADR<sup>high</sup> (IM surrogate) macrophages for coculture
with RA patient-derived fibroblast-like synoviocytes (RA-FLS)
(Fig 5F,G). Upon Lipopolysaccharide (LPS) stimulation, LYVE1<sup>+</sup>
macrophages failed to induce IL-6 secretion by RA-FLS, in stark
contrast to LYVE1<sup>-</sup> macrophages, which promoted IL-6 via
IL1A/B release (inhibited by IL-1R antagonist) (Fig 5H,I). This
functional distinction, demonstrating a dampened proinflamma-
tory capacity for LYVE1<sup>+</sup> macrophages, complements our spa-
tial mapping data and their remission-associated transcriptomic
profile.

Altogether, our findings underscore a distinct negative regu-
latory role for LYVE1<sup>+</sup>/SL-TRM3 in modulating inflammatory
responses, sharply contrasting with proinflammatory HLADR-
high/IM macrophages. The differential cellular interactions and
pathways involving SL-TRM3 in active versus remission RA are
schematically summarised in Figure 6.

DISCUSSION

Here, we provided a comprehensive synovial cell and niche
social network mapping by performing multiplex, single-cell
spatial analysis of the human synovial tissue using IMC. This
approach allowed us to decipher the spatial organisation of the
main tissue resident/homeostatic or infiltrating/inflammatory
myeloid, lymphoid, and stromal cells. We highlighted the pres-
ence of 7 macrophage subsets, for which the spatial features and
expression profile expanded the diversity described so far in the
literature [6–8,11,29]. We then identified the presence of 9 rel-
evant synovial niches, of which 3 were localised in the lining

(L1 to L3) and 6 in the sublining (SL1 to 4, ELS1 and 2), provid-
ing a niche map similar to the one recently obtained using spa-
tial transcriptomic analyses [15].

We first underlined that 2 distinct TRM subsets can be found
in the lining layer, depending on their expression of TIM4 and
MERTK. Whether L-TRM2 represents the proinflammatory state
of L-TRM1 that lost barrier and homeostatic functions or origi-
nates from a different precursor now deserves further studies
[7,11]. We also observed that lining fibroblasts expressing podo-
planin (PDPN) were expanded in RA and mainly localised in the
L3 niche, where, as previously described in the literature
[16,21,29], they assume a central position by connecting with
lining L-TRM1 cells but also with sublining cells and niches.
This expansion matched with a reduction of the density and fre-
quency of the L1 niche mainly composed of L-TRM1 cells in RA,
confirming previous studies that demonstrated a significant dis-
ruption of the lining myeloid barrier that physically secludes the
joint in homeostatic conditions [4,7,9,11].

Within the sublining compartment, different subsets of IM
and lymphoid cells were shown to communicate together and
form 2 distinct ELS niches. Such ELS organisation recapitulates
the architecture of secondary lymphoid organs [30–32]. The
ELS1 niche corresponded to a T cell-rich zone, whereas the ELS2
niche resembled the B cell-rich area. In the context of active RA
and before any treatment intervention, both ELS niches were
also strongly connected with resident sublining fibroblasts
(PDPN-CD90+) near vascular vessels belonging to the SL1 and
SL4 niches, consistent with the described presence of activated
stromal cells and high endothelial venules, respectively [30–32].
Importantly, we also highlighted the presence of different
subtypes of IM within the ELS2 niche. Although analysing the
expression of additional markers would be necessary to further
characterise ELS cells, our data clearly indicate that several
inflammatory myeloid cells with potent roles on monocyte and
fibroblast activation (IM1, S100A12+), antigen presentation
(IM2, HLA-DR+), neutrophil activation and matrix remodelling
(IM3, SPP1+)[6,7,11,15,33–35] are localised in closed proxim-
ity to the cells involved in ELS functions and enriched in active
RA.

Despite the availability of a large variety of cs-, b-, and ts-
DMARDs approved for the treatment of RA, 40% of patients still
do not respond to treatment, understanding molecular signa-
tures underlying the variety of clinical and treatment-response
phenotypes remains one of the major areas of research in rheu-
matology. Here, we focused our analyses on patients with RA
who, 6 months following MTX/SZP treatment, presented a good
or moderate response, in order to decipher the complex biologi-
cal mechanisms driving the suppression of inflammation. We
demonstrated that a key cellular social network involved in the
homeostasis of healthy synovium comprised SL-TRM3, SL-fibro-
blasts, and vascular cells in SL1 to 4 niches, while, in active RA,
the paucity of SL-TRM3 cells induced profound changes in the
SL1 and SL3 organisation. Similarly, recent work highlighted
that LYVE1⁺ macrophages are significantly associated with

and patients with RA at baseline (n = 3) and 6 months post-csDMARD treatment (n = 3) showing SL-fibroblasts (CD90+), vascular cells (aSMA+ or CD31+), SL-TRM3 (LYVE1+), T cells (CD4+ or CD8+), SL-IM1 (S100A12+), and IM2 (CD11C+). (G) Corresponding spatial graphs presenting the interactions (grey nodes) between SL-fibroblasts (red), vascular cells (purple), SL-TRM3 (green), T (orange), SL-IM1 and IM2 (pink) in the 3 patients' groups. (H) Paired analysis of SL3 niche densities in patients with RA at baseline and 6 months post-csDMARD treatment. Paired Student's t-tests were performed. (I) Representative hematoxylin and eosin (H&E) staining (top panels) and corresponding images of the sublining and ectopic lymphoid structures (ELS) (bottom panels) niches in the synovium of patients with RA at baseline and 6 months post-csDMARD treatment. (J) Schematic representation of the niches' spatial organisation, considering the niche densities, distances to the lining and their interactions. Basal niche interactions previously observed in healthy and osteoarthritis (OA) samples are provided in black and grey; additional interactions between niches observed specifically in RA samples are shown in red (refers to Fig 3F).
<figure><img src="image_6.png" /></figure>

**Figure 5.** scRNAseq ligand-receptor analysis underlines the existence of cellular pathways involving SL-TRM3 and linked to disease activity. (A) Synovium samples were obtained from patients with active established anticitrullinated protein antibody positive (ACPA⁺) rheumatoid arthritis (RA) (n = 13, Disease Activity Score [DAS28] > 2.6) and from patients under remission (n = 7, DAS28 < 2.6). IF staining was performed to determine SL-TRM3 (sublining CD68⁺ MERTK⁺ TIM4⁻) cell density. Mann-Whitney U tests were performed. Representative images are shown. (B, C) Public scRNAseq datasets [7,17] were used to analyse the potent ligand-receptor interactions between SL-TRM3 and other synovial cells in the synovium of patients with ACPA⁺ RA with active disease (n = 8, comprising 5 treatment-naïve patients and 3 disease-modifying antirheumatic drugs (DMARDs)-resistant patients) or in remission (n = 3, DAS28 < 2.6 for more than 6 months). Chord diagrams of all upregulated incoming and outgoing interactions (except MHC interactions) involving SL-TRM3 in remission RA versus active RA samples. Sender cells are presented at the start of the arrows and receiver cells at the end. Main interacting neighbours described in Fig 4E are highlighted in bold, and their interactions with SL-TRM3 are presented with bordered arrows. (D, E) Public scRNAseq datasets [7,17,47] were used to analyse differentially expressed genes in SL-TRM3 from patients with anticitrullinated protein antibody positive (ACPA⁺) RA with active disease (n = 13, comprising 6 treatment-naïve patients and 7 DMARDs-resistant patients) versus in remission (n = 8, DAS28 < 2.6 for more than 6 months). Gene expression in SL-TRM3 from healthy controls (n = 6) and patients with OA (n = 4) is also shown. Scatter plot representing the mean expression of selected upregulated genes in remission RA (colour code) and proportion of expressing SL-TRM3 (dot size) (D). Plot showing Gene Ontology (GO) biological pathways identified with STRING-db using upregulated genes in SL-TRM3 in remission RA versus active RA (E). (F) Fibroblast-like synoviocytes (RA-FLS) from active patients with RA (n = 4) were cocultured
<figure><img src="image_7.png" /><figcaption>**Figure 6.** Proposed model of SL-TRM3 cell interactions in rheumatoid arthritis (RA). Schematic representation of the differential cell-cell communications and implicated molecular pathways established by SL-TRM3 in the synovium of active versus patients with RA in remission. Pathways implicating the main SL-TRM3 neighbours are highlighted in higher font and arrow size. Communications between IM and fibroblasts are presented in orange. Location of the cells in their respective niches is also provided. Created with BioRender.</figcaption></figure>

specific cell-type abundance phenotypes enriched for fibroblasts, endothelial, and myeloid cells in a large cohort of patients with RA [8]. Importantly, MTX/SZP treatment was able to induce a partial recovery of the basal homeostatic synovial cartography allowing, for example, a partial restoration of SL-TRM3, and the re-establishment of interactions with TRM, lymphoid and stromal cells similar to the healthy or OA situation. Similarly, we observed that LYVE1 expression was enhanced after csDMARD treatment, especially in responder patients with RA, and that SL-TRM3 cells were also more abundant in patients with RA in remission. Perivascular SL-TRM3 cells are positive for the hyaluronan receptor LYVE1 and the mannose receptor CD206, and are thought to control homeostatic collagen production, leukocyte infiltration, and fibrosis [10,11,36,37]. More recently, LYVE1<sup>+</sup> macrophages have been shown to restrain the inflammatory and fibrotic phenotype of shoulder capsular fibroblasts through upregulation of genes and pathways involved in extracellular matrix organisation and remodelling [38], confirming their beneficial functions.

We further dissected the molecular pathways involved in SL-TRM3 communications by combining IMC with public scRNA-seq datasets analyses, and explored a large number of cell-cell communications based on ligand-receptor interactions. In the synovial tissue of patients with RA in remission, SL-TRM3 could be recruited and regulated by chemokines, anti-inflammatory mediators, and macrophage growth factors such as MIF, CXCL12, ANXA1, and CSF1 [39,40]. They are themselves characterised by a negative regulation of immune and inflammatory responses and could influence other immune and stromal cells through chemokines (eg, CCL2, CXCL16), TNF family members (TNFSF12 and 13B), and CLEC2B [41,42]. Importantly, upon efficient treatment, SL-TRM3 expressed high levels of GAS6

ligand to stimulate stromal cells and macrophages upon binding to its receptors, AXL and MERTK. TAM (TYRO3, AXL, and MERTK) receptors have been shown to negatively regulate the inflammatory cascade involved in RA pathophysiology and mediate phagocytosis of apoptotic cells [7,28,43]. Our *in vitro* coculture assays provide direct functional support for this regulatory role, demonstrating that LYVE1<sup>+</sup>/SL-TRM3 surrogate macrophages, unlike their LYVE1<sup>-</sup> counterparts, exhibit a dampened proinflammatory capacity by failing to induce significant IL-1B/IL-6 production. These collective findings strongly support the development of TAM-targeted therapeutic strategies to foster inflammation resolution in arthritis [7,11,28,44–46]. Although further *in vitro* validation is warranted, our combined analyses provide novel insights into the complex interplay governing synovial homeostasis and therapeutic response.

Our findings must be considered in the context of some limitations. While our high-dimensional IMC approach provides unprecedented cellular resolution, we recognise the inherent heterogeneity of synovitis in RA. To address this challenge, our initial discovery phase focused on a limited number of well-defined, homogeneous early patients with RA, treatment-naïve, ACPA-positive, and exhibiting a lympho-myeloid pathotype, initiating MTX/SZP therapy. Key findings were subsequently validated in larger, albeit more heterogeneous, cohorts (eg, the PEAC for bulk RNA-seq and an independent cohort of established RA for IF), following a standard translational research strategy. Future studies should aim to confirm whether similar molecular perturbations are present in tissues with diverse histopathological profiles (eg pauci-immune and diffuse-myeloid) [5] or in patients with differing ACPA status. Nevertheless, in accordance with our IMC results from MTX/SZP-treated patients, our IF staining demonstrated that SL-TRM3 density was also associated with disease activity in a broader cohort of patients with established RA treated with various cs- and b-DMARDs. While this supports the robustness of our findings across disease stages and treatment regimens, future studies involving larger, homogeneously treated cohorts will be valuable to dissect drug-specific effects from the more general synovial remodelling associated with remission.

In summary, our study provides a comprehensive, spatially resolved characterisation of the synovial microenvironment across healthy individuals, patients with active RA, and those receiving csDMARD therapy. While primarily descriptive, this approach was deliberately chosen to address key gaps in our understanding of in situ cellular dynamics within the RA synovium, particularly how these are modulated by treatment. Given the urgent need for predictive biomarkers and more precisely targeted therapies in RA, a foundational understanding of the cellular and structural changes associated with therapeutic response is essential. In this context, our detailed mapping of the LYVE1<sup>+</sup> macrophage network and its niche remodelling during MTX/SZP-induced remission represents an important step. By capturing these spatial alterations, our findings offer new insights into the cellular mechanisms underlying synovial homeostasis and generate testable hypotheses for future mechanistic and interventional studies.

with LYVE<sup>+</sup> and LYVE<sup>-</sup> macrophages generated from blood monocytes of a healthy donor. (G, H) Macrophages were characterised by flow cytometry and Enzyme-Linked Immunosorbent Assay (ELISA) for their expression of HLA-DR, LYVE1, MERTK, and IL1B. (I) Following Lipopolysaccharide (LPS) stimulation (100 ng/ml), LYVE<sup>-</sup> macrophages induced the secretion of IL6 by RA-FLS through IL1B (inhibited by the IL1 receptor antagonist IL1RA), whereas LYVE1<sup>+</sup> macrophages did not. One representative experiment of 4 independent experiments with FLS from 4 different patients with RA is shown. Mann-Whitney U tests were performed: *, P < .05.
## Competing interests

FB reports financial support was provided by French National Research Agency. FB reports financial support was provided by Novartis-Société Française de Rhumatologie. CP reports financial support was provided by UK Research and Innovation Medical Research Council. CP reports financial support was provided by Versus Arthritis. Boutet reports financial support was provided by Foundation for Medical Research. FA reports financial support was provided by French National Research Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## CRediT authorship contribution statement

**Julien De Lima:** Writing – review & editing, Visualization, Methodology, Data curation, Writing – original draft, Validation, Formal analysis. **Marie-Astrid Boutet:** Writing – original draft, Formal analysis, Writing – review & editing, Methodology, Conceptualization. **Olivier Bortolotti:** Methodology, Data curation, Formal analysis. **Laure-Agnès Chépeaux:** Investigation, Methodology, Formal analysis. **Yaël Glasson:** Methodology, Formal analysis, Validation, Investigation. **Anne-Sophie Dumé:** Methodology, Formal analysis, Investigation. **Rachel Lau:** Formal analysis, Data curation. **Paul Humbert:** Investigation, Methodology, Formal analysis. **Sophie Allain:** Methodology, Formal analysis, Investigation. **Adrien Le Pluart:** Formal analysis, Investigation, Data curation. **Alessandra Nerviani:** Resources, Conceptualization, Methodology. **Liliane Fossati-Jimack:** Methodology, Resources, Conceptualization. **Henri-Alexandre Michaud:** Writing – review & editing, Resources, Conceptualization, Software, Methodology. **Jérôme Guicheux:** Writing – original draft, Writing – review & editing, Supervision. **Benoit Le Goff:** Validation, Resources, Writing – review & editing, Supervision. **Myles J. Lewis:** Methodology, Data curation, Writing – review & editing, Formal analysis. **Costantino Pitzalis:** Validation, Project administration, Conceptualization, Writing – review & editing, Resources, Funding acquisition. **Gabriel Courties:** Writing – review & editing, Methodology, Conceptualization, Validation, Data curation. **Florence Apparailly:** Writing – original draft, Supervision, Funding acquisition, Writing – review & editing, Validation, Project administration, Conceptualization. **Frederic Blanchard:** Writing – original draft, Validation, Resources, Methodology, Formal analysis, Conceptualization, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition, Data curation.

## Acknowledgements

We acknowledge S. Nedellec and P. Paul-Gilloteaux from the MicroPICell core facility (SFR Bonamy, BioCore, Inserm UMS 016, CNRS UAR 3556, Nantes, France), member of the Scientific Interest Group (GIS) Biogenouest, IBISA, and the national infrastructure France-Bioimaging supported by the French National Research Agency (ANR-10-INBS-04) for advice on IF and confocal microscopy, the ‘Institut de la Main Nantes Atlantique’ (L. Ardouin), and the department of orthopedic surgery of the Nantes University Hospital (D. Waast) for providing biopsies. The authors also acknowledge the SC3M platform from the Inserm/NU/ONIRIS UMR1229 RMeS Laboratory and SFR François Bonamy-UMS 016 for their help in managing bio-

informatic datasets, biopsies, and prepare slides (J. Veziers, Y. Le Guennec, and M. Dutilleul). This work was presented at the European Workshop of Rheumatology Research (EWRR) in 2024 and Symposium of the Network on Monocytes-Macrophages (SoMM) in 2023.

## Funding

The funding for this project was provided by French « Agence Nationale de la Recherche » (ANR, grant SyMAC ANR-18-CE14-0042), Novartis-Société Française de Rhumatologie (SFR, Dreamer grant), Institut National de la Santé et de la Recherche Médicale (Inserm) and Nantes University. OB was funded by Montpellier University, and ALP was a recipient from the Nantes University Hospital internship. M-AB was funded by the « Fondation pour la recherche médicale » (grant number ARF202004011786) and by the Inserm « ATIP-Avenir » program. AN was funded by « Versus Arthritis » Clinical Lectureship in Experimental Medicine and Rheumatology (grant number 21890). Medical Research Council (MRC) support has been obtained for the development of the PEAC (grant number 36661 to CP) and « Versus Arthritis » has supported the Experimental Arthritis Treatment Centre (grant number 20222 to CP). Finally, we acknowledge the support of Immun4Cure IHU "Institute for Innovative Immunotherapies in Autoimmune diseases" (France 2030/ANR).

## Patient consent for publication

Not applicable.

## Ethics approval

All procedures related to RA biopsies for IMC analysis were performed following written informed consent and were approved by the hospital's ethics committee (REC 05/Q0703/198). Other RA, OA and control biopsies were obtained following written patient's informed consent and ethics approval was granted by the Nantes University Hospital ethics committee (DC-2011-1399).

## Provenance and peer review

Not commissioned; externally peer reviewed.

## Supplementary materials

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.ard.2025.07.019.

## Orcid

**Julien De Lima:** http://orcid.org/0000-0002-0191-9479
**Olivier Bortolotti:** http://orcid.org/0000-0002-3669-8215
**Yaël Glasson:** http://orcid.org/0000-0002-5072-7078
**Rachel Lau:** http://orcid.org/0000-0002-8330-8048
**Paul Humbert:** http://orcid.org/0000-0003-1208-5550
**Adrien Le Pluart:** http://orcid.org/0000-0002-4513-6054
**Liliane Fossati-Jimack:** http://orcid.org/0000-0003-3757-3999
**Gabriel Courties:** http://orcid.org/0000-0002-0003-7407
**Frederic Blanchard:** http://orcid.org/0000-0003-1055-2573
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