Original Article

Open Access

Characteristics of PD-1⁺CD4⁺ T cells in peripheral blood and synovium of rheumatoid arthritis patients

Yan-juan Chen

orcid.org/0000-0002-9371-0313

Department of Rheumatology and Immunology, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, China

Integrated Chinese and Western Medicine Postdoctoral Research Station, Jinan University, Guangzhou, China

Contribution: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing - original draft, Writing - review & editing

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Yong Chen,

Yong Chen

orcid.org/0000-0001-5538-5081

Department of Rheumatology and Immunology, Affiliated Hospital of Zunyi Medical University, Zunyi, China

Contribution: Investigation, Methodology, Resources, Software, Visualization, Writing - review & editing

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Ping Chen,

Ping Chen

Department of Rheumatology and Immunology, Shenzhen People's Hospital, Shenzhen, China

Contribution: Methodology, Resources

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Yi-qun Jia,

Yi-qun Jia

Stomatology Center, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, China

Contribution: Writing - review & editing

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Hua Wang,

Corresponding Author

Hua Wang

[email protected]

Department of Orthopaedics, The Second Clinical Medical College of Jinan University, The First Afiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China

Correspondence

Hua Wang, Department of Orthopaedics, The Second Clinical Medical College of Jinan University, The First Afiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, No. 1017 Dongmen North Road, Luohu District, Shenzhen 518020, China.

E-mail: [email protected]

Xiao-ping Hong, Department of Rheumatology and Immunology, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, No.1017 Dongmen North Road, Luohu District, Shenzhen 518020, China.

E-mail: [email protected]

Contribution: Funding acquisition, Supervision, Writing - review & editing

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Xiao-ping Hong,

Corresponding Author

Xiao-ping Hong

[email protected]

Department of Rheumatology and Immunology, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, China

Correspondence

E-mail: [email protected]

Contribution: Funding acquisition, Supervision, Writing - review & editing

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Yan-juan Chen,

Yan-juan Chen

orcid.org/0000-0002-9371-0313

Department of Rheumatology and Immunology, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, China

Integrated Chinese and Western Medicine Postdoctoral Research Station, Jinan University, Guangzhou, China

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Yong Chen,

Yong Chen

orcid.org/0000-0001-5538-5081

Department of Rheumatology and Immunology, Affiliated Hospital of Zunyi Medical University, Zunyi, China

Contribution: Investigation, Methodology, Resources, Software, Visualization, Writing - review & editing

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Ping Chen,

Ping Chen

Department of Rheumatology and Immunology, Shenzhen People's Hospital, Shenzhen, China

Contribution: Methodology, Resources

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Yi-qun Jia,

Yi-qun Jia

Stomatology Center, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, China

Contribution: Writing - review & editing

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Hua Wang,

Corresponding Author

Hua Wang

[email protected]

Correspondence

E-mail: [email protected]

Contribution: Funding acquisition, Supervision, Writing - review & editing

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Xiao-ping Hong,

Corresponding Author

Xiao-ping Hong

[email protected]

Correspondence

E-mail: [email protected]

Contribution: Funding acquisition, Supervision, Writing - review & editing

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First published: 27 September 2024

https://doi.org/10.1002/cti2.70006

Citations: 2

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Abstract

Objectives

PD-1 plays a crucial role in the immune dysregulation of rheumatoid arthritis (RA), but the specific characteristics of PD-1⁺CD4⁺ T cells remain unclear and require further investigation.

Methods

Circulating PD-1⁺CD4⁺ T cells from RA patients were analysed using flow cytometry. Plasma levels of soluble PD-1 (sPD-1) were measured using enzyme-linked immunosorbent assay (ELISA). Single-cell RNA sequence data from peripheral blood mononuclear cells (PBMCs) and synovial tissue of patients were obtained from the GEO and the ImmPort databases. Bioinformatics analyses were performed in the R studio to characterise PD-1⁺CD4⁺ T cells. Expression of CCR7, KLF2 and IL32 in PD-1⁺CD4⁺ T cells was validated by flow cytometry.

Results

RA patients showed an elevated proportion of PD-1⁺CD4⁺ T cells in peripheral blood, along with increased plasma sPD-1 levels, which positively correlated with TNF-α and erythrocyte sedimentation rate. Bioinformatic analysis revealed PD-1 expression on CCR7⁺CD4⁺ T cells in PBMCs, and on both CCR7⁺CD4⁺ T cells and CXCL13⁺CD4⁺ T cells in RA synovium. PD-1 was co-expressed with CCR7, KLF2, and IL32 in peripheral CD4⁺ T cells. In synovium, PD-1⁺CCR7⁺CD4⁺ T cells had higher expression of TNF and LCP2, while PD-1⁺CXCL13⁺CD4⁺ T cells showed elevated levels of ARID5A and DUSP2. PD-1⁺CD4⁺ T cells in synovium also appeared to interact with B cells and fibroblasts through BTLA and TNFSF signalling pathways.

Conclusion

This study highlights the increased proportion of PD-1⁺CD4⁺ T cells and elevated sPD-1 levels in RA. The transcriptomic profiles and signalling networks of PD-1⁺CD4⁺ T cells offer new insights into their role in RA pathogenesis.

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterised by synovial proliferation of fibroblasts and leukocytes infiltration, affecting approximately 0.5% of the global population.^{1, 2} Its pathogenesis involves dysregulated immune responses that perpetuate inflammation.³ Activated immune cells release autoantibodies and inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), further intensifying synovial inflammation.⁴ Peripheral CD4⁺ T cells are thought to be recruited and activated within the inflamed synovium.⁵ In collagen-induced arthritis mouse models, the conditional knockout of CD4⁺ T cells significantly reduces inflammation, underscoring their critical role in immune regulation in RA.^5-7

Programmed death 1 (PD-1), encoded by PDCD1 or PD-1 gene, regulates immune suppression by interacting with its ligand, PD-L1.⁸ PD-1 is primarily expressed on T cells, where its activation recruits phosphatases that inhibit tyrosine phosphorylation, leading to T cell supression.⁸ While PD-1 inhibitors restore T cell antitumor activity in cancer patients, PD-1 agonists enhance immunosuppression and have been proposed for treating autoimmune diseases by modulating excessive immune responses.^{9, 10} PD-1 deficiency in collagen-induced arthritis models results in more severe inflammation, whereas PD-L1 immunoglobulin therapy has been shown to reduce joint inflammation.¹¹ These findings highlight PD-1's role in maintaining immune tolerance in RA.¹²

Recent studies have identified PD-1⁺CD4⁺ T cells as key mediators in RA. Peripheral helper T cells (T_PH), marked by a PD-1^highCXCR5⁻CD4⁺ T cells phenotype, promote pathogenic B cells activity within the inflamed synovium.^{13, 14} Some studies have linked elevated PD-1 expression on CD4⁺ T cells with increased RA disease activity,^{15, 16} while others have reported reduced PD-1 expression in RA, showing an inverse correlation with disease severity.^{5, 17} Thus, further research is required to clarify PD-1 expression and the role of PD-1⁺CD4⁺ T cells in RA.

This study utilised flow cytometry to assess PD-1 expression on CD4⁺ T cells in RA. In addition, single-cell RNA sequencing (scRNA-seq) data and bioinformatics analyses were employed to explore the heterogeneity and characteristics of PD-1⁺CD4⁺ T cells, offering novel insights into RA pathogenesis.¹⁸

Results

PD-1 expression on CD4⁺ T cells in the peripheral blood of RA patients

This study included 47 RA patients and 12 age- and gender-matched healthy controls (HC). Demographical and clinical data are summarised in Supplementary table 1. PD-1 expression on CD4⁺ T cells was assessed via flow cytometry, with the gating strategy depicted in Figure 1a. The proportion of CD4⁺ T cells did not differ significantly between RA patients and HCs (Figure 1b). However, the frequency of PD-1⁺CD4⁺ T cell was markedly higher in RA patients (Figure 1c and d).

Details are in the caption following the image — **Figure 1**
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Expression levels of PD-1 in peripheral blood of RA patients. **(a)** Schematic of the flow cytometry analysis strategy. **(b)** A boxplot depicting the proportion of CD4⁺ T cells in RA patients (n = 47) compared to HC (n = 12). Each group was repeated in triplicate. **(c)** Flow cytometry plots illustrating the proportion of PD-1⁺CD4⁺ T cells, with samples from HC, RA patients, and fluorescence minus one (FMO) control shown from right to left. The x-axis and y-axis represent fluorescence intensities, and cell population percentages are labelled in each plot. **(d)** A boxplot showing the percentages of PD-1⁺CD4⁺ T cells in RA patients compared to HC. **(e)** ELISA measurements of sPD-1 and sPD-L1 concentrations in plasma. **(f)** Correlation analysis between concentration of sPD-1 (x-axis) and TNF-α concentration, ESR (y-axis). The line represents the best linear regression fit, and shaded areas denote 95% confidence intervals. Spearman correlation coefficients (ρ) and P-values are shown. Statistical significance was determined using the two-sided unpaired Wilcoxon test. A P-value < 0.05 was considered statistically significant. Statistical significance levels are indicated: ns. (no significant difference), **P < 0.01, ***P < 0.001.

Plasma concentrations of soluble PD-1 (sPD-1) and soluble PD-L1 (sPD-L1) were measured using enzyme-linked immunosorbent assay (ELISA). RA patients exhibited significantly higher sPD-1 levels compared to HCs, while sPD-L1 levels showed no significant difference between groups (Figure 1e). To explore the clinical significance of PD-1, we examined correlations between sPD-1 and RA activity markers, including erythrocyte sedimentation rate (ESR) and TNF-α, both recognised indicators of disease severity.¹⁹ As shown in Figure 1f, sPD-1 levels correlated positively with TNF-α and ESR, indicating that sPD-1 may serve as a biomarker for RA disease activity.

Transcriptomic profiles of PD-1⁺CD4⁺ T cells in the PBMCs of RA patients

To characterise peripheral PD-1⁺CD4⁺ T cells, we performed integrated analysis on two scRNA-seq datasets (GSE159117, GSE208161)^{20, 21} from five RA cases. A total of 24 123 T cells, identified as PTPRC⁺CD3E⁺ at the transcriptome level, were obtained to subsequent analysis. Unsupervised clustering analysis divided these T cells into 10 subtypes, including CCR7⁺⁺CD4⁺ T cells, CCR7⁺CD4⁺ T cells, FOXP3⁺CD4⁺ T cells, CCR7⁺CD8⁺ T cells, GZMK⁺CD8⁺ T cells, NKG7⁺CD8⁺ T cells, HAVCR2⁺ T cells, MT-CO2⁺ T cells, TUBB⁺ T cells and KLRC2⁺ T cells (Figure 2a). The expression patterns of canonical markers enabled precise annotation of these subtypes (Figure 2a and b).^{20, 22} We identified two distinct CD4⁺ T cell subtypes based on CCR7 expression levels. CCR7, which is predominantly expressed on naive T cells and central memory T cells,²³ is crucial for regulating memory T cell migration, lymphocyte activation and dendritic cell maturation.²⁴ These results advance our understanding of T cell subtypes in RA.

Our bioinformatic analysis detected PD-1 expression on both CCR7⁺CD4⁺ T cells and NKG7⁺CD8⁺ T cells (Figure 2a and c). The NKG7⁺CD8⁺ T cells, known for their cytotoxic functions,²² may use PD-1 to modulate immune response in a compensatory way for RA patients. Differentially expressed genes (DEGs) analysis revealed that PD-1⁺CCR7⁺CD4⁺ T cells exhibited high levels of KLF2 and IL32 compared to PD-1⁻CCR7⁺CD4⁺ T cells (Figure 2d, Supplementary table 2). KLF2, a transcription factors from the Kruppel family, plays a role in B cell immune response.²⁵ IL32, a pro-inflammatory cytokine, induces the release of TNF-α, interleukin-6 (IL-6), interleukin-1β (IL-1β) and interleukin-8 (IL-8).²⁶ Gene set enrichment analysis (GSEA) further demonstrated activation of type I interferon production and upregulation of interferon-β production signalling pathways in PD-1⁺CCR7⁺CD4⁺ T cells (Figure 2e). These results suggest that PD-1⁺CCR7⁺CD4⁺ T cells contribute to immune effector functions in RA patients.

CCR7, KLF2 and IL32 expression in peripheral PD-1⁺CD4⁺ T cells in RA patients

We analysed the expression of CCR7, KLF2 and IL32 in circulating PD-1⁺CD4⁺ T cells using flow cytometry on peripheral blood samples from eight RA patients. Patient demographics are summarised in Supplementary table 3. Among the CD4⁺ T cells, 17.38% were PD-1⁺CCR7⁺, while 7.25% were PD-1⁺CCR7⁻ (Figure 3a and b). This indicates that CCR7⁺PD-1⁺CD4⁺ T cells are twice as common as CCR7⁻PD-1⁺CD4⁺ T cells. Additionally, 11.09% of CD4⁺ T cells were PD-1⁺KLF2⁺, a significantly higher proportion compared to 0.71% of PD-1⁺KLF2⁻ cells. Similarly, 10.61% of CD4⁺ T cells were PD-1⁺IL32⁺, while only 0.87% were PD-1⁺IL32⁻ (Figure 3c and d). These results demonstrate the co-expression of PD-1 with CCR7, KLF2 and IL32 in peripheral CD4⁺ T cells of RA patients.

Previous studies have identified two distinct CD4⁺PD-1⁺ T cell subtypes: follicular helper T cells (T_FH) marked by PD-1⁺CXCR5⁺CD4⁺ T cells, and T_PH marked by PD-1⁺CXCR5⁻CD4⁺ T cells, which play roles in B cells activation and differentiation in RA and allergic diseases.^27-29 In our study, PD-1⁺CXCR5⁻ T_PH cells comprised 21.91% of CD4⁺ T cells, whereas PD-1⁺CXCR5⁺ T_FH cells accounted for 5.34% (Figure 4a and b). This suggests that PD-1⁺CXCR5⁻CD4⁺ T_PH cells are approximately four times more prevalent than PD-1⁺CXCR5⁺CD4⁺ T_FH cells in the peripheral blood of RA patients. Within the CD4⁺CXCR5⁻ T cell subset, 15.17% were PD-1⁺CCR7⁺ (Figure 4c and d). Moreover, PD-1⁺KLF2⁺ cells made up 10.58%, and PD-1⁺IL32⁺ cells constituted 9.37% of CD4⁺CXCR5⁻ T cells (Figure 4e and f). These results indicate that T_PH cells are significantly more frequent than T_FH cells in RA patients and that most T_PH cells express CCR7, KLF2 and IL32.

PD-1 expression in T cell subtypes in the synovium of RA patients

The scRNA-seq dataset from the ImmPort database (access No. SDY998) classifies cells from synovium of RA patients into fibroblasts, T cells, B cells and monocytes (Figure 5a).³⁰ This dataset provides a valuable resource to explore the characteristics of synovial PD-1⁺CD4⁺ T cells. The expression patterns of canonical cell markers enabled the identification of these cell subtypes (Figure 5b).^30-32 Fibroblasts (Fib) were further categorised into CD34⁺ Fib, DDK3⁺ Fib, CD55⁺ Fib and HLA-DRA⁺ Fib (Figure 5a). Monocytes (Mon) were subdivided into IL1B⁺ Mon, IGHG4⁺ Mon and NUPR1⁺ Mon (Figure 5a), while B cells were classified into MS4A1⁺ B cells, ITGAX⁺ B cells and CD83⁺ B cells (Figure 5a).

The previous study had revealed T cell subtypes in synovial tissues, including CCR7⁺ T cells, GZMK⁺ T cells and so on.³⁰ Based on specific markers expression, we re-analysed and classified these cells into six subpopulations, including CCR7⁺CD4⁺ T cells, CXCL13⁺CD4⁺ T cells, FOXP3⁺CD4⁺ T cells, GZMK⁺CD8⁺ T cells, GNLY⁺CD8⁺ T cells and MKI67⁺CD8⁺ T cells (Figure 5a and b). PD-1 was predominantly expressed in CCR7⁺CD4⁺ T cells, CXCL13⁺CD4⁺ T cells and GZMK⁺CD8⁺ T cells (Figure 5c and d). Specifically, PD-1⁺ T cells constituted 26.67% of CCR7⁺CD4⁺ T cells and 13.33% of CXCL13⁺CD4⁺ T cells (Figure 5d). These results indicate significant heterogeneity among PD-1⁺CD4⁺ T cells in the synovium.

Transcriptomic profiles of PD-1⁺CCR7⁺CD4⁺ T cells in the synovium of RA patients

To characterise synovial PD-1⁺CD4⁺ T cells, we identified DEGs by comparing PD-1⁺CCR7⁺CD4⁺ T cells with PD-1⁻CCR7⁺CD4⁺ T cells. PD-1⁺CCR7⁺CD4⁺ T cells exhibited elevated levels of TNF, LCP2 and DUSP2 (Figure 6a, Supplementary table 4). ClueGO analysis revealed that the upregulated DEGs in PD-1⁺CCR7⁺CD4⁺ T cells were involved in pathways related to the positive regulation of protein catabolism, cellular catabolism, and negative regulation of protein phosphorylation (Figure 6b). GSEA further indicated that PD-1⁺CCR7⁺CD4⁺ T cells were activated in pathways associated with PD-1 signalling, negative regulation of programmed cell death, positive regulation of T cell activation and interleukin-10 signalling (Figure 6c, Supplementary figure 1a). Additionally, regulons for the cell types were identified using SCENIC package,^{33, 34} revealing high expression of transcription factors such as EGR3, KLF5, TCF12 in PD-1⁺CCR7⁺CD4⁺ T cells (Figure 6d). These transcription factors may provide valuable targets for further experimental studies.

Transcriptomic profiles of PD-1⁺CXCL13⁺CD4⁺ T cells in the synovium of RA patients

To further investigate synovial PD-1⁺CXCL13⁺CD4⁺ T cells, we analysed their gene expression profiles in comparison to PD-1⁻CXCL13⁺CD4⁺ T cells. PD-1⁺CXCL13⁺CD4⁺ T cells showed significantly higher expression levels of ARID5A, DUSP2 and other genes (Figure 7a, Supplementary table 5). ClueGO analysis revealed that upregulated DEGs in these cells were associated with the pathways related to T cell activation, IL-10 production regulation, TNF superfamily cytokine production and IL-6 production (Figure 7b). This suggests a potential role for PD-1⁺CXCL13⁺CD4⁺ T cells in inflammatory cytokine secretion within the RA synovium.

Gene set enrichment analysis results highlighted that PD-1⁺CXCL13⁺CD4⁺ T cells were involved in the PD-1 signalling pathway, second messenger generation, T cell receptor signalling and CD28 family co-stimulation signalling (Figure 7c, Supplementary figure 1b). SCENIC analysis identified specific transcription factors, including BCL6 and BATF in PD-1⁺CXCL13⁺CD4⁺ T cells, while FOXN2 and PPARG were expressed in PD-1⁻CXCL13⁺CD4⁺ T cells (Figure 7d). These differential transcription factors may contribute to the distinct immune functions observed between PD-1⁻CXCL13⁺CD4⁺ T cells and PD-1⁺CCR7⁺CD4⁺ T cells.

Analysis of cell–cell communication between PD-1⁺CD4⁺ T cells and other cells in RA synovium

We used the CellChat algorithm to explore the cell–cell communication networks in the RA synovial microenvironment (Supplementary figure 2). The CellChat analysis demonstrates that the BTLA-TNFRSF14 ligand-receptor pair is pivotal for interactions between PD-1⁺CXCL13⁺CD4⁺ T cells and ITGAX⁺ B cells, impacting BTLA signalling pathways (Figure 8a). BTLA expression is notably high in PD-1⁺CXCL13⁺CD4⁺ T cells (Figure 8c), consistent with previous findings that BTLA is upregulated in effector CD4⁺ T cells and can drive autoreactivity in lupus patients.³⁵ The TNFRSF12-TNFRSF12A pair facilitates communication between PD-1⁺CCR7⁺CD4⁺ T cells and DDK3⁺ fibroblasts (Figure 8a). Additionally, PD-1⁺CCR7⁺CD4⁺ T cells amplify TGFB1 signalling pathways, with TGFB1-TGFBR1 and TGFB1-TGFBR2 serving as key mediators (Figure 8a).

Both PD-1⁺CCR7⁺CD4⁺ T cells and PD-1⁺CXCL13⁺CD4⁺ T cells receive signals from MKI67⁺CD8⁺ T cells via the FAS-FASLG pair (Figure 8b). PD-1⁺CXCL13⁺CD4⁺ T cells also receive signals from HLA-DRA⁺, CD34⁺, CD55⁺ and DDK3⁺ fibroblasts through the TNFSF10-TNFRSF10A pair, and from CD55⁺ and DDK3⁺ fibroblasts via the TNFSF15-TNFRSF25 pair (Figure 8b). In summary, CellChat analysis suggests that PD-1⁺CD4⁺ T cells interact with B cells and synovial fibroblasts through BTLA and TNFSF signalling pathways.

Discussion

The PD-1 signalling pathway plays a critical role in autoimmune diseases, including RA.¹² A phase 2 trial of Peresolimab, a PD-1 agonist, demonstrated its therapeutic potential in RA patients,¹⁰ highlighting PD-1 activation as a promising treatment strategy. In this study, we found an increased percentage of PD-1⁺CD4⁺ T cells in the peripheral blood of RA patients. Additionally, elevated plasma levels of sPD-1 were positively correlated with both ESR and TNF-α, indicating that sPD-1 may serve as a biomarker for RA disease activity.

In allergic diseases, circulating T_FH cells, marked by low CCR7 and high PD-1 expression, have been shown to enhance IgG production in memory B cells.²⁸ In RA patients, we confirmed PD-1 co-expressed with CCR7, KLF2 and IL32 in circulating T_PH cells. CCR7 directs T cell homing and positioning, contributing to adaptive immune responses.³⁶ KLF2 regulates lineage-specific transcription factors and influences CD4⁺ T cells localisation,³⁷ while IL32 stimulates the production of inflammatory cytokines, such as TNF-α, IL-8, IL-6 and IL-1β.³⁸ Furthermore, type I interferon and interferon-β production pathways were activated in PD-1⁺CCR7⁺CD4⁺ T cells, suggesting their immune effector role in RA.

Matsuda et al. reported PD-1 expression in synovial lymphocytes in 66.7% of RA cases, as assessed by immunohistochemistry.³⁹ Our study extended these findings by identifying PD-1 expression on both CCR7⁺CD4⁺ T cells and CXCL13⁺CD4⁺ T cells in RA synovial tissue. PD-1⁺CCR7⁺CD4⁺ T cells exhibited higher levels of TNF, LCP2 and DUSP2 than their PD-1⁻ counterparts. TNF is a central therapeutic target in RA and other autoimmune diseases,⁴⁰ while LCP2 mediates signals from the CD28/B7 family, regulating T cell receptor signalling pahways.⁴¹ DUSP2, part of the dual-specificity phosphatase family, plays a role in cell proliferation and differentiation.⁴² Both PD-1⁺CXCL13⁺CD4⁺ T cells and PD-1⁺CCR7⁺CD4⁺ T cells were linked to the pathways involved in T cell activation and cytokine production. Overall, these findings suggest that PD-1⁺CD4⁺ T cells in RA synovium are highly heterogeneous and contribute to complex immune regulatory mechanisms in RA.

Plasma levels of sPD-1 were elevated in RA patients. In vitro studies demonstrated that sPD-1 could inhibit the PD-1/PD-L1 interaction.⁴³ In a collagen-induced arthritis mouse model, sPD-1 blocked PD-1 on T cell membranes, exacerbating inflammation via the Th1 and Th17 pathways.⁴⁴ However, plasma levels of sPD-L1 did not increase in RA patients, indicating possible insufficiency in PD-1 signalling. This suggests potential dysfunction of the PD-1/PD-L1 signalling axis in RA.

In RA synovium, T_PH cells enhance the differentiation of memory B cells into immunoglobulin-producing cells.⁴⁵ Our analysis revealed enrichment of BTLA and TNFSF signalling pathway among PD-1⁺CD4⁺ T cells, ITGAX⁺ B cells and synovial fibroblasts. These pathways are mediated by ligand-receptor pairs such as BTLA-TNFRSF14, TNFSF10-TNFRSF10A and TNFSF15-TNFRSF25, highlighting critical intercellular communication networks in the RA synovial microenvironment.

SCENIC analysis identified distinct transcriptional regulators in PD-1⁺CCR7⁺CD4⁺ T cells (e.g. EGR3, KLF) and PD-1⁺CXCL13⁺CD4⁺ T cells (e.g. BCL6, BATF) from RA synovium. EGR3 is involved in T cell development and differentiation,⁴⁶ while KLF5 regulates T cell development, metabolism and pluripotency.⁴⁷ BCL6 drives CD4⁺ T cells differentiation into T_FH cells,⁴⁸ and BATF, part of the activator protein-1 family, enhance immune response in T cells and B cells.⁴⁹ These transcription factors offer insight into the regulatory mechanisms of PD-1⁺CD4⁺ T cells in RA.

This study has limitations. Only 12 cases were included because of challenges in obtaining peripheral blood from healthy individuals. Moreover, the treatment history and clinical characteristics of patients in the scRNA-seq datasets were not fully available. Further investigation is needed to examine the molecular features of PD-1⁺CD4⁺ T cells under different treatment regimens and stages of disease activity. In conclusion, this study characterises PD-1 expression and the functional role of PD-1⁺CD4⁺ T cells in RA, providing a basis for the development of PD-1 agonist therapies.

Methods

RA patients and healthy volunteers

Patients with RA were enrolled from the Shenzhen People's Hospital, while HC were recruited from colleagues at the same institution. Approval for the study was granted by the Institutional Review Board of Shenzhen People's Hospital (Approval No. LL-KY-2019504). Informed consent was secured from all participants for the use of their blood samples. It was confirmed that HC volunteers did not have autoimmune diseases. RA diagnoses were established according to the 2010 European League Against Rheumatism (EULAR) and American College of Rheumatology (ACR) criteria.⁵⁰ The exclusion criteria comprised acute active infections, acquired immune deficiency syndrome, tuberculosis, hepatitis B virus, malignant tumors, liver and kidney failure and neuropsychiatric disorders.

Collections of clinical parameters and assessment for disease activity

Clinical information was recorded, including age, height, body weight, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP), tender joint counts (TJC) and swollen joint counts (SJC). Patient global assessment of disease activity (PtGA) were assessed based on above clinical characteristics. The scores of DAS28-ESR and DAS28-CRP were used to assess disease activity and calculated by the following formulas^{51, 52}: DAS28-ESR = 0.56 × SQRT (TJC28) + 0.28 × SQRT (SJC28) + 0.70 × ln (ESR) + 0.014 × (PtGA); DAS28-CRP = 0.56 × SQRT (TJC28) + 0.28 × SQRT (SJC28) + 0.36 × ln (CRP + 1) + 0.014 × (PtGA) + 0.96. RA patients were assigned into four groups according to the following thresholds: remission (DAS28 < 2.6), low (DAS28 < 2.6 to 3.2), moderate (DAS28 > 3.2 to 5.1) and high (DAS28 > 5.1). Demographical and clinical data are summarised in the Supplementary Information.

Blood samples collection and flow cytometry analysis

Fresh peripheral blood samples were collected in EDTA-K2 anticoagulant tubes, and disposed within 2 h in a 4°C refrigerator. Blood samples were stained for 15 min at 25°C using the following antibodies: anti-CD4 (BD Biosciences, San Diego, USA), anti-CD8 (BD Biosciences), anti-CD25 (BD Biosciences), anti-CD127 (BD Biosciences), anti-PD-1 (BD Biosciences), anti-CCR7 (BD Biosciences), anti-CXCR5 (R&D Systems, Minnesota, USA), anti-KLF2 (R&D Systems), anti-IL32 (BD Biosciences). After incubation with these antibodies, red blood cells were lysed with lysing buffer (BD Biosciences) for 10 min. The cells were further fixed and permeabilised for 30 min using a Fixation/Permeabilisation Solution Kit (BD Biosciences). Subsequently, the cells were stained with intracellular and intranuclear antibodies for 30 min. Cell fluorescence was acquired on a BD Aria II flow cytometry equipment (BD Biosciences). Lastly, the data of flow cytometry was analysed using the FlowJo software (version 10.8.1).

Measurement of sPD-1, sPD-L1 and TNF-α concentration by ELISA

The plasma samples from RA patients were collected and stored in a −80°C refrigerator. Human PD-1 ELISA kit (Novus, Colorado, USA), PD-L1 ELISA kit (Novus) and TNF-α ELISA kits (Dakewe Biotech, Shenzhen, China) were used to measure the concentration of sPD-1, sPD-L1 and TNF-α. ELISA was performed at a wavelength of 450 nm according to the manufacturer instructions.

Single-cell transcriptome datasets and bioinformatics analysis

Single-cell RNA-seq datasets from the PBMCs and synovial tissue in RA patients were downloaded from the ImmPort (https://www.immport.org/shared/study/SDY998, accession code SDY998)³⁰ and the GEO databases (https://www.ncbi.nlm.nih.gov, accession no. GSE159117, GSE208161).^{20, 21}

Seurat package (version 4.0.2, https://satijalab.org/seurat/)⁵³ in R studio was used to identify highly variable genes. Principal component analysis (PCA) and UMAP visualisation analysis were performed using 2000 highly variable genes. For the GSE159117 and GSE208161 datasets, the top 15 principal components were selected for unsupervised clustering using FindClusters with the parameters (resolution equal to 1.0). For the SDY998 dataset,³⁰ the top 20 principal components were selected for unsupervised clustering using FindClusters with the parameters (resolution equal to 1.2). The UMAP plots were used for dimension reduction and visualisation of cell subtypes and gene expression.

Those cells with PD-1 expression greater than zero were deemed PD-1⁺ and those with expression less than or equal to zero were deemed PD-1⁻. DEGs were identified using the FindMarkers function in the Seurat package. The DEGs with a P-value < 0.05 were considered statistically significant.

Functional enrichment analysis

Functional enrichment analysis of whole gene expression features were conducted on GSEA software (http://www.gsea-msigdb.org/gsea/index.jsp).⁵⁴ Reference gene sets related to signalling pathways were downloaded from the GSEA database (https://data.broadinstitute.org/gsea-msigdb/msigdb/release/7.4/). The protein–protein interaction and signalling pathways network of the upregulated DEGs in PD-1⁺ T cells were performed using ClueGO and CluePedia plugins of Cytoscape software.^55-57

Transcription factors analysis

The regulon analysis was performed via SCENIC package, according to the motifs database for RcisTarget and GRNboost.^{33, 34, 58} To improve the scalability or robustness, we randomly selected a certain number of cells, according to the minimum number of cell subpopulations.⁵⁹ To quantify the cell-type specificity of a regulon, we evaluated the Jensen-Shannon Divergence (JSD) to identify unique transcription factors.⁵⁹

Cell–cell communication analysis

The CellChat package was employed to identify related ligand–receptor pairs from scRNA-seq data.⁶⁰ Ligand–receptor pairs with a P-value < 0.05 were considered to be significant interacting molecules among different cell subtypes. The most unique signalling pathways in PD-1⁺CD4⁺ T cells were selected for further visualisation.

Statistical analysis

R studio (version 4.0.0) was used for statistical analysis. Visualisation of graphs was conducted using the ggplot2 package in R studio. The correlation analysis between the two datasets was performed using Spearman correlation analysis. The two-sided unpaired Wilcoxon test was applied for quantitative data with non-normal distributions. A P-value < 0.05 was considered to be statistically significant.

Acknowledgments

We thank the participants in this study and the staff members in the Rheumatology Department of Shenzhen People's Hospital, Shenzhen, China. The study was supported by the Sanming Project of Medicine in Shenzhen (Grant number: SZSM202111006), the Shenzhen Key Medical Discipline Construction Fund (Grant number: SZXK011) and the National Natural Science Foundation of China (Grant number: 82302030).

Author contributions

Yan-juan Chen: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; validation; visualization; writing – original draft; writing – review and editing. Yong Chen: Investigation; methodology; resources; software; visualization; writing – review and editing. Ping Chen: Methodology; resources. Yi-qun Jia: Writing – review and editing. Hua Wang: Funding acquisition; supervision; writing – review and editing. Xiao-ping Hong: Funding acquisition; supervision; writing – review and editing.

Conflict of interest

The authors declare no conflict of interest.

Ethics approval

The study design was approved by the Institutional Review Boards of the Shenzhen People's Hospital (No. LL-KY-2019504). Informed consent was secured from all participants for the use of their blood samples.

Open Research

Data availability statement

Data are available upon reasonable request. All data relevant to the study are either included in the article or uploaded as Supplementary Information.

Supporting Information

References

1Di Matteo A, Bathon JM, Emery P. Rheumatoid arthritis. Lancet 2023; 402: 2019–2033.
10.1016/S0140-6736(23)01525-8
PubMed Web of Science® Google Scholar
2Finckh A, Gilbert B, Hodkinson B et al. Global epidemiology of rheumatoid arthritis. Nat Rev Rheumatol 2022; 18: 591–602.
PubMed Web of Science® Google Scholar
3Gravallese EM, Firestein GS. Rheumatoid arthritis - common origins, divergent mechanisms. N Engl J Med 2023; 388: 529–542.
10.1056/NEJMra2103726
CAS PubMed Web of Science® Google Scholar
4Jang S, Kwon EJ, Lee JJ. Rheumatoid arthritis: pathogenic roles of diverse immune cells. Int J Mol Sci 2022; 23: 905.
10.3390/ijms23020905
CAS PubMed Web of Science® Google Scholar
5Li S, Liao W, Chen M et al. Expression of programmed death-1 (PD-1) on CD4⁺ and CD8⁺ T cells in rheumatoid arthritis. Inflammation 2014; 37: 116–121.
10.1007/s10753-013-9718-8
CAS PubMed Web of Science® Google Scholar
6Bai L, Liu W, Guo P et al. Elevated levels of soluble endothelial protein C receptor in rheumatoid arthritis and block the therapeutic effect of protein C in collagen-induced arthritis. Int Immunopharmacol 2020; 81: 106255.
10.1016/j.intimp.2020.106255
CAS PubMed Google Scholar
7Huang L, Liang L, Ji Z et al. Proteomics profiling of CD4⁺ T-cell-derived exosomes from patients with rheumatoid arthritis. Int Immunopharmacol 2023; 122: 110560.
10.1016/j.intimp.2023.110560
CAS PubMed Web of Science® Google Scholar
8Suzuki K, Tajima M, Tokumaru Y et al. Anti-PD-1 antibodies recognizing the membrane-proximal region are PD-1 agonists that can down-regulate inflammatory diseases. Sci Immunol 2023; 8: eadd4947.
10.1126/sciimmunol.add4947
CAS PubMed Web of Science® Google Scholar
9Calabrese LH, Calabrese C, Cappelli LC. Rheumatic immune-related adverse events from cancer immunotherapy. Nat Rev Rheumatol 2018; 14: 569–579.
10.1038/s41584-018-0074-9
PubMed Web of Science® Google Scholar
10Tuttle J, Drescher E, Simon-Campos JA et al. A phase 2 trial of Peresolimab for adults with rheumatoid arthritis. N Engl J Med 2023; 388: 1853–1862.
10.1056/NEJMoa2209856
CAS PubMed Web of Science® Google Scholar
11Wang G, Hu P, Yang J, Shen G, Wu X. The effects of PDL-Ig on collagen-induced arthritis. Rheumatol Int 2011; 31: 513–519.
10.1007/s00296-009-1249-0
CAS PubMed Web of Science® Google Scholar
12Zamani MR, Aslani S, Salmaninejad A, Javan MR, Rezaei N. PD-1/PD-L and autoimmunity: a growing relationship. Cell Immunol 2016; 310: 27–41.
10.1016/j.cellimm.2016.09.009
CAS PubMed Web of Science® Google Scholar
13Murray-Brown W, Guo Y, Small A et al. Differential expansion of T peripheral helper cells in early rheumatoid arthritis and osteoarthritis synovium. RMD Open 2022; 8: e002563.
10.1136/rmdopen-2022-002563
PubMed Google Scholar
14Rao DA, Gurish MF, Marshall JL et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 2017; 542: 110–114.
10.1038/nature20810
CAS PubMed Google Scholar
15Bartosinska J, Zakrzewska E, Krol A et al. Differential expression of programmed death 1 (PD-1) on CD4⁺ and CD8⁺ T cells in rheumatoid arthritis and psoriatic arthritis. Pol Arch Intern Med 2017; 127: 815–822.
PubMed Google Scholar
16Luo Q, Fu P, Guo Y et al. Increased TIGIT⁺PD-1⁺CXCR5⁻CD4⁺ T cells are associated with disease activity in rheumatoid arthritis. Exp Ther Med 2022; 24: 642.
10.3892/etm.2022.11579
CAS PubMed Google Scholar
17Koohini Z, Hossein-Nataj H, Mobini M, Hosseinian-Amiri A, Rafiei A, Asgarian-Omran H. Analysis of PD-1 and Tim-3 expression on CD4⁺ T cells of patients with rheumatoid arthritis; negative association with DAS28. Clin Rheumatol 2018; 37: 2063–2071.
10.1007/s10067-018-4076-4
PubMed Web of Science® Google Scholar
18Li LX, Zhang X, Zhang H et al. Single-cell and cellchat resolution identifies collecting duct cell subsets and their communications with adjacent cells in PKD kidneys. Cells 2022; 12: 45.
10.3390/cells12010045
PubMed Google Scholar
19Zhang N, Zheng N, Luo D et al. A novel single domain bispecific antibody targeting VEGF and TNF-α ameliorates rheumatoid arthritis. Int Immunopharmacol 2024; 126: 111240.
10.1016/j.intimp.2023.111240
CAS PubMed Google Scholar
20Zhang B, Zhang Y, Xiong L et al. CD127 imprints functional heterogeneity to diversify monocyte responses in inflammatory diseases. J Exp Med 2022; 219: e20211191.
10.1084/jem.20211191
CAS PubMed Web of Science® Google Scholar
21Sonigra A, Nel HJ, Wehr P et al. Randomized phase I trial of antigen-specific tolerizing immunotherapy with peptide/calcitriol liposomes in ACPA⁺ rheumatoid arthritis. JCI Insight 2022; 7: e160964.
10.1172/jci.insight.160964
PubMed Web of Science® Google Scholar
22Wang X, Chen Y, Li Z et al. Single-cell RNA-seq of T cells in B-ALL patients reveals an exhausted subset with remarkable heterogeneity. Adv Sci 2021; 8: e2101447.
10.1002/advs.202101447
CAS Google Scholar
23Han L, Zhang L. CCL21/CCR7 axis as a therapeutic target for autoimmune diseases. Int Immunopharmacol 2023; 121: 110431.
10.1016/j.intimp.2023.110431
CAS PubMed Google Scholar
24Wang T, Peng R, Ni H et al. Effects of chemokine receptor CCR7 in the pathophysiology and clinical features of the immuno-inflammatory response in primary pterygium. Int Immunopharmacol 2023; 118: 110086.
10.1016/j.intimp.2023.110086
CAS PubMed Google Scholar
25Wittner J, Schuh W. Kruppel-like factor 2: a central regulator of B cell differentiation and plasma cell homing. Front Immunol 2023; 14: 1172641.
10.3389/fimmu.2023.1172641
CAS PubMed Google Scholar
26de Albuquerque R, Komsi E, Starskaia I, Ullah U, Lahesmaa R. The role of Interleukin-32 in autoimmunity. Scand J Immunol 2021; 93: e13012.
10.1111/sji.13012
PubMed Web of Science® Google Scholar
27Wang ZC, Yao Y, Chen CL et al. Extrafollicular PD-1^highCXCR5⁻CD4⁺ T cells participate in local immunoglobulin production in nasal polyps. J Allergy Clin Immunol 2022; 149: 610–623.
10.1016/j.jaci.2021.06.023
CAS PubMed Google Scholar
28Yao Y, Chen CL, Yu D, Liu Z. Roles of follicular helper and regulatory T cells in allergic diseases and allergen immunotherapy. Allergy 2021; 76: 456–470.
10.1111/all.14639
CAS PubMed Web of Science® Google Scholar
29Yao Y, Chen Z, Zhang H et al. Selenium-GPX4 axis protects follicular helper T cells from ferroptosis. Nat Immunol 2021; 22: 1127–1139.
10.1038/s41590-021-00996-0
CAS PubMed Web of Science® Google Scholar
30Zhang F, Wei K, Slowikowski K et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat Immunol 2019; 20: 928–942.
10.1038/s41590-019-0378-1
PubMed Web of Science® Google Scholar
31Toker A, Engelbert D, Garg G et al. Active demethylation of the Foxp3 locus leads to the generation of stable regulatory T cells within the thymus. J Immunol 2013; 190: 3180–3188.
10.4049/jimmunol.1203473
CAS PubMed Web of Science® Google Scholar
32Wu X, Liu Y, Jin S et al. Single-cell sequencing of immune cells from anticitrullinated peptide antibody positive and negative rheumatoid arthritis. Nat Commun 2021; 12: 4977.
10.1038/s41467-021-25246-7
CAS PubMed Web of Science® Google Scholar
33Aibar S, Gonzalez-Blas CB, Moerman T et al. SCENIC: Single-cell regulatory network inference and clustering. Nat Methods 2017; 14: 1083–1086.
10.1038/nmeth.4463
CAS PubMed Web of Science® Google Scholar
34Davie K, Janssens J, Koldere D et al. A single-cell transcriptome atlas of the aging drosophila brain. Cell 2018; 174: e920.
10.1016/j.cell.2018.05.057
Google Scholar
35Aubergeon L, Sawaf M, Felten R et al. High BTLA expression likely contributes to contraction of the regulatory T cell subset in lupus disease. Front Immunol 2021; 12: 767099.
10.3389/fimmu.2021.767099
CAS PubMed Google Scholar
36Moschovakis GL, Bubke A, Friedrichsen M et al. The chemokine receptor CCR7 is a promising target for rheumatoid arthritis therapy. Cell Mol Immunol 2019; 16: 791–799.
10.1038/s41423-018-0056-5
CAS PubMed Web of Science® Google Scholar
37Lee JY, Skon CN, Lee YJ et al. The transcription factor KLF2 restrains CD4⁺ T follicular helper cell differentiation. Immunity 2015; 42: 252–264.
10.1016/j.immuni.2015.01.013
CAS PubMed Web of Science® Google Scholar
38Sasidharan K, Caddeo A, Jamialahmadi O et al. IL32 downregulation lowers triglycerides and type I collagen in di-lineage human primary liver organoids. Cell Rep Med 2024; 5: 101352.
10.1016/j.xcrm.2023.101352
CAS PubMed Google Scholar
39Matsuda K, Miyoshi H, Hiraoka K et al. Clinicopathological value of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) expression in synovium of patients with rheumatoid arthritis. Clin Exp Med 2018; 18: 487–494.
10.1007/s10238-018-0515-4
CAS PubMed Web of Science® Google Scholar
40Hu F, Shi L, Liu X et al. Proinflammatory phenotype of B10 and B10pro cells elicited by TNF-α in rheumatoid arthritis. Ann Rheum Dis 2024; 83: 576–588.
10.1136/ard-2023-224878
PubMed Google Scholar
41Wang Z, Peng M. A novel prognostic biomarker LCP2 correlates with metastatic melanoma-infiltrating CD8⁺ T cells. Sci Rep 2021; 11: 9164.
10.1038/s41598-021-88676-9
CAS PubMed Google Scholar
42Yi H, Morton CC, Weremowicz S et al. Genomic organization and chromosomal localization of the DUSP2 gene, encoding a MAP kinase phosphatase, to human 2p11.2-q11. Genomics 1995; 28: 92–96.
10.1006/geno.1995.1110
CAS PubMed Web of Science® Google Scholar
43Bommarito D, Hall C, Taams LS et al. Inflammatory cytokines compromise programmed cell death-1 (PD-1)-mediated T cell suppression in inflammatory arthritis through up-regulation of soluble PD-1. Clin Exp Immunol 2017; 188: 455–466.
10.1111/cei.12949
CAS PubMed Web of Science® Google Scholar
44Liu C, Jiang J, Gao L et al. Soluble PD-1 aggravates progression of collagen-induced arthritis through Th1 and Th17 pathways. Arthritis Res Ther 2015; 17: 340.
10.1186/s13075-015-0859-z
PubMed Web of Science® Google Scholar
45Yoshitomi H, Ueno H. Shared and distinct roles of T peripheral helper and T follicular helper cells in human diseases. Cell Mol Immunol 2021; 18: 523–527.
10.1038/s41423-020-00529-z
CAS PubMed Web of Science® Google Scholar
46Kulp M, Larghero P, Alten J et al. The EGR3 regulome of infant KMT2A-r acute lymphoblastic leukemia identifies differential expression of B-lineage genes predictive for outcome. Leukemia 2023; 37: 1216–1233.
10.1038/s41375-023-01895-z
CAS PubMed Web of Science® Google Scholar
47Wu Y, Chen S, Shao Y et al. KLF5 promotes tumor progression and Parp inhibitor resistance in ovarian cancer. Adv Sci 2023; 10: e2304638.
10.1002/advs.202304638
CAS Google Scholar
48Kim S, Boehme L, Nel L et al. Defective STAT5 activation and aberrant expression of BCL6 in naive CD4 T cells enhances follicular Th cell-like differentiation in patients with granulomatosis with polyangiitis. J Immunol 2022; 208: 807–818.
10.4049/jimmunol.2001331
CAS PubMed Web of Science® Google Scholar
49Itahashi K, Irie T, Yuda J et al. BATF epigenetically and transcriptionally controls the activation program of regulatory T cells in human tumors. Sci Immunol 2022; 7: eabk0957.
10.1126/sciimmunol.abk0957
CAS PubMed Web of Science® Google Scholar
50Aletaha D, Neogi T, Silman AJ et al. 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European league against rheumatism collaborative initiative. Ann Rheum Dis 2010; 69: 1580–1588.
10.1136/ard.2010.138461
PubMed Web of Science® Google Scholar
51Funovits J, Aletaha D, Bykerk V et al. The 2010 American College of Rheumatology/European league against rheumatism classification criteria for rheumatoid arthritis: methodological report phase I. Ann Rheum Dis 2010; 69: 1589–1595.
10.1136/ard.2010.130310
PubMed Web of Science® Google Scholar
52Anderson J, Caplan L, Yazdany J et al. Rheumatoid arthritis disease activity measures: American College of Rheumatology recommendations for use in clinical practice. Arthritis Care Res 2012; 64: 640–647.
10.1002/acr.21649
PubMed Web of Science® Google Scholar
53Satija R, Farrell JA, Gennert D et al. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 2015; 33: 495–502.
10.1038/nbt.3192
CAS PubMed Web of Science® Google Scholar
54Subramanian A, Tamayo P, Mootha VK et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102: 15545–15550.
10.1073/pnas.0506580102
CAS PubMed Web of Science® Google Scholar
55Bindea G, Mlecnik B, Hackl H et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009; 25: 1091–1093.
10.1093/bioinformatics/btp101
CAS PubMed Web of Science® Google Scholar
56Bindea G, Galon J, Mlecnik B. CluePedia Cytoscape plugin: pathway insights integrated experimental and in silico data. Bioinformatics 2013; 29: 661–663.
10.1093/bioinformatics/btt019
CAS PubMed Web of Science® Google Scholar
57Shannon P, Markiel A, Ozier O et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13: 2498–2504.
10.1101/gr.1239303
CAS PubMed Web of Science® Google Scholar
58Fiers M, Minnoye L, Aibar S et al. Mapping gene regulatory networks from single-cell omics data. Brief Funct Genomics 2018; 17: 246–254.
10.1093/bfgp/elx046
CAS PubMed Web of Science® Google Scholar
59Suo S, Zhu Q, Saadatpour A et al. Revealing the critical regulators of cell identity in the mouse cell atlas. Cell Rep 2018; 25: 1436–1445.
10.1016/j.celrep.2018.10.045
CAS PubMed Web of Science® Google Scholar
60Jin S, Guerrero-Juarez CF, Zhang L et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 2021; 12: 1088.
10.1038/s41467-021-21246-9
CAS PubMed Web of Science® Google Scholar

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Characteristics of PD-1+CD4+ T cells in peripheral blood and synovium of rheumatoid arthritis patients

Abstract

Objectives

Methods

Results

Conclusion

Introduction

Results

PD-1 expression on CD4+ T cells in the peripheral blood of RA patients

Transcriptomic profiles of PD-1+CD4+ T cells in the PBMCs of RA patients

CCR7, KLF2 and IL32 expression in peripheral PD-1+CD4+ T cells in RA patients

PD-1 expression in T cell subtypes in the synovium of RA patients

Transcriptomic profiles of PD-1+CCR7+CD4+ T cells in the synovium of RA patients

Transcriptomic profiles of PD-1+CXCL13+CD4+ T cells in the synovium of RA patients

Analysis of cell–cell communication between PD-1+CD4+ T cells and other cells in RA synovium

Discussion

Methods

RA patients and healthy volunteers

Collections of clinical parameters and assessment for disease activity

Blood samples collection and flow cytometry analysis

Measurement of sPD-1, sPD-L1 and TNF-α concentration by ELISA

Single-cell transcriptome datasets and bioinformatics analysis

Functional enrichment analysis

Transcription factors analysis

Cell–cell communication analysis

Statistical analysis

Acknowledgments

Author contributions

Conflict of interest

Ethics approval

Open Research

Data availability statement

Supporting Information

References

Citing Literature

Figures

References

Related

Information

Characteristics of PD-1⁺CD4⁺ T cells in peripheral blood and synovium of rheumatoid arthritis patients

PD-1 expression on CD4⁺ T cells in the peripheral blood of RA patients

Transcriptomic profiles of PD-1⁺CD4⁺ T cells in the PBMCs of RA patients

CCR7, KLF2 and IL32 expression in peripheral PD-1⁺CD4⁺ T cells in RA patients

Transcriptomic profiles of PD-1⁺CCR7⁺CD4⁺ T cells in the synovium of RA patients

Transcriptomic profiles of PD-1⁺CXCL13⁺CD4⁺ T cells in the synovium of RA patients

Analysis of cell–cell communication between PD-1⁺CD4⁺ T cells and other cells in RA synovium