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Background <p>Rheumatoid arthritis (RA) is characterized by persistent synovial inflammation, yet the molecular mechanisms distinguishing early from late-stage disease remain incompletely elucidated. Identifying stage-specific biomarkers and pathogenic cellular interactions is crucial for precision medicine.</p> Objective <p>To comprehensively characterize the transcriptomic landscape and cellular composition of early versus late RA synovium, identify diagnostic biomarkers, and elucidate key pathogenic cell–cell interactions driving disease chronicity.</p> Methods <p>Synovial tissues from 51 RA patients (13 early, 38 late-stage) were analyzed using histopathology, immunohistochemistry, bulk RNA sequencing (<i>n</i> = 19),and single-cell RNA sequencing (scRNA-seq, <i>n</i> = 6; 3 Early RA vs. 3 Late-stage RA).Machine learning algorithms (LASSO, SVM-RFE, random forest) were employed to identify diagnostic biomarkers. An artificial neural network (ANN) model was constructed and validated. Cell–cell communication analysis was performed using CellChat.</p> Results <p>Histopathological analysis revealed significantly increased infiltration of macrophages (CD68 +) and plasma cells (CD138 +) in late-stage RA (<i>P</i> &lt; 0.05). RNA sequencing identified 87 differentially expressed genes, with interferon-stimulated genes significantly upregulated. Integrated machine learning identified a minimal three-gene signature (CXCL10, ISG15, IFIH1) as a promising candidate model for RA staging. The three-gene ANN model showed excellent diagnostic performance (AUC = 0.922). Notably, CXCL10 emerged as the most critical component, demonstrating potentially high classification accuracy in this cohort (AUC = 0.767) and standing as the sole independent predictor in multivariable analysis (OR = 7.271, <i>P</i> = 0.022). CXCL10 high expression was strongly associated with M1 macrophage infiltration (<i>r</i> = 0.446, <i>P</i> = 0.005) and enriched in chemokine and JAK-STAT pathways. scRNA-seq revealed macrophages as the primary source of CXCL10, with upstream stimulation from CD8 + T cells via the IFN-γ-CXCL10-CXCR3 axis. Critically, we identified an expanded TREM2 + macrophage subset in late RA, which highly expressed APRIL (TNFSF13) and expanded in parallel with plasma cells expressing APRIL receptors (BCMA + /TACI +). This TREM2 + macrophage-plasma cell niche may represent a potential pathogenic circuit that could contribute to autoimmune chronicity.</p> Conclusions <p>Late-stage RA appears to be characterized by a CXCL10-driven inflammatory signature and an expanded TREM2 + macrophage-plasma cell survival niche. CXCL10 represents a promising candidate biomarker for disease staging that may have mechanistic links to pathogenesis. The IFN-γ-CXCL10-CXCR3 axis and the APRIL-BCMA/TACI pathway may constitute potential therapeutic targets for refractory RA.</p>

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Integrative multi-omics analysis reveals CXCL10-driven inflammation and TREM2 + macrophage-plasma cell survival niche as hallmarks of late-stage rheumatoid arthritis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月12日

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  • Jianbin Li,
  • Mengxia Liu,
  • Yilin Peng,
  • Rui Wu

摘要

Background

Rheumatoid arthritis (RA) is characterized by persistent synovial inflammation, yet the molecular mechanisms distinguishing early from late-stage disease remain incompletely elucidated. Identifying stage-specific biomarkers and pathogenic cellular interactions is crucial for precision medicine.

Objective

To comprehensively characterize the transcriptomic landscape and cellular composition of early versus late RA synovium, identify diagnostic biomarkers, and elucidate key pathogenic cell–cell interactions driving disease chronicity.

Methods

Synovial tissues from 51 RA patients (13 early, 38 late-stage) were analyzed using histopathology, immunohistochemistry, bulk RNA sequencing (n = 19),and single-cell RNA sequencing (scRNA-seq, n = 6; 3 Early RA vs. 3 Late-stage RA).Machine learning algorithms (LASSO, SVM-RFE, random forest) were employed to identify diagnostic biomarkers. An artificial neural network (ANN) model was constructed and validated. Cell–cell communication analysis was performed using CellChat.

Results

Histopathological analysis revealed significantly increased infiltration of macrophages (CD68 +) and plasma cells (CD138 +) in late-stage RA (P < 0.05). RNA sequencing identified 87 differentially expressed genes, with interferon-stimulated genes significantly upregulated. Integrated machine learning identified a minimal three-gene signature (CXCL10, ISG15, IFIH1) as a promising candidate model for RA staging. The three-gene ANN model showed excellent diagnostic performance (AUC = 0.922). Notably, CXCL10 emerged as the most critical component, demonstrating potentially high classification accuracy in this cohort (AUC = 0.767) and standing as the sole independent predictor in multivariable analysis (OR = 7.271, P = 0.022). CXCL10 high expression was strongly associated with M1 macrophage infiltration (r = 0.446, P = 0.005) and enriched in chemokine and JAK-STAT pathways. scRNA-seq revealed macrophages as the primary source of CXCL10, with upstream stimulation from CD8 + T cells via the IFN-γ-CXCL10-CXCR3 axis. Critically, we identified an expanded TREM2 + macrophage subset in late RA, which highly expressed APRIL (TNFSF13) and expanded in parallel with plasma cells expressing APRIL receptors (BCMA + /TACI +). This TREM2 + macrophage-plasma cell niche may represent a potential pathogenic circuit that could contribute to autoimmune chronicity.

Conclusions

Late-stage RA appears to be characterized by a CXCL10-driven inflammatory signature and an expanded TREM2 + macrophage-plasma cell survival niche. CXCL10 represents a promising candidate biomarker for disease staging that may have mechanistic links to pathogenesis. The IFN-γ-CXCL10-CXCR3 axis and the APRIL-BCMA/TACI pathway may constitute potential therapeutic targets for refractory RA.