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Войти в исследования Frontiers in immunology · 2025
Comprehensive and advanced T cell cluster analysis for discriminating seropositive and seronegative rheumatoid arthritis.
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Maeda Shinji, Hashimoto Hiroya, Maeda Tomoyo, Tamechika Shin-Ya, Naniwa Taio, Niimi Akio
Аннотация
OBJECTIVE: Rheumatoid arthritis (RA) is classified into seropositive (SP-RA) and seronegative (SN-RA) types, reflecting distinct immunological profiles. This study aimed to identify the T cell phenotypes associated with each type, thereby enhancing our understanding of their unique pathophysiological mechanisms.
METHODS: We analyzed peripheral blood T cells from 50 participants, including 16 patients with untreated SP-RA, 17 patients with SN-RA, and 17 healthy controls, utilizing 25 T cell markers. For initial analysis, a dataset was established through manual T cell subset gating analysis. For advanced analysis, two distinct datasets derived from a self-organizing map algorithm, FlowSOM, were used: one encompassing all CD3+ T cells and another focusing on activated T cell subsets. Subsequently, these datasets were rigorously analyzed using adaptive least absolute shrinkage and selection operator in conjunction with leave-one-out cross-validation. This approach enhanced analysis robustness, identifying T cell clusters consistently discriminative between SP-RA and SN-RA.
RESULTS: Our analysis revealed significant differences in T cell subsets between RA patients and healthy controls, including elevated levels of activated T cells (CD3+, CD4+, CD8+) and helper subsets (Th1, Th17, Th17.1, and Tph cells). The Tph/Treg ratio was markedly higher in SP-RA, underscoring an effector-dominant immune imbalance. FlowSOM-based clustering identified 44 unique T cell clusters, six of which were selected as discriminative T cell clusters (D-TCLs) for distinguishing SP-RA from SN-RA. TCL21, an activated Th1-type Tph-like cell, was strongly associated with SP-RA's aggressive profile, while TCL02, a central memory CD4+ T cell subset, displayed ICOS+, CTLA-4low+, PD-1low+, and CXCR3+, providing insights into immune memory mechanisms. Additionally, TCL31 and TCL35, both CD4-CD8- T cells, exhibited unique phenotypes: CD161+ for TCL31 and HLA-DR+CD38+TIM-3+ for TCL35, suggesting distinct pro-inflammatory roles. Support vector machine analysis (bootstrap n = 1000) validated the D-TCLs' discriminative power, achieving an accuracy of 86.2%, sensitivity of 85.7%, and specificity of 80.9%.
CONCLUSIONS: This study advances our understanding of immunological distinctions between SP-RA and SN-RA, identifying key T cell phenotypes as potential targets for SP-RA disease progression. These findings provide a basis for studies on targeted therapeutic strategies tailored to modulate the markers and improve treatment for SP-RA.
METHODS: We analyzed peripheral blood T cells from 50 participants, including 16 patients with untreated SP-RA, 17 patients with SN-RA, and 17 healthy controls, utilizing 25 T cell markers. For initial analysis, a dataset was established through manual T cell subset gating analysis. For advanced analysis, two distinct datasets derived from a self-organizing map algorithm, FlowSOM, were used: one encompassing all CD3+ T cells and another focusing on activated T cell subsets. Subsequently, these datasets were rigorously analyzed using adaptive least absolute shrinkage and selection operator in conjunction with leave-one-out cross-validation. This approach enhanced analysis robustness, identifying T cell clusters consistently discriminative between SP-RA and SN-RA.
RESULTS: Our analysis revealed significant differences in T cell subsets between RA patients and healthy controls, including elevated levels of activated T cells (CD3+, CD4+, CD8+) and helper subsets (Th1, Th17, Th17.1, and Tph cells). The Tph/Treg ratio was markedly higher in SP-RA, underscoring an effector-dominant immune imbalance. FlowSOM-based clustering identified 44 unique T cell clusters, six of which were selected as discriminative T cell clusters (D-TCLs) for distinguishing SP-RA from SN-RA. TCL21, an activated Th1-type Tph-like cell, was strongly associated with SP-RA's aggressive profile, while TCL02, a central memory CD4+ T cell subset, displayed ICOS+, CTLA-4low+, PD-1low+, and CXCR3+, providing insights into immune memory mechanisms. Additionally, TCL31 and TCL35, both CD4-CD8- T cells, exhibited unique phenotypes: CD161+ for TCL31 and HLA-DR+CD38+TIM-3+ for TCL35, suggesting distinct pro-inflammatory roles. Support vector machine analysis (bootstrap n = 1000) validated the D-TCLs' discriminative power, achieving an accuracy of 86.2%, sensitivity of 85.7%, and specificity of 80.9%.
CONCLUSIONS: This study advances our understanding of immunological distinctions between SP-RA and SN-RA, identifying key T cell phenotypes as potential targets for SP-RA disease progression. These findings provide a basis for studies on targeted therapeutic strategies tailored to modulate the markers and improve treatment for SP-RA.