Data from: Inflammatory arthritis irAE may represent a unique autoimmune disease primarily driven by T cells, but likely not autoantibodies
Data files
Mar 17, 2026 version files 27.81 MB
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Auxiliary_Data_1_Individual_UMAP.tiff
1.50 MB
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Auxiliary_Data_2_Individual_Top_100_clones.tif
475.07 KB
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Auxiliary_Data_3_heatmap_from_scRNAseq_analysis.jpg
25.79 MB
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Auxillary_Table_1_gene_marker_top20.csv
35.29 KB
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Auxillary_Table_2_cell_counts_patient.csv
1.08 KB
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README.md
2.95 KB
Abstract
The underlying immunopathogenesis of inflammatory arthritis (IA) immune-related adverse event (irAE) remains obscure. Unlike rheumatoid arthritis (RA), where autoantibodies and B-cell dysfunction are central features, the contribution of humoral immunity to IA-irAE is unclear. Here, we performed immunophenotyping of peripheral blood from IA-irAE patients and compared them with seronegative RA patients, ICI-treated patients without irAE, and healthy controls. IA-irAE was marked with increased cytotoxic gene expression and metabolic activation in T cells, and reduced CXCR3 and CCR6 expression in CD4⁺ T cells. Contrary to seronegative RA, IA-irAE patients displayed no significant elevation in autoantibody levels or atypical CD11c⁺CD21⁻ B cells. IA-irAE was further characterized by elevated levels of IL-6, IL-12, and type I IFN, which correlated with the T cell activation phenotypes. Altogether, our findings define IA-irAE as a disease with certain immunological features distinctive from RA, representing a potentially T cell-driven, autoantibody-independent autoimmunity. These results offer insights into immune tolerance breakdown and therapeutic targeting in irAEs.
Dataset DOI: 10.5061/dryad.fxpnvx167
Description of the data and file structure
We did single cell sequencing data on 4 groups of patients (HC, RAC, irAE and ICI) using the PBMCs. UMAP, Top 100 TCR clones and top 20 genes of each individual were presented. We also included gene maker of each cluster and cell counts in each cell type.
Files and variables
File: Auxiliary_Data_3_heatmap_from_scRNAseq_analysis.jpg
Description: Gene expression heatmap from scRNAseq analysis for each cell type.
File: Auxiliary_Data_1_Individual_UMAP.tiff
Description: Individual UMPA for each patient
File: Auxiliary_Data_2_Individual_Top_100_clones.tif
Description: Top 100 TCR clones distribution in T cells
File: Auxillary_Table_2_cell_counts_patient.csv
Description: Cell counts for each cell type
Variables
- Study Group: Defined by our Group
- HC1: Healthy control
- HC2: Healthy control
- HC3: Healthy control
- HC4: Healthy control
- HC5: Healthy control
- HC6: Healthy control
- irAE1: immune-related adverse events
- irAE2: immune-related adverse events
- irAE3: immune-related adverse events
- irAE4: immune-related adverse events
- irAE5: immune-related adverse events
- RAC1:Rheumatoid arthritis control
- RAC2:Rheumatoid arthritis control
- RAC3:Rheumatoid arthritis control
- RAC4:Rheumatoid arthritis control
- RAC5:Rheumatoid arthritis control
- RAC6:Rheumatoid arthritis control
- ICI1: immune checkpoint inhibitor control
- ICI2: immune checkpoint inhibitor control
File: Auxillary_Table_1_gene_marker_top20.csv
Description:
Variables
- Number: gene number
- p_val: p value
- avg_log2FC: average Log2 Foldchange
- pct.1: the percentage of cells expressing the gene/feature in group 1
- pct.2: the percentage of cells expressing the gene/feature in group 2
- p_val_adj: adjusted p value
- cluster: cell type
- gene: gene name
Code/software
FASTQ files generated from the Gene Expression (GEX) and Feature Barcode (TCR)**** libraries were processed using the 10x Genomics Cell Ranger multi pipeline (v7.0.0) to create expression matrices for downstream analysis. Integrated single-cell analysis of gene expression and T cell receptor data (scGEXseq + scTCRseq) was performed using the Immunopipe package (v1.4.0).
Access information
Other publicly accessible locations of the data:
- GSE322576
Data was derived from the following sources:
- Azimuth
Human subjects data
In this study, explicit consent was obtained from all patients, and samples were collected only after patients had provided consent to participate in the study. Meanwhile, we only use the study ID named by our group for all of the patients, thus the data were all de-identified.
