← BioTransfer GEO Dataset Finder
GEO series

Immune hallmarks of recurrent immune checkpoint inhibitor-mediated inflammatory arthritis

GSE338460 Homo sapiens (human) Single-cell RNA-seq 25 samples Submitted 2026/07/25 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 25 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
25 samples: 25 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Immune checkpoint inhibitor (ICI) therapy is often associated with immune-related adverse events including inflammatory arthritis (ICI-IA). However, the mechanisms underlying ICI-IA, especially its recurrence, have not been fully investigated. The purpose of this study is to elucidate mechanisms of recurrent ICI-IA by analyzing longitudinal synovial fluid (SF) samples.
Read the full summary ↓
SF samples were collected from six ICI-IA patients at the first and second occurrences of ICI-IA and analyzed with single-cell RNA sequencing (scRNAseq) (n=3), scTCRseq (n=3), scBCRseq (n=3), and flow cytometry (n=6). SF samples from cancer-naïve osteoarthritis patients (n=6) were used as negative controls. Analysis revealed that effector CD8+ T cells and PD-1hi CXCL13hi CD4+ T cells were enriched in the SF of ICI-IA patients. Ninety three percent and fifty percent of the top ten expanded clones of effector CD8+ T cells and PD-1hi CXCL13hi CD4+ T cells, respectively, were shared between the first and second ICI-IA flare. These top clones were characterized by the production of pro-inflammatory type 1 cytokines including IFNg, TNFa, and IL-21, especially in the second flare, suggesting immune memory responses to cognate antigen. Cell-cell communication analysis suggests that effector CD8+ T cells and PD-1hi CXCL13hi CD4+ T cells interacted with each other and with myeloid cells and B cells through chemokines (CXCL9/10/11/13, CCL3) and cytokines (MIF, IL-2/7/15/21). Overall, longitudinal SF analysis from ICI-IA patients for the first time revealed the expansion of effector CD8+ T cells and PD-1hi CXCL13hi CD4+ T cells with type 1 cytokine signatures that potentially contribute to development or recurrence of ICI-IA.
This dataset
Open GSE338460 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE338460_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1494521 and SRA study SRP717645. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
25 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 25 sample titles: 25 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.