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Dissection of the immune landscape in Psoriatic Arthritis patients

GSE277596 Homo sapiens (human) Single-cell RNA-seq 635 samples Submitted 2025/06/12 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 635 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
635 samples: 635 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.
Paper
Dissection of the immune landscape in psoriatic arthritis defines immunoproteasome up-regulation in treatment resistanceScience immunology · 2025
Summary (written by the submitters)
Despite significant advancements in psoriatic arthritis (PsA) treatment modalities, a considerable proportion of patients continue to experience persistent joint inflammation and functional impairment, unresponsive to the armamentarium of biological disease-modifying antirheumatic drugs (bDMARDs) or Janus kinase (JAK) inhibitors. The etiology of resistance in these cases is complex, likely involving intricate immunological and genetic factors which are currently mostly unknown.
Read the full summary ↓
Identifying novel biomarkers and targets for refractory disease is urgently needed. Here, utilizing single-cell RNA-sequencing of synovial fluid from 41 PsA patients and 9 Osteoarthritis (OA) controls we map the comprehensive immune landscape of PsA. Our analysis reveals major perturbations within the myeloid compartment, identifying distinct cell subsets and altered gene programs in PsA patients. Notably, within the myeloid compartment, refractory patients exhibit elevated expression of interferon- gamma induced gene programs linked to the immunoproteasome and MHC class I antigen presentation, at both the gene and protein level. These results suggest a potential interaction of the immunoproteasome and MHC class I antigen presentation in refractory PsA patients with sustained inflammation. Our research imparts critical insights into PsA pathogenesis, paving the way for both molecular level patient classification and potentially new therapeutic interventions for currently treatment resistant patients.
Published in
Dissection of the immune landscape in psoriatic arthritis defines immunoproteasome up-regulation in treatment resistance
Tzemach R, Gur C, Phan TS et al. · Science immunology 2025 · PMID 40478933 · doi:10.1126/sciimmunol.adu0284
This dataset
Open GSE277596 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 GSE277596_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 PRJNA1162877 and SRA study SRP533716. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40478933) ↗
Study design
635 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 the first 400 of 635 sample titles: 400 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

+ 595 more — browse all 635 samples with per-sample file links →

Datasets on a similar topic

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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.