GEO series

Dissection of the immune landscape in Psoriatic Arthritis patients [Mouse scRNA-seq]

GSE295180 Mus musculus (mouse) Single-cell RNA-seq 52 samples Submitted 2025/07/23 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 52 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 52 samples
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 GSE295180 in finder → Find more mouse 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 GSE295180_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 PRJNA1253673 and SRA study SRP580329. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40478933) ↗

Study design

A cohort of 52 individual samples

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

How we read this: Sample titles run AB18599 … AB18650 and carry no group information.

Samples in this study

GSM8943250 AB18599
GSM8943251 AB18600
GSM8943252 AB18601
GSM8943253 AB18602
GSM8943254 AB18603
GSM8943255 AB18604
GSM8943256 AB18605
GSM8943257 AB18606
GSM8943258 AB18607
GSM8943259 AB18608
GSM8943260 AB18609
GSM8943261 AB18610
GSM8943262 AB18611
GSM8943263 AB18612
GSM8943264 AB18613
GSM8943265 AB18614
GSM8943266 AB18615
GSM8943267 AB18616
GSM8943268 AB18617
GSM8943269 AB18618
GSM8943270 AB18619
GSM8943271 AB18620
GSM8943272 AB18621
GSM8943273 AB18622
GSM8943274 AB18623
GSM8943275 AB18624
GSM8943276 AB18625
GSM8943277 AB18626
GSM8943278 AB18627
GSM8943279 AB18628
GSM8943280 AB18629
GSM8943281 AB18630
GSM8943282 AB18631
GSM8943283 AB18632
GSM8943284 AB18633
GSM8943285 AB18634
GSM8943286 AB18635
GSM8943287 AB18636
GSM8943288 AB18637
GSM8943289 AB18638

+ 12 more — browse all 52 samples with per-sample file links →

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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.