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IL-9 in psoriasis: human endothelial cells and murine T cells

GSE312663 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/12/26 Platform GPL24247Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 18 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
18 samples: 18 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
Th9-arterial endothelial cell crosstalk promotes psoriatic atherosclerosisAnnals of the rheumatic diseases · 2026
Summary (written by the submitters)
Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death in patients wiht systemic autoimmune and inflammatory diseases. The inflammatory cytokine interleukin 9 (IL-9) has been linked to ASCVD atherogenesis, raising fundamental questions about the populations in which and the mechanisms by which IL-9 drives ASCVD, leading us to investigate IL-9 in psoriasis.
Read the full summary ↓
This incuded mRNA sequencing of IL-9+ and IL-9- circulating CD4+ T cells from INFER (IL-9 reporter) mice treated with imiquimod to induce psoriatic inflammation, as well as vehicle vs. IL-9-treated primary human aortic endothelial cells
Published in
Th9-arterial endothelial cell crosstalk promotes psoriatic atherosclerosis
Baral I, Baumer Y, Burma AM et al. · Annals of the rheumatic diseases 2026 · PMID 41390303 · doi:10.1016/j.ard.2025.11.008
This dataset
Open GSE312663 in finder → Find more 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 GSE312663_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1375675 and SRA study SRP651737. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41390303) ↗
Study design
18 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 18 sample titles: 18 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

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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.