GEO series

Asymmetry and redundancy of STAT5 paralogs across CD8+ T cell differentiation states

GSE319829 Mus musculus (mouse) RNA-seq (gene expression) 69 samples Submitted 2026/02/18 Platform GPL13112

At a glance

RNA-seq (gene expression) · mouse · 69 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: Mus musculus (mouse)
Samples: 69 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: Asymmetry and redundancy of STAT5 paralogs across CD8(+) T cell differentiation statesCommunications biology · 2026

Summary (written by the submitters)

Fostering STAT5 signaling is key to immunotherapies that leverage CD8+ T cell biology. Using mouse models, we demonstrate that the two mammalian STAT5 paralogs, STAT5A and STAT5B, are at once redundant and functionally distinct in CD8+ T cells. Thus, they are asymmetric paralogs, exhibiting both widespread homology at molecular level and functional asymmetry at cellular level, with STAT5B emerging as dominant.

Read the full summary ↓

For mechanisms, we determined that STAT5B is twice as abundant, accounting for two-thirds of the total STAT5 pool, and present evidence that it also has distinct, paralog-specific properties. We also defined cytokine- and cell state-restricted STAT5B functions and devised a core signature that spotlights key downstream properties and serves as bioinformatic probe. Together, these studies affirm the centrality of STAT5 in CD8+ T cells, reveal common and circumscribed activities for STAT5A and STAT5B, and present a unifying model that foregrounds both redundancy and asymmetry.

Published in

Asymmetry and redundancy of STAT5 paralogs across CD8(+) T cell differentiation states

Ristin S, Dalzell M, Armstrong C et al. · Communications biology 2026 · PMID 41935202 · doi:10.1038/s42003-026-09999-9

This dataset

Open GSE319829 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 GSE319829_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 PRJNA1425165. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41935202) ↗

Samples in this study

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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.