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RASA3 inhibits Th1 differentiation and attenuates inflammatory immune response by restricting the HCK kinase

GSE313543 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2026/07/05 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 6 conditions, mostly with about 4 replicates eachGroups 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)
Type I immunity is marked by IFN-γ, with Th1 cells as a key subset; however, intrinsic negative regulators of Th1 remain poorly defined. We identified RASA3 as such a regulator, whose expression declines during Th1 differentiation. T cell-specific RASA3 knockout enhances Th1 differentiation and exacerbates allergic contact dermatitis.
Read the full summary ↓
Mechanistically, RASA3 inhibits Hck mRNA translation via RPL36A. HCK then phosphorylates STAT4 at Y693, driving STAT4 dimerization, nuclear entry, and IFN-γ expression. This RASA3–HCK–STAT4 axis reveals a novel mechanism for Th1 negative regulation and offers potential therapeutic targets.
This dataset
Open GSE313543 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 GSE313543_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 PRJNA1380299 and SRA study SRP654746. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly with about 4 replicates each
D1_W ×4 D1_K ×4 D4_W ×4 D4_K ×4 D2_W ×4 D2_K ×4

Supports a between-group comparison across 24 samples.

How we read this: 6 replicated groups read from 24 sample titles; they account for 24 of them. 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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