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Malat1 regulates female Th2 cell cytokine expression through controlling early differentiation and response to IL-2

GSE278413 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2025/08/18 Platform GPL26624
At a glance

RNA-seq (gene expression) · mouse · 16 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
16 samples: 4 × WT_Female_Day0 vs 4 × WT_Female_Day2 vs 4 × KO_Female_Day0 vs 4 × KO_Female_Day2Groups 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
Malat1 regulates female Th2 cell cytokine expression through controlling early differentiation and response to IL-2Journal of immunology (Baltimore, Md. : 1950) · 2025
Summary (written by the submitters)
Transcriptomic profiling of Malat1-deficient and WT CD4+ T cells at Day 0 (naïve CD4+ T cells) and Day 2 post-induction of differentiation under Th2 conditions. Naive CD4+ T cells isolated from spleens and lymphnodes of female mice. For Th2 polarisation, purified naïve CD4+ T cells (500,000 cells per well) were stimulated with 10 μg/ml plate-bound anti-CD3ε (clone 145-2C11) and 4 μg/ml soluble anti-CD28 (37.51) in RPMI 1640 in flat-bottom 96-well plates in the presence of 25 ng/ml mouse rIL-4 and 5 μg/ml anti–IFN-γ (XMG1.2).
Published in
Malat1 regulates female Th2 cell cytokine expression through controlling early differentiation and response to IL-2
Gwynne M, West KA, van Dongen S et al. · Journal of immunology (Baltimore, Md. : 1950) 2025 · PMID 40865984 · doi:10.1093/jimmun/vkaf177
This dataset
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.

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 GSE278413_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1167203 and SRA study SRP535710. Searching any of these in the dataset finder brings you back here.

Study design
4 × WT_Female_Day0 vs 4 × WT_Female_Day2 vs 4 × KO_Female_Day0 vs 4 × KO_Female_Day2

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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
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