GEO series

The transcription factor BATF pioneers the differentiation program of cytolytic effector CD8+ T cells through the direct interaction with IRF4 (RNA-seq)

GSE281500 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2025/07/09 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 10 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: 10 samples: 3 × BatfKO_Day3 vs 3 × WT_Day3 vs 2 × BatfKO_Day0 vs 2 × WT_Day0Groups 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: The transcription factor BATF pioneers the differentiation program of effector CD8(+) T cells through the interaction with IRF4Cell reports · 2025

Summary (written by the submitters)

The transcription factor BATF plays critical roles in the differentiation of various immune cells, including CD8+ T cells. Here, we demonstrated that BATF controls epigenomic and transcriptomic reprogramming of CD8+ T cells at an early phase of acute viral infection, thereby promoting the differentiation of cytolytic effector CD8+ T cells.

Read the full summary ↓

Loss of BATF drastically perturbed gene expression, chromatin accessibility, and the bindings of key transcription factors including Jun, T-bet, and IRF4. The direct interaction with IRF4 was essential for BATF-mediated effector differentiation, as the BATF mutant lacking this interaction failed to induce proper chromatin remodeling and proliferation of antigen-specific CD8+ T cells. Notably, IRF4 binding was exhaustively dependent on BATF, whereas BATF retained binding capacity even in IRF4-deficient CD8+ T cells. Furthermore, BATF initiated chromatin remodeling in the absence of IRF4, whereas subsequent dynamic epigenomic reorganization required IRF4. Our data proposed that BATF serves as a “pioneer transcription factor” spearheading the reorganization of chromatin architecture upon antigen encounter, followed by further rearrangement of epigenomic and transcriptomic landscapes through the cooperation with IRF4.

Published in

The transcription factor BATF pioneers the differentiation program of effector CD8(+) T cells through the interaction with IRF4

Fujisawa S, Tanabe Y, Hojo A et al. · Cell reports 2025 · PMID 40773348 · doi:10.1016/j.celrep.2025.116129

This dataset

Open GSE281500 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 GSE281500_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 PRJNA1184150 and SRA study SRP544224. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40773348) ↗

Study design

3 × BatfKO_Day3 vs 3 × WT_Day3 vs 2 × BatfKO_Day0 vs 2 × WT_Day0

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 10 sample titles; they account for 10 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

GSM8622053 BatfKO_Day0_1 BatfKO_Day0
GSM8622054 BatfKO_Day0_2 BatfKO_Day0
GSM8622055 WT_Day0_1 WT_Day0
GSM8622056 WT_Day0_2 WT_Day0
GSM8622057 BatfKO_Day3_1 BatfKO_Day3
GSM8622058 BatfKO_Day3_2 BatfKO_Day3
GSM8622059 BatfKO_Day3_3 BatfKO_Day3
GSM8622060 WT_Day3_1 WT_Day3
GSM8622061 WT_Day3_2 WT_Day3
GSM8622062 WT_Day3_3 WT_Day3

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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.