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Step-wise signal integration governs adaptive programming of lymphocytes [bulkRNA-seq and ATAC-seq]

GSE292377 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 41 samples Submitted 2026/01/22 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 41 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
41 samples: 41 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
Stepwise epigenetic signal integration drives adaptive programming of cytotoxic lymphocytesImmunity · 2026
Summary (written by the submitters)
Lymphocyte differentiation depends on activation via antigen and cytokines during the immune response to infection. How the timing and integration of these signals program the epigenetic and functional fate of these cells is not completely understood. In this study, we find that inflammatory cytokine signals received by innate and adaptive lymphocytes have a context-dependent role for immune memory formation.
Read the full summary ↓
Without preceding and sufficient antigen receptor signaling, inflammatory cytokines drive terminal differentiation into short-lived effector cells. In contrast, sufficient antigen-receptor signaling redirects inflammatory cytokine signals to promote memory differentiation via cooperation of STAT and AP-1 transcription factors. By this crucial epigenetic mechanism, optimally equipped lymphocytes are selected for memory formation rather than a terminal effector cell fate. Whereas T cells are hardwired to be shielded from premature inflammatory signals, NK cells rely on coincidental early antigen receptor signaling for adaptive responses. Together, step-wise integration of antigen and cytokine signaling optimizes both effector and memory differentiation, allowing for promiscuous recruitment into the acute immune response while promoting avidity maturation in memory populations of both innate and adaptive lymphocytes.
Published in
Stepwise epigenetic signal integration drives adaptive programming of cytotoxic lymphocytes
Grassmann S, Santosa EK, Kim H et al. · Immunity 2026 · PMID 41619729 · doi:10.1016/j.immuni.2026.01.004
This dataset
Open GSE292377 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 GSE292377_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1238266 and SRA study SRP571580. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41619729) ↗
Study design
41 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 41 sample titles: 41 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

+ 1 more — browse all 41 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.