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Pre-existing epigenetic state and differential NF-kB activation shape type 2 immune cell responses

GSE292063 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 61 samples Submitted 2025/03/21 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 61 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
61 samples: 24 conditions, mostly in triplicateGroups 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
Pre-existing epigenetic state and differential NF-κB activation shape type 2 immune cell responsesImmunity · 2025
Summary (written by the submitters)
CD4+ T helper 2 (Th2) cells and their innate counterpart, group 2 innate lymphoid cells (ILC2s), drive type 2 immune responses, which protect against helminth infection and induce allergic inflammation. Although both cell types express an overlapping set of effector molecules, the identity of key transcription factors and how they differentially regulate gene expression in each cell type has not been determined.
Read the full summary ↓
Here, we demonstrate that the transcription factor NF-kB is induced upon stimulation by the alarmin IL-33 in ILC2s and by TCR engagement in Th2 cells, yet NF-kB is essential for the function and expression of type 2 cytokines in ILC2s but not in Th2 cells. In ILC2s, the regulatory regions of key type 2 genes exist in an open chromatin conformation, which can be rapidly occupied by NF-kB following IL-33 stimulation. However, the same regulatory regions are less accessible in unstimulated Th2 cells, and other TCR-induced transcription factors such as NFAT play a more important role in regulating type 2 cytokine expression in Th2 cells. Thus, our results demonstrate a critical role of NF-kB in modulating type 2 immunity in a cell type-specific manner and reveal a remarkable plasticity of innate and adaptive lymphocytes in the use of pre-existing epigenetic states to differentially regulate the same set of effector genes.
Published in
Pre-existing epigenetic state and differential NF-κB activation shape type 2 immune cell responses
Guichard V, Leão FB, Zhao J et al. · Immunity 2025 · PMID 40367949 · doi:10.1016/j.immuni.2025.04.016
This dataset
Open GSE292063 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE292063_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 PRJNA1235125 and SRA study SRP569835. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40367949) ↗
Study design
24 conditions, mostly in triplicate
RNA_ILC2_p65KO_IL33 ×3 RNA_ILC2_p65KO_ns ×3 RNA_ILC2_RelKO_IL33 ×3 RNA_ILC2_RelKO_ns ×3 RNA_ILC2_WT_IL33 ×3 RNA_ILC2_WT_ns ×3 ATAC_ILC2_IL33 ×3 ATAC_ILC2_ns ×3 +16 more

Supports a between-group comparison across 61 samples.

How we read this: 24 replicated groups read from 61 sample titles; they account for 61 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

+ 21 more — browse all 61 samples with per-sample file links →

Datasets on a similar topic

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