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Mapping self-associating chromatin hubs identifies Id proteins as key determinants of exhausted T cell fate

GSE310322 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 37 samples Submitted 2026/04/20 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 37 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
37 samples: 8 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.
Summary (written by the submitters)
This study aims to define how Id2 and Id3 regulate exhausted CD8+ T cell fate. We found that Id2 deletion reduced the Tex-eff population, whereas Id3 ablation in CD4-Cre mice diminished Tpex cells, indicating subset-specific roles for Id2 and Id3. Mechanistically, E2A is a major target of Id2/3, and Runx3 interacts with E2A. Pulldown of Id2/3 recovered both E2A and Runx3, supporting an Id2/3?E2A?Runx3 regulatory complex.
Read the full summary ↓
To map the genomic occupancy of Id2 and Id3, we expressed 3×T7-tagged Id2 or Id3 in in vitro-primed CD45.2+ P14 cells, adoptively transferred these cells into CD45.1 recipient mice, and infected the recipients with LCMV Clone 13. On day 6 post-infection, Tex-eff and Tpex cells were isolated for T7 CUT&RUN to define Id2 binding in Tex-eff cells and Id3 binding in Tpex cells, with the empty T7 vector backbone used as a negative control.
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 GSE310322_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1365760 and SRA study SRP647844. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly in triplicate
Id3 WT, Stem, ATAC, ×3 Id3 WT, Term, ATAC, ×3 Id3 KO, Stem, ATAC, ×3 Id3 KO, Term, ATAC, ×3 Id2 WT, Stem, ATAC, ×3 Id2 WT, Term, ATAC, ×3 Id2 KO, Stem, ATAC, ×3 Id2 KO, Term, ATAC, ×3 +13 more

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 37 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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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.