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The TET2 methylcytosine dioxygenase regulates early and late transitions in exhausted CD8+ T-cell differentiation

GSE261093 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 51 samples Submitted 2025/04/14 Platform GPL21626Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 51 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
Homo sapiens (human)
Samples
51 samples: 50 distinct titles, 1 of them repeatedGroups 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
TET2 regulates early and late transitions in exhausted CD8(+) T cell differentiation and limits CAR T cell functionScience advances · 2024
Summary (written by the submitters)
Epigenetic reprogramming of CAR T-cells by targeting TET2, a methylcytosine deoxygenase that mediates active DNA demethylation, has shown therapeutic potential; however, the role of TET2 in TEX development is unclear. In both CAR T-cell exhaustion models in vitro and chronic LCMV infection in vivo, TET2 drove the conversion from memory-like, self-renewing TEX progenitors towards effector (TEFF)-like and terminally differentiated TEX.
Read the full summary ↓
TET2-deficient terminally differentiated TEX retained aspects of TEX progenitor biology, including decreased expression of the transcription factor TOX, suggesting that TET2 is required for terminal exhaustion.
Published in
TET2 regulates early and late transitions in exhausted CD8(+) T cell differentiation and limits CAR T cell function
Dimitri AJ, Baxter AE, Chen GM et al. · Science advances 2024 · PMID 39536093 · doi:10.1126/sciadv.adp9371
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 GSE261093_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 PRJNA1085279 and SRA study SRP493938. Searching any of these in the dataset finder brings you back here.

Study design
50 distinct titles, 1 of them repeated
WT_Negative_A_S ×2

How we read this: Read from 51 sample titles: 50 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

+ 11 more — browse all 51 samples with per-sample file links →

Datasets on a similar topic

Search all 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.