← BioTransfer GEO Dataset Finder
GEO series

TDG orchestrates ATF4-dependent gene transcription during retinoic acid-induced cell lineage committment

GSE262587 Mus musculus (mouse) RNA-seq (gene expression) 36 samples Submitted 2024/03/31 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 36 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
36 samples: 12 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
TDG orchestrates ATF4-dependent gene transcription during retinoic acid-induced cell fate acquisitionNucleic acids research · 2026
Summary (written by the submitters)
Cell differentiation during development is associated to large-scale modifications in the methylome that require the engagement of an active DNA demethylation machinery including Ten-Eleven-Translocation enzymes for oxidation of 5-methylcytosine and the T:G mismatch DNA glycosylase (TDG) for removal of the oxidized bases. Despite this well-defined molecular function, the biological output of TDG activity remains elusive.
Read the full summary ↓
Here we combined transcriptomic and epigenomic approaches in TDG knock-out embryonal carcinoma cells, an epiblast stem-like cell model, to decipher TDG function in pluripotent cells and their retinoic acid-induced differentiated progeny. We determined that TDG activity is balancing differentiation in favor of a neural fate at the expense of a cardiac mesoderm fate. This process is associated with a sustained activity of a large set of ATF4-dependent genes in relation with TDG engagement at the Atf4 gene promoter and in conjunction with a TDG-dependent regulation of the mammalian target of rapamycin complex 1. These observations highlight the central role of TDG in cell differentiation and support a model linking metabolic reprogramming to cell fate acquisition.
Published in
TDG orchestrates ATF4-dependent gene transcription during retinoic acid-induced cell fate acquisition
Turpin M, Madigou T, Bizot M et al. · Nucleic acids research 2026 · PMID 41773019 · doi:10.1093/nar/gkag152
This dataset
Open GSE262587 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 GSE262587_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 PRJNA1092601 and SRA study SRP498133. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41773019) ↗
Study design
12 conditions, mostly in triplicate
wt_P19_ECC_monolayer ×3 wt_P19_ECC_monolayer_+RA_48hrs ×3 wt_P19_ECC_aggregates ×3 wt_P19_ECC_aggregates_+RA_48hrs ×3 TDG-null_P19_ECC_monolayer ×3 TDG-null_P19_ECC_monolayer_+RA_48hrs ×3 TDG-null_P19_ECC_aggregates ×3 TDG-null_P19_ECC_aggregates_+RA_48hrs ×3 +4 more

Supports a between-group comparison across 36 samples.

How we read this: 12 replicated groups read from 36 sample titles; they account for 36 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

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.