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Chromatin-dependent motif syntax defines differentiation trajectories [RNA-seq]

GSE272534 Mus musculus (mouse) RNA-seq (gene expression) 99 samples Submitted 2025/08/07 Platform GPL24247Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 99 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
99 samples: 83 distinct titles, 14 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
Chromatin-dependent motif syntax defines differentiation trajectoriesMolecular cell · 2025
Summary (written by the submitters)
Transcription factors (TFs) recognizing DNA motifs within regulatory regions drive cell identity. Despite recent advances, their specificity remains incompletely understood. Here, we address this by contrasting two TFs, Neurogenin-2 (NGN2) and MyoD1, which recognize ubiquitous E-box motifs yet drive distinct cell fates toward neurons and muscles, respectively.
Read the full summary ↓
Upon induction in mouse embryonic stem cells, we monitor binding across differentiation, employing an interpretable machine learning approach that integrates preexisting DNA accessibility. This reveals a chromatin-dependent motif syntax, delineating both common and factor-specific binding, validated by cellular and in vitro assays. Shared binding sites reside in open chromatin, locally influenced by nucleosomes. In contrast, factor-specific binding in closed chromatin involves NGN2 and MyoD1 acting as pioneer factors, influenced by motif variant frequencies, motif spacing, and interaction partners, which together account for subsequent lineage divergence. Transferring our methodology to other models demonstrates how a combination of opportunistic binding and context-specific chromatin-opening underpin TF specificity, driving differentiation trajectories.
Published in
Chromatin-dependent motif syntax defines differentiation trajectories
Durdu S, Iskar M, Isbel L et al. · Molecular cell 2025 · PMID 40780181 · doi:10.1016/j.molcel.2025.07.005
This dataset
Open GSE272534 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 GSE272534_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 PRJNA1137312 and SRA study SRP520749. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40780181) ↗
Study design
83 distinct titles, 14 of them repeated
RNAseq_NGN2_6h_r1 ×3 RNAseq_NGN2_6h_r2 ×3 RNAseq_NGN2_0h_r1 ×2 RNAseq_NGN2_0h_r2 ×2 RNAseq_NGN2_12h_r1 ×2 RNAseq_NGN2_12h_r2 ×2 RNAseq_NGN2_24h_r1 ×2 RNAseq_NGN2_24h_r2 ×2 +6 more

How we read this: Read from 99 sample titles: 83 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

+ 59 more — browse all 99 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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