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RNA-seq analysis of neuronal differentiation of KAT6A/B knockout mouse embryonic stem cells

GSE250570 Mus musculus (mouse) RNA-seq (gene expression) 48 samples Submitted 2024/12/27 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 48 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
48 samples: 16 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
Nucleosomal asymmetry shapes histone mark binding and promotes poising at bivalent domainsMolecular cell · 2025
Summary (written by the submitters)
Promoters of developmental genes in embryonic stem cells (ESCs) are marked by histone H3 lysine 4 trimethylation (H3K4me3) and H3K27me3 in an asymmetric nucleosomal conformation, with each sister histone H3 carrying one of the two marks. These so-called bivalent domains are thought to poise genes for timely activation upon differentiation.
Read the full summary ↓
Here we show that asymmetric bivalent nucleosomes recruit repressive H3K27me3 binders but fail to enrich activating H3K4me3 binders, despite presence of H3K4me3, thereby promoting a poised state. Strikingly, the bivalent mark combination further promotes binding of chromatin proteins that are not recruited by each mark individually, including the histone acetyltransferase complex KAT6B (MORF). Knockout of KAT6B blocks neuronal differentiation, demonstrating that bivalency-specific readers are critical for proper ESC differentiation. These findings reveal how histone mark bivalency directly promotes establishment of a poised state at developmental genes, while highlighting how nucleosomal asymmetry is critical for histone mark readout and function.
Published in
Nucleosomal asymmetry shapes histone mark binding and promotes poising at bivalent domains
Bryan E, Valsakumar D, Idigo NJ et al. · Molecular cell 2025 · PMID 39731917 · doi:10.1016/j.molcel.2024.12.002
This dataset
Open GSE250570 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 GSE250570_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 PRJNA1054553 and SRA study SRP478940. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39731917) ↗
Study design
16 conditions, mostly in triplicate
E14_d0 ×3 E14_d15 ×3 E14_d2 ×3 E14_d6 ×3 KAT6A_KO_d0 ×3 KAT6A_KO_d15 ×3 KAT6A_KO_d2 ×3 KAT6A_KO_d6 ×3 +8 more

Supports a between-group comparison across 48 samples.

How we read this: 16 replicated groups read from 48 sample titles; they account for 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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