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H3K9 di-methylation dynamics underlies mouse minor zygotic genome activation [RNA-seq of mESC]

GSE296993 Mus musculus (mouse) DNA methylation 50 samples Submitted 2026/04/19 Platform GPL24247
At a glance

DNA methylation · mouse · 50 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Mus musculus (mouse)
Samples
50 samples: 25 conditions, mostly in duplicateGroups 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
Dynamic regulation of H3K9 dimethylation drives mouse minor zygotic genome activationNature structural & molecular biology · 2026
Summary (written by the submitters)
Minor zygotic genome activation (ZGA) is crucial for early development and totipotency acquisition, yet the regulatory mechanisms driving minor ZGA genes remain elusive. Here we show that dynamic regulation of H3K9me2 is essential for minor ZGA. H3K9me2 levels at minor ZGA gene loci are reduced at early 2-cell and are re-established by morula.
Read the full summary ↓
Maternal depletion of the H3K9 demethylases KDM3A and KDM3B leads to increased H3K9me2 and impaired minor ZGA in early 2-cell, followed by developmental arrest at 2- to 4-cell. In mESCs, H3K9 at minor ZGA loci is highly di-methylated; combined loss of the H3K9 methyltransferases G9a and SETDB1 leads to synergistic de-repression of minor ZGA genes. Mechanistically, SETDB1 specifically targets Dux, while G9a broadly represses minor ZGA genes through H3K9 di-methylation linked to lamina-associated heterochromatin formation. These findings establish H3K9me2 dynamics as a key regulator for minor ZGA, highlighting the indispensable role of epigenetic control in early embryogenesis.
Published in
Dynamic regulation of H3K9 dimethylation drives mouse minor zygotic genome activation
Maeda R, Kuroki S, Shimojo H et al. · Nature structural & molecular biology 2026 · PMID 42191875 · doi:10.1038/s41594-026-01811-w
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 GSE296993_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 beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1262337 and SRA study SRP584872. Searching any of these in the dataset finder brings you back here.

Study design
25 conditions, mostly in duplicate
RNA_mESC_33-6_NT_control ×2 RNA_mESC_33-6_OHT_Setdb1-KO ×2 RNA_mESC_SE_NT_Suv39h1/2-DKO ×2 RNA_mESC_SE_OHT_Suv39h1/2;Setdb1-TKO ×2 RNA_mESC_SG_NT_G9a-KO ×2 RNA_mESC_SG_OHT_G9a;Setdb1-DKO ×2 RNA_mESC_3S1G_NT_G9a;Suv39h1/2-TKO ×2 RNA_mESC_3S1G_OHT_G9a;Suv39h1/2;Setdb1-QKO ×2 +17 more

Supports a between-group comparison across 50 samples.

How we read this: 25 replicated groups read from 50 sample titles; they account for 50 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

+ 10 more — browse all 50 samples with per-sample file links →

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