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Addressing the specific roles of histone modifications in transcriptional repression [RNA-seq]

GSE263060 Mus musculus (mouse) RNA-seq (gene expression) 105 samples Submitted 2025/10/14 Platform GPL30172Platform GPL21626
At a glance

RNA-seq (gene expression) · mouse · 105 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
105 samples: 35 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
Addressing the specific roles of histone modifications in transcriptional repressionNature communications · 2025
Summary (written by the submitters)
Our understanding of epigenetic processes is based on the hypothesis that individual posttranslational modifications of DNA and histones, or combinations thereof, function to direct unique downstream effects on transcription. Still, histone modifications are broadly categorized as repressive or activating, raising the question of potential functional redundancy.
Read the full summary ↓
Here, we present an approach for addressing this question by substituting the genome-wide H3K27me3 pattern with other histone modifications. By taking advantage of the modular organization of PRC2, we direct de novo recruitment of H3K9me3 and H3K36me3 to PRC2 target genes in H3K27me3 null mouse embryonic stem cells (mESCs). We show that despite accurate genome-wide re-establishment of H3K36me3 at PRC2 target genes, which leads to significant reduction in H3K4me3 levels, the remaining H3K4me3 prevents H3K36me3 from recruiting sufficient DNA methylation to substitute for H3K27me3-mediated repression. In contrast, we demonstrate that H3K9me3 is more efficient in repressing H3K27me3 regulated genes, however this repression is also contingent on H3K4me3 status. Taken together, these results highlight the unique repressive functions of H3K27me3 and suggest that the functional effects of individual posttranslational modifications are highly dependent on the interplay with the existing chromatin environment.
Published in
Addressing the specific roles of histone modifications in transcriptional repression
Hedehus L, Mas AM, Alpsoy A et al. · Nature communications 2025 · PMID 41274922 · doi:10.1038/s41467-025-66426-z
This dataset
Open GSE263060 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 GSE263060_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 PRJNA1095527 and SRA study SRP499359. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41274922) ↗
Study design
35 conditions, mostly in triplicate
RNAseq_S12NSUV2dead_KDM4i_50nM ×3 RNAseq_S12NSUV2dead_KDM4i_100nM ×3 RNAseq_S12NSUV2dead ×3 RNAseq_S12NSUV2_KDM4i_50nM ×3 RNAseq_S12NSUV2_KDM4i_100nM ×3 RNAseq_S12NSUV2 ×3 RNAseq_Suz12KO_S12N ×3 RNAseq_Suz12KO_S12NSD2 ×3 +27 more

Supports a case/control comparison: 21 samples read as cases, 18 as controls.

How we read this: 35 replicated groups read from 105 sample titles; they account for 105 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

+ 65 more — browse all 105 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.