GEO series

Deciphering histone mark-specific fine-scale chromatin organization at high resolution with Micro-C-ChIP

GSE246346 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 11 samples Submitted 2025/09/05 Platform GPL30173Platform GPL30172

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 11 samples

What it is: ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism: Mus musculus (mouse)
Samples: 11 samples: 11 samples, each with a distinct title — no replicated groupsGroups 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: Deciphering histone mark-specific fine-scale chromatin organization at high resolution with Micro-C-ChIPNature communications · 2025

Summary (written by the submitters)

The regulation of cell-type-specific transcription programs is a highly controlled and complex process that needs to be fully understood. The gene regulation is often influenced by distal regulatory elements and their interactions with promoters in three-dimensional space. Although proximity ligation techniques like Hi-C have revolutionized our understanding of genome organization, the genomic resolution for many of these methods is limited by both experimental and financial constraints.

Read the full summary ↓

Here, we introduce Micro-C-ChIP to provide extremely high-resolution views of chromosome architecture at genomic loci marked by specific covalent histone modifications. This is achieved by affinity isolation of specific chromatin states in order to target chromosome folding libraries to focus on chromatin domains (regulatory elements, heterochromatin, etc.) of interest, yielding extremely high sequencing depth at these loci. We applied Micro-C-ChIP to mouse embryonic stem cells (mESC) and hTERT-immortalized human retinal epithelial cells (hTERT-RPE1), revealing architectural features of genome organization with comparable or higher resolution than Micro-C datasets sequenced to 10X higher depth. In particular, we discovered extensive promoter-promoter networks in both cell types and characterized the specific architecture of bivalently bookmarked promoters in mESC. Together, these data highlight Micro-C-ChIP as a cost-effective approach to exploring the landscape of genome folding at extraordinarily high resolution.

Published in

Deciphering histone mark-specific fine-scale chromatin organization at high resolution with Micro-C-ChIP

Metelova M, Vigh ML, Krietenstein N · Nature communications 2025 · PMID 41125591 · doi:10.1038/s41467-025-64350-w

This dataset

Open GSE246346 in finder → Find more ChIP / ATAC / CUT&Tag 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 GSE246346_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1031830 and SRA study SRP468255. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41125591) ↗

Study design

11 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 11 sample titles: 11 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

GSM7867523 mESC_H3K4me3_B1T1S1
GSM7867524 mESC_H3K4me3_B2T1S1
GSM7867525 mESC_H3K27me3_B1T1S1
GSM7867526 mESC_H3K27me3_B2T1S1
GSM7867527 mESC_H3K27me3_B2T2S1
GSM7867528 RPE1_H3K4me3_B1T1S1
GSM7867529 RPE1_H3K4me3_B2T1S1
GSM7867530 RPE1_H3K27me3_B1T1S1
GSM7867531 RPE1_H3K27me3_B2T1S1
GSM8900865 MC-ChIP_HCT116_H3K4me3_UT
GSM8900866 MC-ChIP_HCT116_H3K4me3_IAA

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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.