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Reversible epiblast regionalisation determines differentiation potential of human PSCs [CUT&Tag]

GSE335557 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 40 samples Submitted 2026/06/30 Platform GPL34281
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 40 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
Homo sapiens (human)
Samples
40 samples: 20 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.
Summary (written by the submitters)
Although the epiblast in the embryo has the capacity to generate all tissues of the body, its in vitro counterparts often exhibit differentiation biases, posing significant challenges for both basic research and translational applications involving pluripotent stem cells (PSCs). The origins of these biases remain incompletely understood.
Read the full summary ↓
In this study, we identify regional epiblast identity as the hallmark of differentiation competency through fluctuations in repressive and activating histone posttranslational modifications. We present a novel approach to overcome this bias using a chemical chromatin restoration (CHR) treatment. This method restores transcriptional programs, chromatin accessibility, histone modification profiles, and differentiation potential, effectively recapitulating the competent anterior epiblast-like state. Furthermore, we propose that a high bivalency state is a defining feature of the anterior human epiblast and is a characteristic of unbiased PSCs. We suggest that fluctuations in histone modification marks drive epiblast regionalization, ultimately shaping cellular responses to differentiation cues.
This dataset
Open GSE335557 in finder → Find more human 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 GSE335557_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 PRJNA1478871 and SRA study SRP709941. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
20 conditions, mostly in duplicate
Burb1-CHR_H3K27me3 ×2 Burb1-CHR_H3K4me3 ×2 Burb1-untreated_H3K27me3 ×2 Burb1-untreated_H3K4me3 ×2 Fiaj1-CHR_H3K27me3 ×2 Fiaj1-CHR_H3K4me3 ×2 Fiaj1-untreated_H3K27me3 ×2 Fiaj1-untreated_H3K4me3 ×2 +12 more

Supports a between-group comparison across 40 samples.

How we read this: 20 replicated groups read from 40 sample titles; they account for 40 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
Datasets on a similar topic

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