GEO series

Genome-wide profiling of histone modification H3K27ac and CTCF transcription factor binding in Rett syndrome iPS cells

GSE308264 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2026/02/02 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 24 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: 24 samples: 12 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: Image-based epigenetic profiling with deep learning and high-speed super-resolution microscopyEpigenetics & chromatin · 2026

Summary (written by the submitters)

Rett syndrome (RTT) is a rare neurodevelopmental disorder that primarily affects females. It is caused by mutations in the MECP2 gene located on the X chromosome, which plays a critical role in normal brain development and function. The mutation induces epigenetic changes and chromatin re-arrangement which lead to alteration to H3K27ac and CTCF binding in the genome.

Read the full summary ↓

To determine the epigenetic changes in RTT cells, we performed ChIP-seq on healthy and Rett syndrome iPS cells. RTT increased the H3K27ac marks and reduce CTCF binding, which supports the evidence of MeCP2 role in chromatin alterations.

Published in

Image-based epigenetic profiling with deep learning and high-speed super-resolution microscopy

Wang Y, Ab Ghani NS, Dutta M et al. · Epigenetics & chromatin 2026 · PMID 41764479 · doi:10.1186/s13072-026-00662-5

This dataset

Open GSE308264 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 GSE308264_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 PRJNA1329326 and SRA study SRP620874. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41764479) ↗

Study design

12 conditions, mostly in duplicate

ChIP-seq, iPS cells, H3K27ac, Healthy, ×2 ChIP-seq, iPS cells, H3K27ac, RETT, s3042, ×2 ChIP-seq, iPS cells, H3K27ac, RETT, s3049, ×2 ChIP-seq, iPS cells, H3K27ac, RETT, s3084, ×2 ChIP-seq, iPS cells, CTCF, Healthy, ×2 ChIP-seq, iPS cells, CTCF, RETT, s3042, ×2 ChIP-seq, iPS cells, CTCF, RETT, s3049, ×2 ChIP-seq, iPS cells, CTCF, RETT, s3084, ×2 +4 more

Supports a between-group comparison across 24 samples.

How we read this: 12 replicated groups read from 24 sample titles; they account for 24 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

GSM9241251 ChIP-seq, iPS cells, H3K27ac, Healthy, Rep1 ChIP-seq, iPS cells, H3…
GSM9241252 ChIP-seq, iPS cells, H3K27ac, Healthy, Rep2 ChIP-seq, iPS cells, H3…
GSM9241253 ChIP-seq, iPS cells, H3K27ac, RETT, s3042, Rep1 ChIP-seq, iPS cells, H3…
GSM9241254 ChIP-seq, iPS cells, H3K27ac, RETT, s3042, Rep2 ChIP-seq, iPS cells, H3…
GSM9241255 ChIP-seq, iPS cells, H3K27ac, RETT, s3049, Rep1 ChIP-seq, iPS cells, H3…
GSM9241256 ChIP-seq, iPS cells, H3K27ac, RETT, s3049, Rep2 ChIP-seq, iPS cells, H3…
GSM9241257 ChIP-seq, iPS cells, H3K27ac, RETT, s3084, Rep1 ChIP-seq, iPS cells, H3…
GSM9241258 ChIP-seq, iPS cells, H3K27ac, RETT, s3084, Rep2 ChIP-seq, iPS cells, H3…
GSM9241259 ChIP-seq, iPS cells, CTCF, Healthy, Rep1 ChIP-seq, iPS cells, CT…
GSM9241260 ChIP-seq, iPS cells, CTCF, Healthy, Rep2 ChIP-seq, iPS cells, CT…
GSM9241261 ChIP-seq, iPS cells, CTCF, RETT, s3042, Rep1 ChIP-seq, iPS cells, CT…
GSM9241262 ChIP-seq, iPS cells, CTCF, RETT, s3042, Rep2 ChIP-seq, iPS cells, CT…
GSM9241263 ChIP-seq, iPS cells, CTCF, RETT, s3049, Rep1 ChIP-seq, iPS cells, CT…
GSM9241264 ChIP-seq, iPS cells, CTCF, RETT, s3049, Rep2 ChIP-seq, iPS cells, CT…
GSM9241265 ChIP-seq, iPS cells, CTCF, RETT, s3084, Rep1 ChIP-seq, iPS cells, CT…
GSM9241266 ChIP-seq, iPS cells, CTCF, RETT, s3084, Rep2 ChIP-seq, iPS cells, CT…
GSM9241267 ChIP-seq, iPS cells, Input, Healthy, Rep1 ChIP-seq, iPS cells, In…
GSM9241268 ChIP-seq, iPS cells, Input, Healthy, Rep2 ChIP-seq, iPS cells, In…
GSM9241269 ChIP-seq, iPS cells, Input, RETT, s3042, Rep1 ChIP-seq, iPS cells, In…
GSM9241270 ChIP-seq, iPS cells, Input, RETT, s3042, Rep2 ChIP-seq, iPS cells, In…
GSM9241271 ChIP-seq, iPS cells, Input, RETT, s3049, Rep1 ChIP-seq, iPS cells, In…
GSM9241272 ChIP-seq, iPS cells, Input, RETT, s3049, Rep2 ChIP-seq, iPS cells, In…
GSM9241273 ChIP-seq, iPS cells, Input, RETT, s3084, Rep1 ChIP-seq, iPS cells, In…
GSM9241274 ChIP-seq, iPS cells, Input, RETT, s3084, Rep2 ChIP-seq, iPS cells, In…

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