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Neocentromeres fail to maintain DNA methylation boundaries, driving CENP-A drift, instability, and chromosome missegregation

GSE336584 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 60 samples Submitted 2026/06/26 Platform GPL16791
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 60 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
60 samples: 48 distinct titles, 7 of them repeatedGroups 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)
Centromere identity is specified by CENP-A, a histone H3 variant that epigenetically defines centromere position. How CENP-A is maintained at one location in rapidly evolving centromeric DNA is unknown. Using single-cell derived clones of human cell lines, we demonstrate heterogeneity in CENP-A position within cell populations at neocentromeres and a native centromere.
Read the full summary ↓
CENP-A heterogeneity is accompanied by heterogenous DNA methylation patterns, with DNA methylation shifting according to CENP-A position. We demonstrate centromere epigenetic plasticity over extended proliferation, with native centromeres maintaining stable DNA methylation boundaries, but neocentromeres exhibiting DNA methylation instability, boundary loss and increased missegregation. Finally, we show that neocentromeres are more sensitive to DNA methylation inhibition than native centromeres, and that this inhibition is accompanied by expanded CENP-A-enriched domains and increased missegregation. This study supports a role for DNA methylation boundaries in maintaining centromere position, stability and function and highlights the intrinsic instability of DNA methylation at neocentromeres.
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 GSE336584_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 peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1231498 and SRA study SRP567869. Searching any of these in the dataset finder brings you back here.

Study design
48 distinct titles, 7 of them repeated
IMS13q clone B ×4 PD-NC4 C2Y subclone ×3 PD-NC4 C212m subclone ×3 PD-NC4 E212m subclone ×3 IMS13q clone A ×2 IMS13q clone C ×2 BBB clone G ×2

How we read this: Read from 60 sample titles: 48 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

+ 20 more — browse all 60 samples with per-sample file links →

Datasets on a similar topic

Search all human ChIP / ATAC / CUT&Tag 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.