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mChIP-seq for high-throughput epigenomic profiling reveals a decoupling of H2A.Z and H3K4me3 in cancer

GSE280574 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 576 samples Submitted 2026/08/05 Platform GPL29480Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 576 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
576 samples: 96 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
mChIP-seq for Multiplex and Multifactorial Epigenomic Profiling Uncovers Cancer-specific Histone Features in Cellular and Circulating NucleosomesbioRxiv : the preprint server for biology · 2026
Summary (written by the submitters)
Histone variant H2A.Z has multiple roles in regulating gene transcription and its overexpression has been observed in various cancer types. However, the epigenomic characterization of H2A.Z in cancer is unclear. Combining the pool-and-split strategy with sequencing, we developed mChIP-seq, a multiplexed chromatin immunoprecipitation followed by sequencing method to efficiently profile multifactorial epigenetic landscapes of histone marks for multiple samples in parallel.
Read the full summary ↓
mChIP-seq generates high-quality profiles comparable to standard ChIP-seq but with merits of high efficiency, low cost, and low-input requirement. Using mChIP-seq to profile H2A.Z and 10 H3 histone modifications for 24 cancer cell lines spanning 9 cancer types, we generated 528 epigenomic profiles from two workflows, comprehensively characterized genomic distribution of H2A.Z, and revealed its associations with main histone modifications and gene expression. Moreover, compared with normal cells by integration analysis of public data, we found a decoupling of H2A.Z and H3K4me3 at promoter sites in cancer cells, where the signal intensity of H2A.Z is not in line with the signal intensity of H3K4me3, which further dysregulates gene expression. Altogether, our results demonstrate that mChIP-seq is a powerful technology for epigenomic profiling and reveal abnormal regulatory features of H2A.Z in cancer.
Published in
mChIP-seq for Multiplex and Multifactorial Epigenomic Profiling Uncovers Cancer-specific Histone Features in Cellular and Circulating Nucleosomes
Sun C, Zhang Q, Yan J et al. · bioRxiv : the preprint server for biology 2026 · PMID 42094403 · doi:10.64898/2026.04.27.721226
This dataset
Open GSE280574 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 GSE280574_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 PRJNA1179441 and SRA study SRP541848. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42094403) ↗
Study design
96 conditions, mostly in duplicate
SNB19, H3K27me3, ×3 NCI-H460, H3K27me3, ×3 786-O, H3K27me3, ×3 A549, H3K27me3, ×3 U251, H3K27me3, ×3 DU145, H3K27me3, ×3 A498, H3K27me3, ×3 NCI-H23, H3K27me3, ×3 +280 more

Supports a between-group comparison across 208 samples.

How we read this: 96 replicated groups read from the first 400 of 576 sample titles; they account for 208 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

+ 536 more — browse all 576 samples with per-sample file links →

Datasets on a similar topic

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