← BioTransfer GEO Dataset Finder
GEO series

Deciphering the function of intrinsic and genomics-driven epigenetic heterogeneity in head and neck cancer progression with single-nucleus CUT&RUN [single-nucleus CUT&RUN]

GSE212249 Homo sapiens (human) Single-nucleus RNA-seq 3955 samples Submitted 2024/11/18 Platform GPL15520
At a glance

Single-nucleus RNA-seq · human · 3955 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism
Homo sapiens (human)
Samples
3955 samples: 184 × HN120Pri, H3K4me3, Rep2, Cell vs 92 × HN120Pri, H3K4me3, Rep1, Cell vs 92 × HN120Met, H3K4me3, Rep1, Cell vs 32 × HN120Met, H3K4me3, Rep2, CellGroups 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
Single-nucleus CUT&RUN elucidates the function of intrinsic and genomics-driven epigenetic heterogeneity in head and neck cancer progressionGenome research · 2025
Summary (written by the submitters)
Single-cell RNA sequencing (scRNA-seq) has proven to be an invaluable tool for the study of tumor evolution. However, interrogating regulatory epigenetic alterations during cancer progression at the resolution of single-cells has remained a major challenge due to the lack of sensitivity of current assays. Here, we developed the highly-sensitive single-nucleus CUT&RUN (snCUT&RUN) assay to profile histone modifications in primary, metastatic, and cisplatin-resistant head and neck squamous cell carcinoma (HNSCC) patient-derived cell lines.
Read the full summary ↓
We find that the epigenome can employ diverse modes to contribute towards cancer progression. Notably, we demonstrate that gene expression changes during cancer progression are co-modulated by alterations in both copy number and chromatin activity to drive epigenetic rewiring of cell-states. Furthermore, intratumor epigenetic heterogeneity may predispose sub-clonal populations within the primary tumor to acquire malignant characteristics. In conclusion, snCUT&RUN serves as a valuable addition to existing toolkit of single-cell assays to dissect the function of the epigenome during cancer progression.
Published in
Single-nucleus CUT&RUN elucidates the function of intrinsic and genomics-driven epigenetic heterogeneity in head and neck cancer progression
Womersley HJ, Muliaditan D, DasGupta R et al. · Genome research 2025 · PMID 39622638 · doi:10.1101/gr.279105.124
This dataset
Open GSE212249 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 GSE212249_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA874833. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39622638) ↗
Study design
184 × HN120Pri, H3K4me3, Rep2, Cell vs 92 × HN120Pri, H3K4me3, Rep1, Cell vs 92 × HN120Met, H3K4me3, Rep1, Cell vs 32 × HN120Met, H3K4me3, Rep2, Cell

Supports a between-group comparison across 400 samples.

How we read this: 4 replicated groups read from the first 400 of 3955 sample titles; they account for 400 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

+ 3915 more — browse all 3955 samples with per-sample file links →

Datasets on a similar topic

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

Share this dataset

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.