GEO series

Single-molecule, single-cell profiling of linked chromatin states [Bulk_CUT&Tag]

GSE327816 Homo sapiens (human) Single-cell RNA-seq 9 samples Submitted 2026/04/30 Platform GPL34295

At a glance

Single-cell RNA-seq · human · 9 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 9 samples: 9 samples, each with a distinct title — no replicated groupsGroups 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)

Genomic methods for profiling multiple chromatin features face tradeoffs between specificity, sensitivity, and the ability to measure chromatin co-occupancy. As a result, context-dependent interactions between chromatin proteins remain difficult to resolve in rare cell types and primary tissues. Here, we present CoCUT&Tag, which uses synthetic antigen-peptide repeats paired with nanobodies to amplify Tn5 transposase targeting and directly measure co-occupancy of two chromatin features in single cells.

Read the full summary ↓

We applied CoCUT&Tag to test the model that bivalent chromatin, marked by the coexistence of H3K27me3 and H3K4 methylation, preserves stem-cell multipotency by keeping developmental genes poised for future activation. Instead, we find that during human hematopoiesis, stem cells are defined by a more active chromatin landscape, whereas lineage-restricted populations contain more bivalent genes. Bivalent and unmarked genes are activated with similar timing, but bivalent genes achieve higher expression, are associated with more numerous and stronger enhancers, and are preferentially derepressed following BMI1 loss. Our study shows that bivalent chromatin increases enhancer demand to enable high-amplitude gene activation, demonstrating that CoCUT&Tag can resolve longstanding questions about combinatorial chromatin regulation.

This dataset

Open GSE327816 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 GSE327816_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 PRJNA1452839 and SRA study SRP691368. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

9 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 9 sample titles: 9 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

GSM9666474 Standard CUT&Tag for H3K27me3 (Abcam ab6002) in RS411 cells
GSM9666475 Standard CUT&Tag for H3K27me3 (CST 9733S) in RS411 cells
GSM9666476 Standard CUT&Tag for H3K4me2 (Active Motif 39679) in RS411 cells
GSM9666477 Standard CUT&Tag for H3K4me2 (Sigma Aldrich 07-030) in RS411 cells
GSM9666478 Standard CUT&Tag for H3K27ac (Diagenode C15200184) in K562 cells
GSM9666479 Standard CUT&Tag for H3K4me3 ( CST 9751) in K562 cells
GSM9666480 Standard CUT&Tag for H3K27me3 (BPS Bioscience 25243) in K562 cells
GSM9666481 Standard CUT&Tag for H3K27me3 (CST 9733) in K562 cells
GSM9666482 Standard CUT&Tag for H3K4me3 (Diagenode C15200152) in K562 cells

Datasets on a similar topic

GSE327819 Single-molecule, single-cell profiling of linked chromatin states [Bulk_CoCUT&Tag] 35 samples shares cut, single-molecule, single-cell, chromatin, linked, states, bulk
GSE278910 Pooled CRISPR screens with joint single-nucleus chromatin accessibility and transcriptome profiling [CUT&RUN] 30 samples shares cut, chromatin
GSE319969 Spatial and Bulk Transcriptomic Profiling Defines the Molecular Evolution of Cutaneous Squamous Cell Carcinoma and Reveals Stage-Specific Biomarkers of Clinical Relevance [RNA-Seq] 24 samples shares cut, bulk
GSE334588 Single-cell transcriptomic atlas of adult dermatomyositis, cutaneous lupus erythematosus and healthy skin 48 samples shares cut, single-cell
GSE272005 Genomic and single-cell landscape of transformed cutaneous T-cell lymphoma 32 samples shares cut, single-cell
GSE337829 Integrated single-cell profiling of RNA and DNA interactomes reveals targetable chromatin architectures in cancer [ChIP-Seq] 32 samples shares single-cell, chromatin
GSE337246 Integrated single-cell profiling of RNA and DNA interactomes reveals targetable chromatin architectures in cancer [scRNA-Seq organoids] 35 samples shares single-cell, chromatin
GSE283621 Single-cell multi-omics analysis identifies metabolism-linked epigenetic reprogramming as a driver of therapy-resistant medulloblastoma 79 samples shares single-cell, linked

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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.