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IT-scC&T-seq streamlines scalable, parallel profiling of protein-DNA interactions in single cells

GSE299567 Mus musculus (mouse) Single-cell RNA-seq 17 samples Submitted 2025/06/11 Platform GPL24676Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 17 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
17 samples: 3 × Bulk CUT&TAG of LMNB1 in H1, vs 3 × Bulk CUT&TAG of IgG control in H1, vs 2 × IT-scC&T-seq of multiple histone marks in multiple cell lines, vs 2 × IT-scC&T-seq of H3K4me3 and H3K27ac in 10-week female mouse mammary gland,Groups 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
IT-scC&T-seq streamlines scalable, parallel profiling of protein-DNA interactions in single cellsGenome biology · 2025
Summary (written by the submitters)
Single-cell profiling protein-chromatin interactions is often constrained by complex workflows, high cost, or dependence on specialised equipment. We present indexed tagmentation-based single-cell CUT&Tag-sequencing (IT-scC&T-seq), a modular, plate-based strategy using three-round combinatorial barcoding. IT-scC&T-seq robustly profiles histone modifications and transcription factors with high specificity and throughput, supporting simultaneous analysis of multiple samples and epitopes.
Read the full summary ↓
Notably, it enables sensitive single-cell mapping of lamina-associated domains, low-abundance chromatin features previously difficult to resolve. Applied to adult mouse mammary gland, the method reveals cell-type-specific chromatin landscapes and lineage-regulatory dynamics. Together, IT-scC&T-seq provides a scalable, cost-effective, and broadly accessible approach for high-resolution chromatin profiling.
Published in
IT-scC&T-seq streamlines scalable, parallel profiling of protein-DNA interactions in single cells
Ma J, Jin W, Rong L et al. · Genome biology 2025 · PMID 40624534 · doi:10.1186/s13059-025-03661-z
This dataset
Open GSE299567 in finder → Find more 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 GSE299567_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 PRJNA1158575 and SRA study SRP531441. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40624534) ↗
Study design
3 × Bulk CUT&TAG of LMNB1 in H1, vs 3 × Bulk CUT&TAG of IgG control in H1, vs 2 × IT-scC&T-seq of multiple histone marks in multiple cell lines, vs 2 × IT-scC&T-seq of H3K4me3 and H3K27ac in 10-week female mouse mammary gland,

Supports a between-group comparison across 10 samples.

How we read this: 4 replicated groups read from 17 sample titles; they account for 10 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
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