← BioTransfer GEO Dataset Finder
GEO series

Matched single-cell chromatin, transcriptome, and surface marker profiling captures in vivo epigenomic reprogramming during basal-to-luminal transition in the mammary gland [transplantations_OneCell_multiome]

GSE333887 Mus musculus (mouse) Single-cell RNA-seq 14 samples Submitted 2026/07/03 Platform GPL16417Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 14 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
14 samples: 13 × transplantations_chromatin_pGroups 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)
Single-cell multi-omics methods enable simultaneous mapping of chromatin states and transcriptomes, offering deep insights into gene regulation. Yet, the full potential of these approaches remains untapped for rare cell populations, as most methods require thousands of cells and are limited in their ability to capture multiple molecular layers comprehensively within the same cell.
Read the full summary ↓
Here, we introduce OneCell CUT&Tag a user-friendly method that provides matched high-resolution epigenome, full-transcriptome, and surface marker quantification from every cell, with input as low as one cell. Using this approach, we uncovered epigenomic priming of basal cells in the mammary gland and captured the dynamics of basal-to-luminal transdifferentiation. We identified a transitional cell population with intermediate epigenomic profiles—absent in reference populations—and demonstrated a continuous epigenomic progression from basal to luminal states, while transcriptomes exhibited a binary switch. Adaptable to diverse samples and tissues, this method also revealed the role of H3K27me3 in shaping zygotic expression programs. By matching multiple layers of molecular information at single-cell resolution, OneCell CUT&Tag dissects the complementary roles of each omics layer in shaping cellular identity and function, opening new avenues to study rare and complex biological systems.
This dataset
Open GSE333887 in finder → Find more mouse RNA-seq 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 GSE333887_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 PRJNA1473161 and SRA study SRP705707. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
13 × transplantations_chromatin_p

Supports a between-group comparison across 13 samples.

How we read this: 1 replicated groups read from 14 sample titles; they account for 13 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
Datasets on a similar topic

Search all mouse RNA-seq 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.