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Linking single cell genomes and transcriptomes at scale to decode breast cancer progression [scRNA-seq]

GSE261713 Homo sapiens (human) Single-cell RNA-seq 35 samples Submitted 2024/04/22 Platform GPL30173
At a glance

Single-cell RNA-seq · human · 35 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)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
35 samples: 14 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
Coalescing single-cell genomes and transcriptomes to decode breast cancer progressionCell · 2025
Summary (written by the submitters)
Understanding the epithelial lineages of breast cancer and genotype-phenotype relationships requires direct measurements of the genome and transcriptome of the same single cells at scale. To achieve this, we developed wellDR-seq, the first high-genomic resolution, high-throughput method to simultaneously profile the genome and transcriptome of thousands of single cells in parallel.
Read the full summary ↓
We profiled 33,646 single cells from 12 estrogen receptor-positive breast cancers, and identified ancestral subclones in multiple patients that showed a luminal hormone responsive lineage, indicating a potential cell-of-origin. Our data also identified sporadic copy number aberrations (CNAs) in the two luminal epithelial lineages and stromal cells. While gene expression was highly correlated with copy number in larger chromosome segments, our data reveals extensive variation at the single gene-level, reflecting differences in dosage-sensitive and dosage-insensitive genes. Overall, these data reveal the complex relationships between CNAs and gene expression in single cancer cells, improving our understanding of breast cancer progression.
Published in
Coalescing single-cell genomes and transcriptomes to decode breast cancer progression
Wang K, Ye R, Bai S et al. · Cell 2025 · PMID 40912251 · doi:10.1016/j.cell.2025.08.012
This dataset
Open GSE261713 in finder → Find more human RNA-seq datasets → Breast cancer landscape →
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 GSE261713_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 PRJNA1088478 and SRA study SRP495487. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40912251) ↗
Study design
14 conditions, mostly in duplicate
ECIS44T_chip ×5 ECIS25T_chip ×3 MDA231_chip ×2 BCIS28T_chip ×2 ECIS48T_chip ×2 BCIS13T_chip ×2 ECIS36T_chip ×2 Cellline_mixing_experiment_1 ×2 +9 more

Supports a between-group comparison across 32 samples.

How we read this: 14 replicated groups read from 35 sample titles; they account for 32 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
More Breast cancer datasets

Search all human RNA-seq datasets in GEO →

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