Single-cell CRISPRi screens of breast cancer associated enhancers
Single-cell RNA-seq · human · 94 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
- 94 samples: 80 distinct titles, 2 of them repeatedGroups 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
- Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genesGenome biology · 2025
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE224986_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA933230 and SRA study SRP421766. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 94 sample titles: 80 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.
- GSM7036536 MDAMB361_GWAS_SM_Lane1_NovaSeq
- GSM7036537 MDAMB361_GWAS_SM_Lane1
- GSM7036538 MDAMB361_GWAS_SM_Lane2_NovaSeq
- GSM7036539 MDAMB361_GWAS_SM_Lane3_NovaSeq
- GSM7036540 MDAMB361_GWAS_SM_Lane4_NovaSeq
- GSM7036541 MDAMB361_GWAS_SM_Lane5_NovaSeq
- GSM7036542 MDAMB361_GWAS_SM_Lane6_NovaSeq
- GSM7036543 MDAMB361_GWAS_SM_Lane7_NovaSeq
- GSM7036544 MDAMB361_GWAS_SM_Lane8_NovaSeq
- GSM7036545 DAA271_sgRNA_NovaSeq
- GSM7036546 DAA271_sgRNA
- GSM7036547 DAA272_sgRNA_NovaSeq
- GSM7036548 DAA273_sgRNA_NovaSeq
- GSM7036549 DAA274_sgRNA_NovaSeq
- GSM7036550 DAA275_sgRNA_NovaSeq
- GSM7036551 DAA276_sgRNA_NovaSeq
- GSM7036552 DAA277_sgRNA_NovaSeq
- GSM7036553 DAA278_sgRNA_NovaSeq
- GSM7036554 DAA271_CellPlex
- GSM7036555 DAA272_CellPlex
- GSM7036556 DAA273_CellPlex
- GSM7036557 DAA274_CellPlex
- GSM7036558 DAA275_CellPlex
- GSM7036559 DAA276_CellPlex
- GSM7036560 DAA277_CellPlex
- GSM7036561 DAA278_CellPlex
- GSM7036562 MDAMB231_GWAS_Lane3_NovaSeq
- GSM7036563 MDAMB231_GWAS_Lane3
- GSM7036564 MDAMB231_GWAS_Lane4_NovaSeq
- GSM7036565 MDAMB231_GWAS_Lane4
- GSM7036566 MDAMB231_GWAS_Lane5_NovaSeq
- GSM7036567 MDAMB231_GWAS_Lane5
- GSM7036568 MDAMB231_GWAS_Lane6_NovaSeq
- GSM7036569 MDAMB231_GWAS_Lane6
- GSM7036570 MDAMB231_GWAS_Lane7_NovaSeq
- GSM7036571 MDAMB231_GWAS_Lane7
- GSM7036572 MDAMB231_GWAS_Lane8_NovaSeq
- GSM7036573 MDAMB231_GWAS_Lane8
- GSM7036574 YW104_sgRNA_NovaSeq
- GSM7036575 YW104_sgRNA
+ 54 more — browse all 94 samples with per-sample file links →
- GSE104193 Hypoxia-mediated translational activation of ITGB3 in breast cancer cells enhances TGF-β signalling and malignant features in vitro and in vivo 32 samples same disease briefing · shares breast
- GSE104675 Role for the Transcriptional Activator ZRF1 in Breast Cancer Progression and Endocrine Resistance 6 samples same disease briefing · shares breast
- GSE10886 A Supervised Risk Predictor of Breast Cancer Based on Biological Subtypes 226 samples same disease briefing · shares breast
- GSE11121 The humoral immune system has a key prognostic impact in node-negative breast cancer 200 samples same disease briefing · shares breast
- GSE10885 An integromic analysis reveals that metaplastic carcinomas are distinct from basal-like tumors. 244 samples same disease briefing
- GSE328275 Single-cell RNA sequencing of CD45+ immune cells across primary tumor, sentinel tumor-draining lymph node, and axillary lymph node in treatment-naive triple-negative breast cancer 28 samples shares single-cell, breast
- GSE176078 A single-cell and spatially resolved atlas of human breast cancers 26 samples shares single-cell, breast
- GSE303346 Single-Cell RNA-Seq Reveals Immunosuppressive Effects of Triple-Negative Breast Cancer–Derived Exosomes on NK Cell Responses 16 samples shares single-cell, breast
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.