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
Single-cell RNA-seq · human · 28 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
- 28 samples: 28 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.
Read the full summary ↓
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 GSE328275_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 PRJNA1454472. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 28 sample titles: 28 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.
- GSM9677534 Sample1-Tumor primary tumor scRNA-seq
- GSM9677535 Sample1-SLN sentinel lymph node scRNA-seq
- GSM9677536 Sample2-Tumor primary tumor scRNA-seq
- GSM9677537 Sample2-SLN sentinel lymph node scRNA-seq
- GSM9677538 Sample2-ALN axillary lymph node scRNA-seq
- GSM9677539 Sample3-Tumor primary tumor scRNA-seq
- GSM9677540 Sample3-SLN sentinel lymph node scRNA-seq
- GSM9677541 Sample4-Tumor primary tumor scRNA-seq
- GSM9677542 Sample4-SLN sentinel lymph node scRNA-seq
- GSM9677543 Sample4-ALN axillary lymph node scRNA-seq
- GSM9677544 Sample5-Tumor primary tumor scRNA-seq
- GSM9677545 Sample5-SLN sentinel lymph node scRNA-seq
- GSM9677546 Sample5-ALN axillary lymph node scRNA-seq
- GSM9677547 Sample6-Tumor primary tumor scRNA-seq
- GSM9677548 Sample6-SLN sentinel lymph node scRNA-seq
- GSM9677549 Sample6-ALN axillary lymph node scRNA-seq
- GSM9677550 Sample7-Tumor primary tumor scRNA-seq
- GSM9677551 Sample7-SLN sentinel lymph node scRNA-seq
- GSM9677552 Sample7-ALN axillary lymph node scRNA-seq
- GSM9677553 Sample8-Tumor primary tumor scRNA-seq
- GSM9677554 Sample8-SLN sentinel lymph node scRNA-seq
- GSM9677555 Sample8-ALN axillary lymph node scRNA-seq
- GSM9677556 Sample9-Tumor primary tumor scRNA-seq
- GSM9677557 Sample9-SLN sentinel lymph node scRNA-seq
- GSM9677558 Sample9-ALN axillary lymph node scRNA-seq
- GSM9677559 Sample10-Tumor primary tumor scRNA-seq
- GSM9677560 Sample10-SLN sentinel lymph node scRNA-seq
- GSM9677561 Sample10-ALN axillary lymph node scRNA-seq
- 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
- GSE104675 Role for the Transcriptional Activator ZRF1 in Breast Cancer Progression and Endocrine Resistance 6 samples same disease briefing
- GSE10885 An integromic analysis reveals that metaplastic carcinomas are distinct from basal-like tumors. 244 samples same disease briefing
- GSE10886 A Supervised Risk Predictor of Breast Cancer Based on Biological Subtypes 226 samples same disease briefing
- GSE11121 The humoral immune system has a key prognostic impact in node-negative breast cancer 200 samples same disease briefing
- GSE313511 Single-cell RNA-sequencing of CD45+ immune cells from a kidney transplant biopsy and matched peripheral blood 2 samples shares cd45, single-cell
- GSE334033 A Patient-Derived Xenograft Model Identifies AK1 as a Key Regulator of Metastatic Potential in Primary Triple-Negative Breast Cancer 105 samples shares triple-negative, primary
- GSE303346 Single-Cell RNA-Seq Reveals Immunosuppressive Effects of Triple-Negative Breast Cancer–Derived Exosomes on NK Cell Responses 16 samples shares triple-negative, single-cell
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.