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Single-cell RNA-seq of 4T1 breast tumors from mice treated with anti-Siglec-E and/or anti-PD-1 antibodies

GSE271964 Mus musculus (mouse) Single-cell RNA-seq 2 samples Submitted 2025/08/15 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 2 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
2 samples
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
Tumor-expressed GPNMB orchestrates Siglec-9(+) TAM polarization and EMT to promote metastasis in triple-negative breast cancerProceedings of the National Academy of Sciences of the United States of America · 2025
Summary (written by the submitters)
Metastasis remains the leading cause of cancer-related mortality, driven by complex interactions within the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) play a pivotal role in metastatic progression, yet molecular diversity and upstream regulators remain poorly defined. Glycoprotein nonmetastatic melanoma protein B (GPNMB), overexpressed in subsets of tumors including triple-negative breast cancer (TNBC), is implicated in epithelial-mesenchymal transition (EMT) and cancer stemness.
Read the full summary ↓
Recent single-cell RNA-seq studies have identified GPNMB as a marker of immunosuppressive TAMs associated with poor prognosis, but its mechanistic role in TAM polarization in TNBC has remained unclear. Co-culturing monocytic cells with three-dimensional sphere-forming TNBC cells induces their conversion into GPNMB⁺Siglec-9⁺ tumor-associated macrophages (TAMs). Tumor-derived GPNMB promotes monocyte-to-TAM polarization by inducing secondary GPNMB expression in monocytes, establishing a feed-forward amplification loop. Knockdown of GPNMB in TNBC cells significantly inhibits multiple immunosuppressive TAM subtypes, including Siglec-9⁺ TAM and EMT-associated TAM populations, as inferred from scRNA-seq and validated in patient tumors using bulk RNA-seq deconvolution. Distinct sialylation patterns were identified: tumor-derived GPNMB exhibited α2,3-sialylation, whereas macrophage-derived GPNMB exhibited α2,6-sialylation, enabling differential Siglec-9 recognition. Elevated GPNMB and Siglec-9 expression correlated with poor prognosis in TNBC patient cohorts. Importantly, dual inhibition of Siglec-E (murine Siglec-9 ortholog) and PD-1 suppressed IL-6-dependent EMT, reduced tumor stemness, and significantly limited lung metastasis in vivo. The GPNMB–Siglec-9 axis thus represents a critical glyco-immunological checkpoint driving TAM-mediated metastasis, providing a promising therapeutic target in TNBC.
Published in
Tumor-expressed GPNMB orchestrates Siglec-9(+) TAM polarization and EMT to promote metastasis in triple-negative breast cancer
Dang Cao TL, Kawanishi K, Hashimoto S et al. · Proceedings of the National Academy of Sciences of the United States of America 2025 · PMID 40892920 · doi:10.1073/pnas.2503081122
This dataset
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.

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 GSE271964_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1134211 and SRA study SRP519164. Searching any of these in the dataset finder brings you back here.

Samples in this study

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