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Single-Cell Atlas of the Tumor Immune Microenvironment Across Syngeneic Murine Models

GSE307143 Mus musculus (mouse) Single-cell RNA-seq 30 samples Submitted 2025/10/28 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 30 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
30 samples: 10 conditions, mostly in triplicateGroups 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
Single-cell atlas of the tumor immune microenvironment across syngeneic murine modelsFrontiers in immunology · 2025
Summary (written by the submitters)
The tumor immune microenvironment plays a critical role in tumor progression and responses to immunotherapy. Nevertheless, its cellular complexity and heterogeneity remain incompletely understood. In this study, we employed high-resolution single-cell RNA sequencing on CD45+ immune cells isolated from ten syngeneic murine tumor models, representing seven distinct cancer types under treatment-naïve conditions, thereby enabling a comprehensive profiling of tumor-infiltrating immune cells.
Read the full summary ↓
We classified seven principal immune cell populations and provided an in-depth characterization of T cells, NK/innate lymphoid cells, dendritic cells, monocytes/macrophages, and neutrophils.
Published in
Single-cell atlas of the tumor immune microenvironment across syngeneic murine models
Wang J, Jiang B, Deng M et al. · Frontiers in immunology 2025 · PMID 41322421 · doi:10.3389/fimmu.2025.1676581
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 GSE307143_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 PRJNA1314645 and SRA study SRP616977. Searching any of these in the dataset finder brings you back here.

Study design
10 conditions, mostly in triplicate
MC38 ×3 RENCA ×3 MMTV-PyMT ×3 4T1 ×3 CT26WT ×3 B16F10 ×3 EMT6 ×3 LL2 ×3 +2 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 30 sample titles; they account for 30 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
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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.