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Pan-cancer single-cell atlas of mouse and human tumor-associated dendritic cells

GSE304727 Mus musculus (mouse) Single-cell RNA-seq 17 samples Submitted 2026/03/19 Platform GPL24247Platform GPL21626
At a glance

Single-cell RNA-seq · mouse · 17 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
17 samples: 15 distinct titles, 1 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
Pan-cancer single-cell atlases of mouse and human tumor-associated dendritic cellsNature communications · 2026
Summary (written by the submitters)
Dendritic cells (DCs) are critical inducers of anti-tumor immunity. Yet, a comprehensive mapping of mouse and human DC subsets and states in a cancer context is lacking. Aiming to address this knowledge gap, we have generated pan-cancer mouse and human tumor-associated DC (TADC) scRNA-seq atlases, encompassing 15 mouse tumor models and 10 human cancer types, within which we identified several lineage-defined DC subsets along with functional states.
Read the full summary ↓
We show that TADCs acquire an inflammatory profile with tumor progression and that tumor-mediated reprogramming occurs within the DCs from lymph nodes of tumor-bearing mice. Importantly, we demonstrate that TADCs are conserved across mice and human, and that gene signatures of different TADC subsets/states correlate with patient outcomes. Overall, we provide an in-depth characterization of the TADC compartment in mouse and human cancers, which can improve our understanding of the tumor microenvironment and contribute to the development of new anti-cancer therapies.
Published in
Pan-cancer single-cell atlases of mouse and human tumor-associated dendritic cells
Caro AA, Kancheva D, Hadadi E et al. · Nature communications 2026 · PMID 42218150 · doi:10.1038/s41467-026-73721-w
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 GSE304727_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 PRJNA1302406 and SRA study SRP607024. Searching any of these in the dataset finder brings you back here.

Study design
15 distinct titles, 1 of them repeated
E0771 tumor day ×3

Supports a case/control comparison: 14 samples read as cases, 3 as controls.

How we read this: Read from 17 sample titles: 15 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.

Samples in this study
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