← BioTransfer GEO Dataset Finder
GEO series

Genomic and functional characterization of the tumor-killing capacity of Imiquimod-activated plasmacytoid dendritic cells

GSE255537 Mus musculus (mouse) Single-cell RNA-seq 18 samples Submitted 2025/05/28 Platform GPL19057
At a glance

Single-cell RNA-seq · mouse · 18 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
18 samples: 18 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.
Paper
Multi-omics and functional characterization of the tumor-killing capacity of Imiquimod-activated plasmacytoid dendritic cellsiScience · 2025
Summary (written by the submitters)
Plasmacytoid dendritic cells (pDCs) are a subset of DCs that act as important modulators of anti-tumor immune responses. pDCs have been associated with poor prognosis of tumor patients. Despite this, upon TLR7 activation, pDCs have also been shown to acquire tumor killing capacities, and we previously provided evidence on the role of activated pDCs in eliminating tumor cells in vivo, independently of adaptive immunity.
Read the full summary ↓
In this study we investigated the mechanism by which pDCs acquire tumor-killing capacities following TLR7/8 triggering by Imiquimod (IMQ). By combining pathway perturbation and analysis of transcriptome, surfaceome, secretome, and killing function, we identified the MAPKs and NF-kB pathways as important downstream mediators of the tumor-killing ability of these cells. JNK inhibition reduces killing by secreted factors whereas p38 or NF-kB inhibition enhances cell mediated killing. Analysis of surface and secreted proteins revealed differential control of expression and secretion by IFN-I/-II, MAPK and NF-kB, while indicating that a complex signaling network is necessary to shape the cytotoxic phenotype of activated pDCs. Our integrated analysis converged on a new pDCs killing gene signature that is predictive of survival in cohorts of melanoma patients. These newly identified markers will be instructive for the rationale design and interpretation of further single-cell and functional approaches aimed at studying the role of pDCs in the tumor microenvironment. These might pinpoint novel strategies to improve cancer management and treatment, by modulating the pDCs pro- vs anti-tumorigenic functions.
Published in
Multi-omics and functional characterization of the tumor-killing capacity of Imiquimod-activated plasmacytoid dendritic cells
Gastaldi T, Novoszel P, Sanlorenzo M et al. · iScience 2025 · PMID 40530418 · doi:10.1016/j.isci.2025.112670
This dataset
Open GSE255537 in finder → Find more mouse RNA-seq datasets →
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.

Series matrix ↓
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 GSE255537_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1075315 and SRA study SRP489239. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40530418) ↗
Study design
18 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 18 sample titles: 18 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
Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.