GEO series

IFN-γ and TLR9 signaling reprogram tumor-associated macrophages to promote antitumor immunity in melanoma

GSE324935 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2026/07/06 Platform GPL17021

At a glance

RNA-seq (gene expression) · mouse · 20 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Mus musculus (mouse)
Samples: 20 samples: 20 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.

Summary (written by the submitters)

Tumor-associated macrophages (TAMs) often adopt immunosuppressive phenotypes that promote tumor progression and limit the efficacy of immunotherapy. Although Toll-like receptor 9 (TLR9) agonists and interferon-γ (IFN-γ) possess immunostimulatory properties, their antitumor efficacy as monotherapy remains limited, particularly in poorly immunogenic tumors.

Read the full summary ↓

Here, we investigated whether coordinated activation of innate immune pathways could reprogram macrophages and enhance antitumor immunity in melanoma. Using cap analysis of gene expression (CAGE), we found that combined CpG DNA and IFN-γ stimulation induced extensive transcriptional reprogramming in macrophages, characterized by activation of M1-associated genes, enhancer remodeling, alternative transcription start site usage, and induction of transcription factors associated with inflammatory and cytotoxic responses. Functionally, this combination also markedly enhanced macrophage-mediated phagocytosis of live tumor cells.

This dataset

Open GSE324935 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 GSE324935_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1438129 and SRA study SRP684069. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

20 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 20 sample titles: 20 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

GSM9589067 10_9h_IFN2
GSM9589068 11_9h_CPG1
GSM9589069 12_9h_CPG2
GSM9589070 13_9h_IFN_plus_CPG1
GSM9589071 14_9h_IFN_plus_CPG2
GSM9589072 15_15h_IFN1
GSM9589073 16_15h_IFN2
GSM9589074 17_15h_CPG1
GSM9589075 18_15h_CPG2
GSM9589076 19_15h_IFN_plus_CPG1
GSM9589077 1_Ctr1
GSM9589078 20_15h_IFN_plus_CPG2
GSM9589079 2_Ctr2
GSM9589080 3_3h_IFN1
GSM9589081 4_3h_IFN2
GSM9589082 5_3h_CPG1
GSM9589083 6_3h_CPG2
GSM9589084 7_3h_IFN_plus_CPG1
GSM9589085 8_3h_IFN_plus_CPG2
GSM9589086 9_9h_IFN1

Datasets on a similar topic

GSE272424 Rejuvenating endogenous antitumor immunity via a chimeric receptor-engineered oncolytic virus targeting tumor-associated macrophages 6 samples shares tumor-associated, macrophages, antitumor, immunity
GSE271267 Tumoral Interferon Beta Induces an Immune-Stimulatory Phenotype in Tumor-Associated Macrophages in Melanoma Brain Metastases 12 samples shares tumor-associated, macrophages, melanoma
GSE300933 Ptpn2 limits plasma cell fate and antiviral immunity by integrating B cell receptor and IFN-γ signals in B cells 23 samples shares ifn, immunity
GSE243269 The RNA binding protein HNRNPA2B1 regulates IFNG signaling in macrophages 12 samples shares ifn, macrophages
GSE252269 Targeting the FOXP3–T-bet interaction to restore Treg stability in IFN-γ–driven autoimmunity [CUT&Tag/ATAC-seq] 18 samples shares ifn, immunity
GSE300757 Efferocytosis induces proangiogenic function and chromatin remodeling in tumor-associated macrophages 127 samples shares tumor-associated, macrophages
GSE237783 Complex interaction of tumor-derived factors instructs the niche specific phenotypes of tumor-associated macrophages [RNA-Seq] 28 samples shares tumor-associated, macrophages
GSE286348 Macrophages foster anti-tumor immunity by ZEB1-dependent cytotoxic T cell chemoattraction 18 samples shares macrophages, immunity

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.