GEO series

Targeting E-Proteins Licenses cDC1 Immunogenicity for Potent Anti-Tumor Vaccination [RNA-seq]

GSE324467 Mus musculus (mouse) RNA-seq (gene expression) 14 samples Submitted 2026/03/15 Platform GPL34290

At a glance

RNA-seq (gene expression) · mouse · 14 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: 14 samples: 14 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)

Type 1 conventional dendritic cells (cDC1s) are essential for priming anti-tumor CD8⁺ T cells, yet the intrinsic pathways limiting their function remain unclear. Here we identify E-proteins (E2A/HEB) as a master checkpoint that actively suppresses cDC1 immunogenicity by directly transactivating PTEN, thereby restraining PI3K-AKT-mTOR signaling, FLT3L-driven proliferation, and NF-κB-mediated IL-12 production.

Read the full summary ↓

Genetic ablation of E-proteins in DCs unleashes a hyperfunctional program—expanded cDC1 populations, enhanced antigen presentation, and superior CD8⁺ T cell priming—while deletion of the E-protein antagonist Id2 impairs cDC1 function, establishing a bidirectional rheostat. This axis is clinically relevant, as dynamic E-protein activity in human tumor DCs correlates with patient survival and response to immune checkpoint blockade. Finally, vaccination with E-protein-deficient DCs elicits potent antigen-specific T cell expansion and achieves robust therapeutic efficacy across multiple preclinical tumor models, defining a targetable checkpoint and establishing E-protein inactivation as a platform for next-generation cancer immunotherapies.

This dataset

Open GSE324467 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 GSE324467_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 PRJNA1435201 and SRA study SRP682650. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

14 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 14 sample titles: 14 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

GSM9577969 WTDC10.5X10-69.13
GSM9577970 WTDC10.5X10-6con
GSM9577971 WT-ctr_L2_UDI370
GSM9577972 EKODC10.5X10-69.13
GSM9577973 EKODC10.5X10-6con
GSM9577974 EKO-ctr_L2_UDI386
GSM9577975 WTDC10.5MHKLM
GSM9577976 WTDC1HKLM9.23
GSM9577977 EKO-HK_L2_UDI387
GSM9577978 EKODC10.5MHKLM
GSM9577979 A1_L2_UDI202
GSM9577980 A3_L1_UDI204
GSM9577981 A6_L3_UDI213
GSM9577982 ZXY-8_L2_Q0248W0071

Datasets on a similar topic

GSE324468 Targeting E-Proteins Licenses cDC1 Immunogenicity for Potent Anti-Tumor Vaccination [ChIP-seq] 2 samples shares cdc1, immunogenicity, vaccination, e-proteins, anti-tumor, targeting, licenses
GSE261556 Chromosomal instability can favor macrophage-mediated immune response and induce a broad, vaccination-like anti-tumor IgG response [RNA-Seq] 6 samples shares vaccination, anti-tumor, rna-seq
GSE261555 Chromosomal instability can favor macrophage-mediated immune response and induce a broad, vaccination-like anti-tumor IgG response [scRNA-Seq] 2 samples shares vaccination, anti-tumor, rna-seq
GSE314794 Vincristine induces spermatogenic impairment in a B-ALL PDX mouse model via downregulation of Tssk4 and Ccdc159: RNA-seq transcriptomic profiling 20 samples shares cdc1, rna-seq
GSE315388 A20 stabilizes IRF8: De novo trogocytic cDC2s orchestrate robust anti-tumor immunity in cDC1-deficient A20cKO mice 9 samples shares cdc1, anti-tumor
GSE299049 A20 stabilizes IRF8: De novo trogocytic cDC2s orchestrate robust anti-tumor immunity in cDC1-deficient A20cKO mice 9 samples shares cdc1, anti-tumor
GSE255477 Ms4a7 expression in cDC1s determines cross-presentation and anti-tumor immunity 40 samples shares cdc1, anti-tumor
GSE336861 ID2 secures cDC1 specification by antagonizing E proteins at a pleiotropic Zeb2 enhancer [scRNA-seq] 6 samples shares cdc1, rna-seq

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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.