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Pharmacological Targeting of ALDH1a2 in Dendritic Cells Enhances Antigen Presentation, T Cell Activation and Anti-tumor immunity

GSE269630 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2025/10/27 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 10 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
10 samples: 3 × Low maturation subpopulation of BMDC, CD11c+MHCIIdimCD86dim, vs 3 × High maturation subpopulation of BMDC, CD11c+MHCIIhiCD86hi, vs 2 × BMDC with wild type Aldh1a2 (floxed) #Groups 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)
Dendritic cell (DC) vaccines have been proposed as cancer immunotherapies due to their role as crucial antigen-presenting cells that regulate T cell functions. Despite considerable efforts to optimize ex vivo priming of DCs, DC vaccines have rarely been successful, suggesting the presence of unknown inhibitory factors. Here, we examined DC differentiation dynamics and discovered that ALDH1a2-produced retinoic acid (RA) acts as a bottleneck factor, initiating negative feedback to inhibit DC maturation.
Read the full summary ↓
Removing this inhibition through either genetic knockout or pharmacological blockade using KyA33, an ALDH1a2 inhibitor we developed, significantly enhances DC maturation, phagocytosis, antigen presentation, and T cell activation, in part by downregulating glucose metabolism. KyA33 demonstrates favorable drug-like properties, including low toxicity, high membrane permeability, and low cell efflux rate. Its non-covalent binding to ALDH1A2 was also validated through X-ray crystallography. Importantly, application of KyA33 generates more robust DCs vaccines, promoting anti-tumor immunity through enhancing antigen-specific T cell responses. Our investigation highlights the intricate interplay between retinoid signaling, dendritic cell maturation, and immune metabolism, offering promising avenues for enhancing cancer immunotherapies.
This dataset
Open GSE269630 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 GSE269630_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 PRJNA1122792 and SRA study SRP513293. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × Low maturation subpopulation of BMDC, CD11c+MHCIIdimCD86dim, vs 3 × High maturation subpopulation of BMDC, CD11c+MHCIIhiCD86hi, vs 2 × BMDC with wild type Aldh1a2 (floxed) #

Supports a between-group comparison across 8 samples.

How we read this: 3 replicated groups read from 10 sample titles; they account for 8 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
Datasets on a similar topic

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