← BioTransfer GEO Dataset Finder
GEO series

Loss of YTHDF2 enhances Th9 programming and CAR-Th9 cell antitumor efficacy

GSE279983 Mus musculus (mouse) DNA methylation 20 samples Submitted 2025/06/17 Platform GPL24676Platform GPL24247
At a glance

DNA methylation · mouse · 20 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Mus musculus (mouse)
Samples
20 samples: 7 conditions, mostly in triplicateGroups 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
Loss of YTHDF2 enhances Th9 programming and CAR-Th9 cell antitumor efficacyNature immunology · 2025
Summary (written by the submitters)
CD4+ T cells differentiate into various subsets, including T helper 1 (Th1), Th2, Th9, Th17, and regulatory T cells, which are essential in immune responses and cancer immunotherapy. However, the role of RNA N6-methyladenosine (m6A) modification in this differentiation remains unclear. Here, we demonstrate that YTHDF2, a key m6A reader protein known to destabilize m6A-modified mRNA, negatively regulates Th9 cell differentiation.
Read the full summary ↓
Ablation of Ythdf2 in both mouse and human naïve CD4+ T cells significantly promotes Th9 differentiation by stabilizing Gata3 and Smad3 mRNA under IL-4 and TGF-β signaling. Ythdf2-deficient Th9 cells produce higher levels of IL-9 and IL-21, leading to increased tumor infiltration and cytotoxicity by CD8+ T and NK cells, thereby improving antitumor activity versus wild-type Th9 cells. Moreover, YTHDF2 depletion in CAR-Th9 cells enhances immune activation, reduces terminal differentiation, and augments antitumor efficacy. Targeting YTHDF2 thus represents a promising strategy for enhancing Th9 and CAR-Th9 cell-based cancer immunotherapies.
Published in
Loss of YTHDF2 enhances Th9 programming and CAR-Th9 cell antitumor efficacy
Xiao S, Duan S, Hong Y et al. · Nature immunology 2025 · PMID 40826275 · doi:10.1038/s41590-025-02235-2
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 GSE279983_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: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1175660 and SRA study SRP539939. Searching any of these in the dataset finder brings you back here.

Study design
7 conditions, mostly in triplicate
WT_Th9_MeRIP ×3 KO_Th9_MeRIP ×3 m_WT_Th9 ×3 m_KO_Th9 ×3 H_WT_Th9 ×3 H_KO_Th9 ×3 Th9_RIP ×2

Supports a between-group comparison across 20 samples.

How we read this: 7 replicated groups read from 20 sample titles; they account for 20 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

Search all 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.