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Loss of TET function in T regulatory cells yields ex-Treg cells biased toward T follicular helper cells, causing autoimmune diseases through autoantibody production

GSE325544 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2026/04/15 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 12 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
12 samples: 4 × CD4+ YFP(FOXP3)- T cells from WT vs 4 × CD4+ YFP(FOXP3)- T cells from DKO-moderate vs 4 × CD4+ YFP(FOXP3)- T cells from DKO-severeGroups 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 TET function in T regulatory cells yields ex-Treg cells biased toward T follicular helper cells, causing autoimmune diseases through autoantibody productionFrontiers in immunology · 2026
Summary (written by the submitters)
T regulatory cells (Treg cells) express the transcription factor FOXP3 and maintain immune homeostasis by attenuating effector responses. Treg cells are prone to lose FOXP3 and convert to pathological ‘ex-Treg’ cells under conditions of strong or chronic inflammation.
Read the full summary ↓
One mechanism for loss of FOXP3 expression involves increased DNA methylation of intronic enhancers CNS1 and CNS2 in the Foxp3 locus; these enhancers are maintained in a demethylated state by TET enzymes, 5-methylcytosine (5mC) dioxygenases that generate 5-hydroxymethylcytosine (5hmC) and other oxidized methylcytosines that are essential intermediates in all pathways of DNA demethylation. We previously showed that FOXP3+ Treg cells from Tet2/3-deficient (Tet2/3 DKO) mice displayed increased methylation of CNS1 and CNS2 and converted to FOXP3-negative ex-Treg cells considerably more efficiently than WT Treg cells. Here we extend our previous analysis of Foxp3-Cre Tet2/3fl/fl mice. We classified the mice as DKO-moderate or DKO-severe based on the total number of leukocytes in the spleen and peripheral lymph nodes and investigated the phenotypic and molecular basis for the progressive inflammation occurring in these mice. RNA-seq as well as histological and immunocytochemical analyses showed a striking expansion of T follicular helper (Tfh) cells and plasma cells in Tet2/3 DKO-severe mice. And single-cell (sc) RNA-seq analyses suggested that this was due to skewed differentiation of both Tet2/3 DKO FOXP3+ Treg cells and Tet2/3 DKO FOXP3– ex-Treg cells into Tfh-like cells. Base-resolution “6-base” sequencing showed the expected loss of 5hmC and increased 5mC in Tfh cells purified from Tet2/3 DKO-severe mice, and suggested that the observed bias in gene expression patterns could arise either from a direct increase in methylation of essential enhancers due to TET deficiency, or from interference with binding of methylation-sensitive transcriptional repressors including CTCF.
Published in
Loss of TET function in T regulatory cells yields ex-Treg cells biased toward T follicular helper cells, causing autoimmune diseases through autoantibody production
Suzuki K, Arteaga-Vazquez LJ, Villalobos Reveles B et al. · Frontiers in immunology 2026 · PMID 41972131 · doi:10.3389/fimmu.2026.1684023
This dataset
Open GSE325544 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 GSE325544_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 PRJNA1440441 and SRA study SRP685459. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41972131) ↗
Study design
4 × CD4+ YFP(FOXP3)- T cells from WT vs 4 × CD4+ YFP(FOXP3)- T cells from DKO-moderate vs 4 × CD4+ YFP(FOXP3)- T cells from DKO-severe

Supports a between-group comparison across 12 samples.

How we read this: 3 replicated groups read from 12 sample titles; they account for 12 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
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