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Context-dependent regulation of T follicular helper cell formation, expansion and differentiation into T-regulatory type 1 cells by multiple transcription factors

GSE291030 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2025/10/02 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 15 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
15 samples: 14 distinct titles, 1 of them repeatedGroups 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
Contextual regulation of T follicular helper cell expansion and differentiation into T regulatory type 1 cells by multiple transcription factorsCell reports · 2025
Summary (written by the submitters)
Differentiation of naïve T cell precursors into distinct T helper cell subsets, including T follicular helper (TFH) cells, is driven by the expression/repression of subset-specific transcription factors (TFs) that have well defined effects on the acquisition of alternative gene expression programs. Repetitive antigen encounters by TFH cells can trigger their differentiation into IL-10-producing Foxp3– and Foxp3+ T-regulatory type 1 (TR1) cells.
Read the full summary ↓
Since TFH cells are epigenetically poised to differentiate into TR1 cells, we hypothesized that the cell conversion events underpinning this process are regulated at the transcriptional level. Here, we interrogate the role of 20 TFs that bind to motifs enriched in open chromatin regions along the TFH-TR1 cell axis, on the size and function of the pre-treatment TFH cell pool and the size and transcriptional make-up of the various antigen-specific TFH/TR1 cell subsets arising in vivo post repetitive antigen challenge. We find that certain TFH TFs, such as TCF-1 and TOX-2, contribute to TFH cell genesis and TFH cell function but are dispensable for antigen-specific TFH cell expansion and TR1 transdifferentiation. Eleven other TFs, including ATF-6, BATF, BCL-6, BHLHE40, BLIMP-1, ELK-4, IRF4, c-MAF, STAT-3, STAT-4 and T-BET, play distinct roles at different stages of this pathway in a context-dependent manner. Most of these TFs simultaneously promote and suppress different cell conversion events along the axis and promote the expression of subset-specific signature genes at specific cell stages while simultaneously suppressing the expression of such genes at other cell stages in the pathway. This indicates that differentiation of TFH cells into TR1 progeny is driven by a carefully orchestrated sequence of TF gene expression and silencing events that yields dynamic combinations of TFs that enable cell-to-cell transitions. The transcriptional context in which specific TFs are expressed determines their ability to either drive or suppress such transitions and promote or inhibit the expression of alternative gene expression programs at different cell states.
Published in
Contextual regulation of T follicular helper cell expansion and differentiation into T regulatory type 1 cells by multiple transcription factors
Garnica J, Yamanouchi J, Thiessen S et al. · Cell reports 2025 · PMID 41100252 · doi:10.1016/j.celrep.2025.116381
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 GSE291030_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: 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 PRJNA1231420 and SRA study SRP567807. Searching any of these in the dataset finder brings you back here.

Study design
14 distinct titles, 1 of them repeated
scRNA-seq library of Tet+ T cells deficient f… ×2

How we read this: Read from 15 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
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