GEO series

Interferon gamma suppresses T follicular helper cells and antibody responses upon viral infection

GSE239968 Mus musculus (mouse) RNA-seq (gene expression) 14 samples Submitted 2025/03/25 Platform GPL24247

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: 6 × SMARTA, rVSV, vs 4 × SMARTA, Naive, vs 4 × SMARTA, rLCMV,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.
Paper: T-cell-derived IFN-γ suppresses T follicular helper cell differentiation and antibody responsesThe EMBO journal · 2025

Summary (written by the submitters)

Humoral and cellular immunity upon viral infections usually co-exist; however, sometimes, one of the two responses emerges as dominant and is responsible for most of the antiviral activity. For example, infection with lymphocytic choriomeningitis virus (LCMV) induces very strong cellular responses, but weak and inefficient neutralizing Ab (nAb) responses.

Read the full summary ↓

Lack of nAb responses is an important issue for complete clearance of LCMV and other similar viruses, which in some instances tend to establish chronic infections. Recent work from our group has shown that this unbalance is observed also at the level of CD4 T cells responses, with subcutaneous LCMV infection leading almost exclusively to TH1 but no TFH differentiation. Here, we report that LCMV-induced TH1 cells are heterogeneous and comprise a Tcf-1+ subset and a GzmB+ subset. We found that none of the subsets is dependent on the major TH1-polarizing cytokine IL-12. Instead, IFN-gamma (IFN-g) was found to promote the development of GzmB+ cells and to suppress the commitment of the Tcf-1+ population into fully differentiated TFH. As a result, blockade of IFN-g led to a rescue in the TFH population, germinal center B cells and antibody responses. Our results shed light on new mechanisms underlying the inefficient nAbs production in response to non-cytopathic viruses such as LCMV and are promising for the design of new vaccination strategies.

Published in

T-cell-derived IFN-γ suppresses T follicular helper cell differentiation and antibody responses

Sala E, Nelli M, Laura C et al. · The EMBO journal 2025 · PMID 40169810 · doi:10.1038/s44318-025-00414-3

This dataset

Open GSE239968 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 GSE239968_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 PRJNA1001837 and SRA study SRP453126. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40169810) ↗

Study design

6 × SMARTA, rVSV, vs 4 × SMARTA, Naive, vs 4 × SMARTA, rLCMV,

Supports a between-group comparison across 14 samples.

How we read this: 3 replicated groups read from 14 sample titles; they account for 14 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

GSM7678453 SMARTA, Naive, rep1 SMARTA, Naive,
GSM7678454 SMARTA, Naive, rep2 SMARTA, Naive,
GSM7678455 SMARTA, Naive, rep3 SMARTA, Naive,
GSM7678456 SMARTA, Naive, rep4 SMARTA, Naive,
GSM7678457 SMARTA, rVSV, rep1 SMARTA, rVSV,
GSM7678458 SMARTA, rVSV, rep2 SMARTA, rVSV,
GSM7678459 SMARTA, rVSV, rep3 SMARTA, rVSV,
GSM7678460 SMARTA, rVSV, rep4 SMARTA, rVSV,
GSM7678461 SMARTA, rVSV, rep5 SMARTA, rVSV,
GSM7678462 SMARTA, rVSV, rep6 SMARTA, rVSV,
GSM7678463 SMARTA, rLCMV, rep1 SMARTA, rLCMV,
GSM7678464 SMARTA, rLCMV, rep2 SMARTA, rLCMV,
GSM7678465 SMARTA, rLCMV, rep3 SMARTA, rLCMV,
GSM7678466 SMARTA, rLCMV, rep4 SMARTA, rLCMV,

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