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Vg1+ gd T cells promote DC activation and CD8 T cell expansion via IL-4

GSE263267 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2024/12/07 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 16 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
16 samples: 5 × IL4 vs 4 × Media vs 4 × CD40 and IL4 vs 3 × CD40Groups 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)
Classically, dendritic cells (DC) capture pathogen material and upon activation by pathogen products (or damage), initiate adaptive immunity. Here, we describe an additional layer to this process, required when the pathogen-derived signals themselves do not provide effective DC activation. Immunisation with sporozoites from Plasmodium leads to the generation of protective anti-malarial immunity mediated by liver resident memory T (TRM) cells, in a complex response that is crucially dependent upon gd T cells.
Read the full summary ↓
Here, we show that CD40L signals to antigen-presenting DC are CD4 T cell derived, but Vg1 gd T cells play an initiating role by providing essential IL-4 signals to DC. IL-4 acts together with IFNg to induce IL-12 and promote CD8 T cell expansion. This study shows that responses to some pathogens, such as Plasmodium, require help from innate-like T cells to pass the initiation threshold, and demonstrates the critical role of IL-4 in this process.
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 GSE263267_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 PRJNA1096369 and SRA study SRP499850. Searching any of these in the dataset finder brings you back here.

Study design
5 × IL4 vs 4 × Media vs 4 × CD40 and IL4 vs 3 × CD40

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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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