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Metabolic exhaustion of T cells in chronic infection is mediated by inhibitory receptor PD-1 and T cell receptor dependent transcription factor IRF4

GSE84820 Mus musculus (mouse) RNA-seq (gene expression) 37 samples Submitted 2018/01/01 Platform GPL13112Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 37 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
37 samples
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)
During chronic stimulation T cells acquire an exhausted phenotype characterized by expression of multiple inhibitory receptors and down-modulation of effector function. While this is required for the protection of the organism from excessive immunopathology, it also prevents successful immunity against persistent viruses or tumor cells.
Read the full summary ↓
Here we demonstrate that CD8+ T cell exhaustion is characterized by a progressive decline in cellular metabolism. Exhausted T cells exhibit reduced metabolic reserve, impaired fatty acid oxidation and production of mitochondrial reactive oxygen species (ROS). Blockade of inhibitory PD-1/PD-L1 signaling rescued mitochondrial biogenesis, oxidative phosphorylation and ROS production, which was required for efficient restoration of cellular expansion and effector function. Expression of inhibitory receptors and impaired metabolic function was fuled by high amounts of IRF4, BATF and NFAT, which formed a TCR-responsive transcriptional circuit that sustained the transcriptional network responsible for T cell exhaustion.
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 GSE84820_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 PRJNA335368 and SRA study SRP079900. Searching any of these in the dataset finder brings you back here.

Samples in this study

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