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Direct regulation of T cell exhaustion by NFAT [RNA-Seq]

GSE64408 Mus musculus (mouse) RNA-seq (gene expression) 8 samples Submitted 2015/02/13 Platform GPL14602
At a glance

RNA-seq (gene expression) · mouse · 8 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
8 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 persistent antigen stimulation, CD8+ cytolytic T cells (CTL) show a gradual decrease in effector function, or “exhaustion”, which impairs the immune response to tumors and infections. Here we show that NFAT, a transcription factor with an established role in T cell activation, in parallel controls a second transcriptional program conferring the characteristic features of CD8+ T cell exhaustion, including upregulation of genes encoding inhibitory cell surface receptors and diminished TCR signaling.
Read the full summary ↓
Expression of an engineered NFAT1, which induces this negative regulatory program in the absence of the effector program, interferes with the ability of CD8+ T cells to protect against Listeria infection or attenuate tumor growth in vivo. NFAT elicits this second program of gene expression in large part by binding to a subset of the sites occupied by NFAT during a typical effector response, suggesting that a balance between the two pathways determines the outcome of TCR signaling.
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 GSE64408_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 PRJNA270947 and SRA study SRP051443. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 8 more — browse all 8 samples with per-sample file links →

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