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The transcription factor IRF4 impedes human CD8 T-cell function and promotes cells proliferation and PD-1 expression

GSE245739 Homo sapiens (human) RNA-seq (gene expression) 40 samples Submitted 2024/05/31 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 40 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
Homo sapiens (human)
Samples
40 samples: 40 samples, each with a distinct title — no replicated groupsGroups 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
IRF4 impedes human CD8 T cell function and promotes cell proliferation and PD-1 expressionCell reports · 2024
Summary (written by the submitters)
The transcription factor IRF4 is well known to be quickly upregulated after TCR activation, and magnitude of IRF4 expression is proportional to the strength of TCR activation. However, contradictory reports have been published as to the role of IRF4 in mouse T-cells during acute and chronic viral infections. Furthermore, no data have been published as to the role of IRF4 in human CD8 T-cells, both in normal and pathological conditions.
Read the full summary ↓
In this work, we subjected CD8 T-cells from healthy donors to IRF4 overexpression or knock-out and assessed the impact of these modifications on T-cell functions and transcriptome.
Published in
IRF4 impedes human CD8 T cell function and promotes cell proliferation and PD-1 expression
Hirsch T, Neyens D, Duhamel C et al. · Cell reports 2024 · PMID 38943641 · doi:10.1016/j.celrep.2024.114401
This dataset
Open GSE245739 in finder → Find more human 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 GSE245739_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 PRJNA1029632 and SRA study SRP467184. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38943641) ↗
Study design
40 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 20 samples read as cases, 10 as controls.

How we read this: Read from 40 sample titles: 40 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
Datasets on a similar topic

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.