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The transcription factor AFF3 is necessary to maintain metabolic quiescence in naïve CD8 T cells and prevent premature immune aging

GSE331107 Mus musculus (mouse) RNA-seq (gene expression) 30 samples Submitted 2026/06/01 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 30 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
30 samples: 27 distinct titles, 2 of them repeatedGroups 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)
Naïve CD8 T cells must be maintained in a quiescent metabolic state in order to respond robustly to infection while avoiding inappropriate activation and causing immunopathology. With age this quiescent state is lost and the CD8 T cell response to infection decreases. The factors regulating metabolic quiescence of CD8 T cells and how this regulation is lost during aging are not completely understood.
Read the full summary ↓
Herein, we identify the transcription factor AFF3 as a novel regulator of metabolic quiescence in naïve CD8 T cells. While naïve AFF3 deficient CD8 T cells are more metabolically active prior to infection, they have reduced accumulation in response to viral infection and this is correlated with a poor capacity to engage glycolysis. During aging in both murine and human CD8 T cells, AFF3 expression is reducing, correlating with to a loss of quiescent metabolism without the cells entering an activated state. Our data highlights the role of the metabolism in CD8 T cell quiescence and identifies a novel transcription factor that may be a target to reinvigorate CD8 T cell responses during aging.
This dataset
Open GSE331107 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 GSE331107_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 PRJNA1466903 and SRA study SRP700945. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
27 distinct titles, 2 of them repeated
Naïve AFF3 KO ×3 Naïve AFF3 WT ×2

How we read this: Read from 30 sample titles: 27 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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