← BioTransfer GEO Dataset Finder
GEO series

Translational Downregulation of 5’ TOP mRNAs During T Cell Exhaustion [RNA-Seq]

GSE313691 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/12/18 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 4 × Human CD8+ T cells, acute, dono vs 4 × Human CD8+ T cells, re-activated, dono vs 4 × Human CD8+ T cells, chronic, donoGroups 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)
T cell exhaustion is a dysfunctional state that arises during chronic infections and cancer, characterized by impaired effector functions and sustained expression of inhibitory receptors. While transcriptional, epigenetic, and metabolic rewiring have been well documented in exhausted T cells, a comprehensive understanding of how translation is regulated in this state remains incomplete.
Read the full summary ↓
To address this gap, we performed ribosome profiling and RNA sequencing on exhausted human CD8⁺ T cells to globally assess translational control. Our analyses reveal a marked repression of 5’ terminal oligopyrimidine (TOP) mRNAs during exhaustion. Unexpectedly, we demonstrate that this translational repression occurs despite evidence of elevated mTOR activity in exhausted T cells. These findings uncover a previously unknown layer of translational control in exhausted T cells.
This dataset
Open GSE313691 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 GSE313691_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 PRJNA1381344 and SRA study SRP655136. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × Human CD8+ T cells, acute, dono vs 4 × Human CD8+ T cells, re-activated, dono vs 4 × Human CD8+ T cells, chronic, dono

Supports a between-group comparison across 12 samples.

How we read this: 3 replicated groups read from 12 sample titles; they account for 12 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
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.