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Age-independent and targetable transcription factor networks regulate CD8+ T cell senescence in aging humans

GSE310729 Homo sapiens (human) RNA-seq (gene expression) 83 samples Submitted 2026/01/05 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 83 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
83 samples: 83 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
Age-independent and targetable transcription factor networks regulating CD8(+) T cell senescence in aging humansCell reports · 2026
Summary (written by the submitters)
Aging impairs immunity in part through the accumulation of CD8+ T cells with senescence features, including a pro-inflammatory transcriptome and impaired proliferative capacity. Using senescent cell isolation coupled with multiomic profiling, we defined the epigenetic regulation of CD8+ T cell senescence in healthy younger and older humans.
Read the full summary ↓
We discovered that the transition to senescence was controlled by chromatin state-specific transcription factor (TF) networks whose composition was largely insensitive to donor age. These TF networks mediated widespread enhancer remodeling, causing repression of genes essential for proliferation as well as upregulation of inflammatory and secretory pathways. Inhibition of AP1, KLF5, or RUNX2 modulated the transcriptional output and partially restored the blunted transcriptional response to stimulation of senescent CD8+ T cells. Our data also revealed CD8+ T cell senescence gene signatures predictive of response to CAR-T cell therapy in diffuse large B cell lymphomas. Overall, our study defines the gene regulatory mechanisms of senescence in human CD8+ T cells, provides a resource for the interrogation of CD8+ T cell senescence in disease, highlights TF network perturbation as a viable strategy to manipulate the senescence state of CD8+ T cells, and reveals senescent CD8+ T cell gene signatures as valuable prognostic tools for immunotherapy outcome.
Published in
Age-independent and targetable transcription factor networks regulating CD8(+) T cell senescence in aging humans
Turano PS, Akbulut E, Dewald HK et al. · Cell reports 2026 · PMID 41481417 · doi:10.1016/j.celrep.2025.116795
This dataset
Open GSE310729 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 GSE310729_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 PRJNA1366996 and SRA study SRP647247. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41481417) ↗
Study design
83 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 83 sample titles: 83 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

+ 43 more — browse all 83 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.