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Multi-omics Analysis Revealed Unique Features of Age-associated Type2 CD8 Memory T cells

GSE297039 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2026/02/11 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
Samples
30 samples: 5 conditions, mostly with about 6 replicates eachGroups 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
Multi-Omics Analysis of Human Blood Cells Reveals Unique Features of Age-Associated Type 2 CD8 Memory T CellsAging cell · 2026
Summary (written by the submitters)
Aging impacts immune function, yet how and why various subsets of immune cells change during aging is still incompletely understood. Here, we analyzed peripheral blood mononuclear cells (PBMCs) from a cohort of healthy donors aged 20–82 years to uncover previously unrecognized age-dependent changes, using a 36-color spectral flow cytometry panel focused on T cells.
Read the full summary ↓
We found that CXCR3- memory CD8 T cells, which produce a Th2-like cytokine response, accumulate with age. Comprehensive multi-omics analysis revealed transcriptional and epigenetic signatures that support Th2-like function. Importantly, we also identified an age-dependent bias towards the production of Th2 cytokines after activation in naive CD8 T cells, along with transcriptional and epigenetic changes supporting this outcome. Moreover, health outcome association analysis demonstrated a pathogenic feature of CXCR3- CM accumulation. Together, our results suggest that age-dependent corruption of epigenetic programming may cause the aberrant emergence of this potentially pathogenic Th2-like immune population
Published in
Multi-Omics Analysis of Human Blood Cells Reveals Unique Features of Age-Associated Type 2 CD8 Memory T Cells
Matsui H, Cervantes M, Khalid MM et al. · Aging cell 2026 · PMID 41622514 · doi:10.1111/acel.70393
This dataset
Open GSE297039 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 GSE297039_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 PRJNA1262428 and SRA study SRP585067. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41622514) ↗
Study design
5 conditions, mostly with about 6 replicates each
FACS sorted CD8+ CXCR3+ CM T cells from ×6 FACS sorted CD8+ CXCR3- CM T cells from ×6 FACS sorted CD8+ CD57+ EMRA T cells from ×6 FACS sorted CD8+ CD28+/CD57+ EM T cells from ×6 FACS sorted CD8+ Naive T cells from ×6

Supports a between-group comparison across 30 samples.

How we read this: 5 replicated groups read from 30 sample titles; they account for 30 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
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