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Single-cell analysis of human thymus and peripheral blood unveils the dynamics of T-cell development and aging.

GSE231906 Homo sapiens (human) Single-cell RNA-seq 59 samples Submitted 2025/08/31 Platform GPL34284Platform GPL24676
At a glance

Single-cell RNA-seq · human · 59 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
59 samples: 33 × thymus sample of dono vs 22 × peripheral blood sample of dono vs 2 × thymus sample of donor40, sorted CD34+cells,Groups 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
Single-cell analysis of human thymus and peripheral blood unveils the dynamics of T cell development and agingNature aging · 2025
Summary (written by the submitters)
Age-related thymic involution is the hallmark of immune aging, leading to susceptibility to cancers and emerging pathogens such as SARS-Cov2 in the elderly. However, the precise mechanisms underlying the thymic involution and the effect on compromised peripheral T-cell function is unknown. Here by scRNA-seq and TCR-seq, we captured and analyzed cells in the thymus and peripheral blood from the young and elderly.
Read the full summary ↓
Collectively, our data constitute the first detailed single-cell analysis of ageing-related human thymic involution and T-cell homeostasis in peripheral, providing the insights into human immune aging and potential strategies to rejuvenate the involuted thymus and compromised T-cell immunity.
Published in
Single-cell analysis of human thymus and peripheral blood unveils the dynamics of T cell development and aging
Deng Y, Peng Z, Ming K et al. · Nature aging 2025 · PMID 41136751 · doi:10.1038/s43587-025-00990-3
This dataset
Open GSE231906 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 GSE231906_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA970218. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41136751) ↗
Study design
33 × thymus sample of dono vs 22 × peripheral blood sample of dono vs 2 × thymus sample of donor40, sorted CD34+cells,

Supports a between-group comparison across 57 samples.

How we read this: 3 replicated groups read from 59 sample titles; they account for 57 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

+ 19 more — browse all 59 samples with per-sample file links →

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.