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Deciphering the Transcriptomic Signatures of Aging Across Organs in Mice

GSE291420 Mus musculus (mouse) RNA-seq (gene expression) 270 samples Submitted 2026/02/06 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 270 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
270 samples: 270 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
Deciphering the Transcriptomic Signatures of Aging Across Organs in MiceAging cell · 2026
Summary (written by the submitters)
Aging is a key risk factor for disease in mammals, yet its molecular basis across organs remains unclear. Here, we performed bulk RNA sequencing on eight organs (brain, heart, kidney, liver, lung, skeletal muscle, spleen, testis) from male C57BL/6J mice at distinct life stages. Our analysis revealed that age-related transcriptomic shifts vary in timing and magnitude: early in lung, spleen, testis; mid-life in heart, kidney, skeletal muscle; and late in brain and liver.
Read the full summary ↓
Magnitude ranged from very low (testis), low (brain, heart), moderate (lungs, skeletal muscle) to high (kidneys, liver, spleen). We uncovered organ-specific aging signatures, for instance, mitochondrial and epigenetic regulation in the kidney, metabolic/detoxification in the lung, and angiogenesis as well as ribosome biogenesis in the spleen). We also identified shared transcriptomic signatures, such as cellular senescence in the kidney and skeletal muscle, ECM remodeling in the heart, skeletal muscle and spleen), or inflammation in the heart, kidney, liver and lungs. These findings highlight unique and overlapping transcriptomic aging signatures, informing future therapeutic strategies to improve healthspan.
Published in
Deciphering the Transcriptomic Signatures of Aging Across Organs in Mice
Morsy S, Scifo E, Xie K et al. · Aging cell 2026 · PMID 41508419 · doi:10.1111/acel.70357
This dataset
Open GSE291420 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 GSE291420_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 PRJNA1233337 and SRA study SRP568759. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41508419) ↗
Study design
270 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 270 sample titles: 270 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

+ 230 more — browse all 270 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.