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Spatial transcriptomics of the aging mouse brain reveals origins of inflammation in the white matter

GSE233983 Mus musculus (mouse) Spatial transcriptomics 44 samples Submitted 2025/01/02 Platform GPL24247
At a glance

Spatial transcriptomics · mouse · 44 samples

What it is
Spatial transcriptomicsGene activity measured with its position in a tissue section.
Organism
Mus musculus (mouse)
Samples
44 samples: 44 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
Spatial transcriptomics of the aging mouse brain reveals origins of inflammation in the white matterNature communications · 2025
Summary (written by the submitters)
To systematically understand age-induced molecular changes, we performed spatial transcriptomics of young, middle-aged, and old mouse brains and identified seven transcriptionally distinct regions. All regions exhibited age-associated upregulation of inflammatory mRNAs and downregulation of mRNAs related to synaptic function. Notably, aging white matter fiber tracts showed the most prominent changes with pronounced effects in females.
Read the full summary ↓
The inflammatory signatures indicated major ongoing events: microglia activation, astrogliosis, complement activation, and myeloid cell infiltration. Immunofluorescence and quantitative MRI analyses confirmed physical interaction of activated microglia with fiber tracts and concomitant reduction of myelin in old mice. In silico analyses identified potential transcription factors influencing these changes. Our study provides a resourceful dataset of spatially resolved transcriptomic features in the naturally aging murine brain encompassing three age groups and both sexes. The results link previous disjointed findings and provide a comprehensive overview of brain aging identifying fiber tracts as a focal point of inflammation.
Published in
Spatial transcriptomics of the aging mouse brain reveals origins of inflammation in the white matter
Wang L, Cui CY, Lee CT et al. · Nature communications 2025 · PMID 40185750 · doi:10.1038/s41467-025-58466-2
This dataset
Open GSE233983 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 GSE233983_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: Spatial studies usually ship Space Ranger style folders (count matrix plus tissue images). Look for them among the supplementary files.

Also filed as BioProject PRJNA979150 and SRA study SRP440956. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40185750) ↗
Study design
44 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 44 sample titles: 44 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

+ 4 more — browse all 44 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.