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Comparative single-cell multiomic analysis reveals evolutionarily conserved and species-specific cellular mechanisms mediating natural retinal aging [Mouse_multiome_scATAC]

GSE325477 Mus musculus (mouse) Single-cell RNA-seq 17 samples Submitted 2026/04/02 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 17 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
17 samples: 17 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.
Summary (written by the submitters)
Biological age is a major risk factor in the development of common degenerative retinal diseases such as age-related macular degeneration and glaucoma. To systematically characterize molecular mechanisms underlying retinal aging, we performed integrated single- cell RNA- and ATAC-Seq analyses of the retina and retinal pigment epithelium (RPE) across the natural lifespan in zebrafish, mice, and humans.
Read the full summary ↓
By profiling gene expression and chromatin accessibility, we identified extensive cell type- and species-specific aging-dependent changes, with a much smaller number of broadly expressed and conserved genes that include regulators of inflammation and autophagy. We constructed predictive aging clocks for retinal cell types and observed dynamic, reversible shifts in cellular age following acute injury. Spatial transcriptomic analysis revealed region-specific aging signatures and proximity effects, with Müller glia exhibiting pro-rejuvenating influences on neighboring neurons. Targeted Müller glia-specific induction of Yamanaka factors reduced molecular age in rod photoreceptors and bipolar cells without altering glial age. Our findings define conserved and divergent regulatory and signaling pathways mediating retinal aging, highlighting Müller glia as potential therapeutic targets for combating age-associated retinal dystrophies.
This dataset
Open GSE325477 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE325477_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 PRJNA1440255 and SRA study SRP685371. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
17 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 17 sample titles: 17 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
Datasets on a similar topic

Search all mouse ChIP / ATAC / CUT&Tag 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.