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Single-Cell Epigenomics Uncovers Heterochromatin Instability and Transcription Factor Dysfunction during Mouse Brain Aging [snATAC-Seq] [10X Multiome]

GSE294772 Mus musculus (mouse) Single-cell RNA-seq 142 samples Submitted 2025/05/02 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 142 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
142 samples: 64 conditions, mostly in duplicateGroups 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 Epigenomics Uncovers Heterochromatin Instability and Transcription Factor Dysfunction during Mouse Brain AgingbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
We performed single-nucleus ATAC-seq (snATAC-seq) to profile chromatin accessibility across eight brain regions from C57BL/6J male and female mice at 2, 9, and 18 months of age. Nuclei were isolated from frozen dissected brain regions, tagmented using barcoded Tn5, and sequenced using combinatorial barcoding or 10x Multiome (female samples). Our analysis reveals region- and age-dependent chromatin changes, highlighting regulatory programs associated with aging in specific brain cell types.
Published in
Single-Cell Epigenomics Uncovers Heterochromatin Instability and Transcription Factor Dysfunction during Mouse Brain Aging
Amaral ML, Mamde S, Miller M et al. · bioRxiv : the preprint server for biology 2025 · PMID 40475496 · doi:10.1101/2025.04.21.649585
This dataset
Open GSE294772 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 GSE294772_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 PRJNA1248586 and SRA study SRP578707. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40475496) ↗
Study design
64 conditions, mostly in duplicate
AMY_18mo_F_ATAC ×3 AMY_2mo_F_ATAC ×3 CP_9mo_F_ATAC ×3 ENT_18mo_F_ATAC ×3 ENT_9mo_F_ATAC ×3 HCP_9mo_F_ATAC ×3 AMY_18mo_F_RNA ×3 AMY_2mo_F_RNA ×3 +58 more

Supports a between-group comparison across 140 samples.

How we read this: 64 replicated groups read from 142 sample titles; they account for 140 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

+ 102 more — browse all 142 samples with per-sample file links →

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.