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Single-Cell Transcriptomics Reveals Ovarian Aging and Senescent Cell Accumulation underlying the Estropausal Transition in Mice

GSE267729 Mus musculus (mouse) Single-cell RNA-seq 18 samples Submitted 2026/07/01 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 18 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
18 samples: 5 × mouse_young_regular_ovary vs 5 × mouse_post-estropause_acylic_ovary vs 4 × mouse_peri-estropause_irregular_ovary vs 3 × mouse_peri-estropause_regular_ovaryGroups 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)
Reproductive aging in mice leads to estropause, characterized by estrous cycle irregularity and eventual cessation, yet its underlying mechanism remains unclear. Here, we present a comprehensive single-cell atlas of mouse ovaries across precisely defined reproductive stages—from young (regular cycling) through the estropausal transition (regular versus irregular cycling) to post-estropause (acyclic)—and of ovary-specific senescent cells defined by high senescence-associated β-galactosidase activity.
Read the full summary ↓
We mapped transcriptomic dynamics of ovarian aging and characterized the molecular features of ovarian senescent cells. Our analyses revealed that during the estropausal transition, irregularly cycling ovaries exhibited accelerated aging and cellular senescence features compared with regularly cycling counterparts, including increased transcriptional noise, altered conserved aging pathways such as oxidative phosphorylation and proteostasis, hormone dysregulation in granulosa cells, and elevated expression of the senescence marker Cdkn1a and senescence-associated secretory phenotype factors. This atlas delineates the cellular and molecular hallmarks of mouse ovarian aging and ovary-specific senescent cells, providing a resource for understanding the mechanisms underlying the estropausal transition.
This dataset
Open GSE267729 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 GSE267729_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 PRJNA1112627 and SRA study SRP508342. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 × mouse_young_regular_ovary vs 5 × mouse_post-estropause_acylic_ovary vs 4 × mouse_peri-estropause_irregular_ovary vs 3 × mouse_peri-estropause_regular_ovary

Supports a between-group comparison across 17 samples.

How we read this: 4 replicated groups read from 18 sample titles; they account for 17 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
Datasets on a similar topic

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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.