GEO series

Asynchronous female reproductive organ ageing as revealed by single-cell transcriptomic atlas

GSE241318 Mus musculus (mouse) Single-cell RNA-seq 21 samples Submitted 2024/12/18 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 21 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: 21 samples: 21 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: Circulating Interleukin-6 Mediates PM(2.5)-Induced Ovarian Injury by Suppressing the PPARγ PathwayResearch (Washington, D.C.) · 2024

Summary (written by the submitters)

Successful pregnancies require fine coordination among the reproductive organs. Reproductive research has focused on ovarian ageing, the uterus and oviduct have received much less attention; whether these organs age synchronously with the ovaries remains unclear. Here, we established a single-cell transcriptomic atlas of the ovary, oviduct, and uterus across the murine reproductive lifespan.

Read the full summary ↓

Organ–organ communication patterns were identified using cell-specific hormone receptor analysis. The ageing hallmarks (including DNA damage, cellular senescence, and apoptosis, etc.) were evaluated for each cell type. Analysis of these ageing-related gene set scores combined with changes in morphology and molecular signatures revealed that the ovary aged earlier and faster than the oviduct and uterus; this was also confirmed in human reproductive tissues. Our single-cell transcriptomic atlas provides molecular insights into reproductive ageing and its asynchronization for the reproductive organs across the murine reproductive lifespan, which may reveal the causes of declining fertility at advanced ages.

Published in

Circulating Interleukin-6 Mediates PM(2.5)-Induced Ovarian Injury by Suppressing the PPARγ Pathway

Chen Y, Zhang J, Zhang T et al. · Research (Washington, D.C.) 2024 · PMID 39639885 · doi:10.34133/research.0538

This dataset

Open GSE241318 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 GSE241318_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 PRJNA1007677 and SRA study SRP456355. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39639885) ↗

Study design

21 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 21 sample titles: 21 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

GSM7721014 OVA5D
GSM7721015 OVA5W
GSM7721016 OVA3M
GSM7721017 OVA6M
GSM7721018 OVA9M
GSM7721019 OVA12M
GSM7721020 OVA15M
GSM7721021 OVI5D
GSM7721022 OVI5W
GSM7721023 OVI3M
GSM7721024 OVI6M
GSM7721025 OVI9M
GSM7721026 OVI12M
GSM7721027 OVI15M
GSM7721028 UTE5D
GSM7721029 UTE5W
GSM7721030 UTE3M
GSM7721031 UTE6M
GSM7721032 UTE9M
GSM7721033 UTE12M
GSM7721034 UTE15M

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