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Transcriptome profiling of ovaries in aged mice administered with individual ingredients of Samul-tang

GSE264002 Mus musculus (mouse) RNA-seq (gene expression) 127 samples Submitted 2024/04/30 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 127 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
127 samples: 40 conditions, mostly in triplicateGroups 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
Transcriptome profiling of aged-mice ovaries administered with individual ingredients of Samul-tangScientific data · 2025
Summary (written by the submitters)
Samul-tang (SM) is a traditional multi-ingredient treatment widely used in clinical practice for improving ovarian function in women with diminished ovarian reserve. Despite its therapeutic benefits, the complexity of its mechanisms of action due to the combination of multiple compounds has limited its research integration into modern pharmacological science.
Read the full summary ↓
To address this challenge, we identified 39 herbal compounds as major ingredients in SM and generated transcriptome data from the mice ovaries after administrating with individual ingredients. This dataset aims to provide insights into the mechanisms of action of SM and to aid in the identification of effective ingredient combinations.
Published in
Transcriptome profiling of aged-mice ovaries administered with individual ingredients of Samul-tang
Shin H, Kim J, Park M et al. · Scientific data 2025 · PMID 39814759 · doi:10.1038/s41597-025-04370-9
This dataset
Open GSE264002 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 GSE264002_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1100537 and SRA study SRP501954. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39814759) ↗
Study design
40 conditions, mostly in triplicate
OC # ×10 OC + Albiflorin # ×3 OC + Aucubin # ×3 OC + Benzoylpaeoniflorin # ×3 OC + Isoacteoside # ×3 OC + Paeoniflorin # ×3 OC + Senkyunolide A # ×3 OC + Senkyunolide I # ×3 +32 more

Supports a between-group comparison across 127 samples.

How we read this: 40 replicated groups read from 127 sample titles; they account for 127 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

+ 87 more — browse all 127 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.