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Gene Regulatory Networks Orchestrating Oocyte Fate Bifurcation in Primordial Follicles Revealed by Single-Cell Transcriptomics

GSE290574 Mus musculus (mouse) Single-cell RNA-seq 386 samples Submitted 2026/04/24 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 386 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
386 samples: 317 × P2.5 vs 47 × P6.5 vs 22 × E17.5Groups 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
Gene regulatory networks orchestrating oocyte fate bifurcation in primordial follicles revealed by single-cell transcriptomicsCommunications biology · 2026
Summary (written by the submitters)
The first wave of primordial follicle activation occurs around postnatal day 2.5 (P2.5), with the remaining follicles entering a dormant state to ensure a continuous supply of fertilizable oocytes. However, the molecular mechanisms underlying this critical fate bifurcation remain poorly understood. Here, we employ SMART-seq2 to profile transcriptional dynamics in individual perinatal female mouse germ cells across three developmental stages: cysts at embryonic day 17.5 (E17.5), primordial follicles at P2.5, and primary follicles at P6.5.
Read the full summary ↓
Unsupervised clustering and trajectory inference reveal two distinct transcriptional states in P2.5 oocytes, corresponding to dormant and activated subpopulations. Activated oocytes exhibit upregulation of genes linked to PI3K-Akt/mTORC1 signaling, extracellular matrix (ECM) disassembly, and oocyte maturation, whereas marker genes of dormant oocytes are enriched in oxidative stress response and DNA repair pathways. Transcriptional regulatory network reconstruction based on SCENIC inference identifies key transcription factors (e.g., Bhlhe41, Tcf3/12) orchestrating the fate bifurcation. Notably, dormant oocytes form a stable transcriptional state distinct from the continuous activation and development trajectory. Our findings provide a comprehensive landscape of gene expression and regulatory networks correlated to fate determination of P2.5 primordial follicles, paving the way for advanced therapeutic strategies for related diseases and improved techniques for in vitro follicle activation.
Published in
Gene regulatory networks orchestrating oocyte fate bifurcation in primordial follicles revealed by single-cell transcriptomics
Zhang H, He X, Chen Q et al. · Communications biology 2026 · PMID 42106546 · doi:10.1038/s42003-026-10235-7
This dataset
Open GSE290574 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 GSE290574_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 PRJNA1228640 and SRA study SRP566528. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42106546) ↗
Study design
317 × P2.5 vs 47 × P6.5 vs 22 × E17.5

Supports a between-group comparison across 386 samples.

How we read this: 3 replicated groups read from 386 sample titles; they account for 386 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

+ 346 more — browse all 386 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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