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Single-cell multiomic comparison of mouse and rat spermatogenesis reveal gene regulatory networks conserved for over 20 million years.

GSE268104 Rattus norvegicus (rat) Single-cell RNA-seq 70 samples Submitted 2025/04/28 Platform GPL25029Platform GPL21626Platform GPL32190Platform GPL30172
At a glance

Single-cell RNA-seq · rat · 70 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Rattus norvegicus (rat)
Samples
70 samples: 56 distinct titles, 2 of them repeatedGroups 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 multiomic comparison of mouse and rat spermatogenesis reveals gene regulatory networks conserved for over 20 million yearsStem cell reports · 2025
Summary (written by the submitters)
Spermatogenesis is driven by dramatic temporal and spatial changes in chromatin regulation, gene transcription, and protein expression. To assess the mechanistic bases for these developmental changes, we utilized multiomic single-cell/single-nuclear RNA-sequencing (sc/snRNA-seq) and transposase-accessible chromatin (snATAC-seq) to identify chromatin changes associated with transcription along with cellular indexing of transcriptomes and epitopes (CITE-seq) to assess post-transcriptional control of expression in adult mouse and rat testes.
Read the full summary ↓
We characterized similarities and variations between the transcriptomes of both species, including a difference in the expression of Id4 in spermatogonial stem cells. In both mice and rats, promoter accessibility and gene expression showed the greatest association during meiosis. We mapped cross-species conservation in putative regulatory regions for key spermatogenic genes, including Sdc4, Sycp3 and Spam1, and we found that the correlation between Cd9 chromatin states and CD9 protein abundance allowed for the isolation of distinct germ cell types with improved resolution. Using a gene regulatory network (GRN) model, we identified 40 key regulons conserved between mouse and rat germ cells, highlighting the relevance of chromatin-related factors in regulating the transcription and translation of key genes across spermatogenesis. (iPSCs) into primordial germ cell-like cells that self-organize within xenogeneic reconstituted testes (xrTestes) generated from mouse fetal testicular cells. Subsequent transplant of xrTestes into immunodeficient mice resulted in efficient generation of undifferentiated and differentiated spermatogonia as well as preleptotene spermatocytes with striking similarities to their in vivo counterparts. As the fertilization competency of human iPSC-generated germ cells cannot be evaluated due to ethical constraints, we utilized a similar strategy to differentiate rhesus iPSCs through all fetal germ cell stages into spermatogonia-like cells. Together, these newly identified models of human gametogenesis will allow further mechanistic assessment of both germ cell development and genetic causes of infertility.
Published in
Single-cell multiomic comparison of mouse and rat spermatogenesis reveals gene regulatory networks conserved for over 20 million years
Whelan EC, Swain JJ, Sussman JH et al. · Stem cell reports 2025 · PMID 40086448 · doi:10.1016/j.stemcr.2025.102449
This dataset
Open GSE268104 in finder → Find more 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 GSE268104_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 PRJNA1114670 and SRA study SRP509202. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40086448) ↗
Study design
56 distinct titles, 2 of them repeated
unselected.rat ×8 EpCAM.rat ×8

How we read this: Read from 70 sample titles: 56 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

+ 30 more — browse all 70 samples with per-sample file links →

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.