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Evolutionarily divergent transcriptomic programs in ovarian folliculogenesis across mice, monkeys and humans [Human]

GSE290074 Homo sapiens (human) RNA-seq (gene expression) 297 samples Submitted 2026/01/19 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 297 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
Homo sapiens (human)
Samples
297 samples: 297 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
Evolutionarily divergent transcriptomic programs in ovarian folliculogenesis across mice, monkeys and humansDevelopment (Cambridge, England) · 2026
Summary (written by the submitters)
Understanding the mechanisms for human ovarian folliculogenesis is fundamental to reproductive biology and medicine. Here, we investigated transcriptomic dynamics in individual oocytes and their associated granulosa cells (GCs) during folliculogenesis in mice, monkeys and humans. Unlike mouse oocytes, which exhibited stage-specific, stepwise transcriptomic maturation, monkey and human oocytes showed minimal transcriptomic changes until the secondary follicle stage and could be broadly categorized as either immature or mature.
Read the full summary ↓
In all three species, most highly variable genes (HVGs) during oocyte growth displayed monotonic up- or downregulation, with limited overlap in HVGs across species. GCs exhibited similarly species-specific transcriptomic trajectories. Correspondingly, intercellular communication pathways, including ligand–receptor signaling, gap junctions, and metabolic coupling between oocytes and GCs, demonstrated substantial species-specific differences. Nonetheless, X-chromosome dosage compensation and repression of evolutionarily young transposons were conserved across species. We established in vitro culture systems supporting preantral to antral follicle development in monkeys and humans, revealing relatively normal oocyte transcriptome maturation but aberrant GC profiles. By delineating interspecies differences in folliculogenesis, this study provides a framework for understanding human ovarian development and advancing its in vitro reconstruction.
Published in
Evolutionarily divergent transcriptomic programs in ovarian folliculogenesis across mice, monkeys and humans
Nakakita B, Mizuta K, Katou Y et al. · Development (Cambridge, England) 2026 · PMID 41503675 · doi:10.1242/dev.205241
This dataset
Open GSE290074 in finder → Find more human 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 GSE290074_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 PRJNA1226150 and SRA study SRP565267. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41503675) ↗
Study design
297 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 297 sample titles: 297 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

+ 257 more — browse all 297 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.