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ZBTB16/PLZF regulates self-renewal and differentiation of spermatogonial stem cells through an extensive transcription factor-chromatin poising network [RNA-Seq]

GSE202817 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2025/01/03 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 8 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
ZBTB16/PLZF regulates juvenile spermatogonial stem cell development through an extensive transcription factor poising networkNature structural & molecular biology · 2025
Summary (written by the submitters)
Spermatogonial stem cells (SSCs) balance self-renewal versus differentiation/spermatogenesis to ensure continuous sperm production. Here, we uncover multiple roles for the transcription factor ZBTB16/PLZF in juvenile mouse undifferentiated spermatogonia (uSPG). ZBTB16 activates genes in uSPG promoting self-renewal and cell cycle progression (Ccnd1) to maintain uSPG and transit-amplifying states.
Read the full summary ↓
Remarkably, in uSPG, ZBTB16, SALL4, SOX3 all co-localize at over 12,000 promoters regulating uSPG and meiosis. These regions also feature broad H3K4me3 and H3K27ac marks, DNA hypomethylation, and often CTCF binding. Hi-C analyses reveal robust promoter-promoter physical interactions, revealing a transcription factor and higher-order active chromatin interaction network within uSPG that poises meiotic promoters for subsequent activation. Conversely, these factors do not occupy germline-specific promoters driving spermiogenesis, which instead lack promoter-promoter physical interactions and bear DNA hypermethylation. Therefore, ZBTB16 ensures uSPG cell cycle progression and colocalizes with SALL4, SOX3 and often CTCF to establish a novel chromatin poising network.Spermatogonial stem cells (SSCs) balance self-renewal and differentiation to ensure continuous sperm production in the testis. The transcription factor Zbtb16 (PLZF) supports undifferentiated SSC maintenance through partly unknown mechanisms. We combined genomics (RNA-seq and ChIP-seq) and genetic approaches to reveal multiple functions of Zbtb16 in juvenile mouse SSCs. Zbtb16-bound loci show a striking correlation with active promoters bearing H3K4me3 and the activator Sall4. Zbtb16 activates genes that support SSC self-renewal and cell cycle progression (e.g., Ccnd1) that help maintain undifferentiated SSC pools, including both self-renewing SSCs and transit-amplifying progenitors. Zbtb16 also attenuates certain genes, including meiotic genes and specific retrotransposons that confer genome instability. Notably, Zbtb16 genome localization and its impact on the transcriptome are dynamic, displaying mesenchymal gene targets in vivo, which are not maintained in cultured SSCs. Our data reveal dynamic roles for Zbtb16 in ensuring SSC identity, amplification, and maintenance in vivo.
Published in
ZBTB16/PLZF regulates juvenile spermatogonial stem cell development through an extensive transcription factor poising network
Yi C, Kitamura Y, Maezawa S et al. · Nature structural & molecular biology 2025 · PMID 40033150 · doi:10.1038/s41594-025-01509-5
This dataset
Open GSE202817 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 GSE202817_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 PRJNA837421 and SRA study SRP374877. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40033150) ↗
Study design
8 conditions, mostly in triplicate
RNAseq_THY1+_uSPG_Zbtb16_WT ×3 RNAseq_KIT+_dSPG_Zbtb16_WT ×3 RNAseq_KIT+_dSPG_Zbtb16_Null ×3 RNAseq_THY1+_uSPG_Sox3_WT ×3 RNAseq_KIT+_dSPG_Sox3_WT ×3 RNAseq_THY1+_uSPG_Sox3_cKO ×3 RNAseq_KIT+_dSPG_Sox3_cKO ×3 RNAseq_THY1+_uSPG_Zbtb16_Null ×2 +1 more

Supports a between-group comparison across 23 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 23 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
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