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Versatile SMAD2 and SMAD3 epitope-tagged mouse models for genome-wide profiling of TGFβ superfamily signaling: uncovering novel GDF9-SMAD2/3 target genes

GSE315433 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 44 samples Submitted 2026/03/17 Platform GPL21626Platform GPL34328
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 44 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
44 samples: 42 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.
Summary (written by the submitters)
Transforming growth factor β (TGFβ) signaling pathways are integral for a plethora of biological processes. SMAD2 and SMAD3 are the principal transcriptional effectors of TGFβ superfamily ligands, yet quantitative, genome-wide mapping of their DNA-associated complexes under physiological contexts has remained limited due to the lack of specific, robust models.
Read the full summary ↓
Here, we generated two versatile epitope-tagged mouse models in which endogenous SMAD2 and SMAD3 proteins are globally tagged with HA and PA, respectively. To demonstrate the broad application of our models, we evaluated our lines in ovarian biology. By integrating genomic and transcriptomic analyses, we identified direct genes induced by the GDF9-SMAD2/3 axis and discovered gene sets suppressed by this signaling cascade, highlighting a previously underappreciated role of GDF9 in attenuating competing pathways to ensure proper ovarian granulosa cell fate transitions. Together, these epitope-tagged SMAD mouse models provide extensive, applicable in vivo genetic toolkits for tissue-specific dissection of TGFβ family signaling.
This dataset
Open GSE315433 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE315433_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1397153 and SRA study SRP659346. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
42 distinct titles, 2 of them repeated
Primary mouse granulosa cells, GDF9 treatment ×2 Primary mouse granulosa cells, Veh treatment ×2

How we read this: Read from 44 sample titles: 42 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

+ 4 more — browse all 44 samples with per-sample file links →

Datasets on a similar topic

Search all mouse ChIP / ATAC / CUT&Tag 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.