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Endothelial PIEZO1 activation impairs osteogenesis via GREM1-mediated inhibition of BMP signaling [MC3T3_RNA-seq]

GSE300884 Mus musculus (mouse) RNA-seq (gene expression) 8 samples Submitted 2026/06/26 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 8 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
8 samples: 8 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.
Summary (written by the submitters)
The mechanosensitive ion channel PIEZO1 has emerged as a key regulator of vascular and skeletal biology. Here, we show that systemic activation of PIEZO1 using the chemical agonist Yoda1 in 3-week-old mice disrupts bone homeostasis by impairing type H endothelial cells and inhibiting osteogenic differentiation. In vivo, Yoda1 treatment significantly reduced the abundance of CD31hiEMCNhi type H vessels and altered trabecular bone microarchitecture without affecting total bone mass.
Read the full summary ↓
In vitro, Yoda1 inhibited osteogenic gene expression and alkaline phosphatase activity in MC3T3-E1 preosteoblasts. Transcriptomic analysis of Yoda1-treated human bone microvascular endothelial cells (hBMECs) revealed a shift from an angiogenic H-type to a quiescent L-type, accompanied by upregulation of BMP signaling antagonists, particularly GREM1. Functional assays confirmed that GREM1 secreted from Yoda1-stimulated endothelial cells suppresses BMP4-induced osteoblast differentiation via paracrine signaling. Together, these findings uncover a novel endothelial-to-osteoblast regulatory axis mediated by PIEZO1-GREM1-BMP4 signaling, and underscore the context-dependent effects of PIEZO1 activation on bone regeneration.
This dataset
Open GSE300884 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 GSE300884_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 PRJNA1282592 and SRA study SRP594859. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 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 8 sample titles: 8 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
Datasets on a similar topic

Search all mouse 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.