GEO series

Pressure Points: Endothelial Responses to Hemodynamic Stress in Health and Pulmonary Arterial Hypertension

GSE303084 Homo sapiens (human) RNA-seq (gene expression) 72 samples Submitted 2025/08/14 Platform GPL30173

At a glance

RNA-seq (gene expression) · human · 72 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: 72 samples: 72 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: Pressure Points: Endothelial Responses to Shear Stress and Pressure in Health and Pulmonary Arterial HypertensionbioRxiv : the preprint server for biology · 2025

Summary (written by the submitters)

Background: Hemodynamic forces exert a profound influence on endothelial cell signaling and, when abnormal, contribute centrally to human vascular disease. Pulmonary arterial hypertension (PAH) is characterized by both hemodynamic derangement and pulmonary arterial endothelial cell (PAEC) dysfunction. Despite importance in disease initiation and progression, the combined effects of shear and pressure forces on PAEC biology remain incompletely understood, particularly in the context of PAH.

Read the full summary ↓

Methods: PAECs obtained at explant from controls and from patients with idiopathic or congenital heart disease-associated PAH (CHD-PAH) were cultured in a custom resistor-coupled microfluidic platform and exposed to static, low (3 dyne/cm²), or high (20 dyne/cm²) shear stress under either low or elevated (60 mmHg) pressure. After 24 hours, we assessed cellular morphology and performed systems-level transcriptomic analysis via bulk RNA sequencing, incorporating analyses of PAH subtype and donor sex. Results: PAECs (n=18 donors) aligned with flow under high, but not low, shear, and alignment was not significantly altered by disease state or pressure. Shear stress fundamentally reorganized the PAEC transcriptome and the “dose-response” to increasing shear differed across biological pathways in six statistically significant patterns. Increasing shear led to divergence in transcription between control and PAH cells, particularly in pathways involved in immune activation, stress signaling, and vascular remodeling, with subtype differences also observed. Pressure alone had modest effects on transcription, though CHD-PAH PAECs especially displayed pressure-induced stress and inflammatory signaling. We identified sexual dimorphism in the endothelial shear response, noting male cells under shear enriched for pathways involved in proliferation and inflammation and female cells enriched for lipid metabolism and stress responses. Conclusions: Shear and pressure forces profoundly influence PAEC transcription, with responses shaped by disease state, PAH subtype, and sex. These findings highlight the need for further investigation into mechanosensitive pathways in PAH as potential targets for novel therapies.

Published in

Pressure Points: Endothelial Responses to Shear Stress and Pressure in Health and Pulmonary Arterial Hypertension

Hirsch K, Mandrycky CJ, Kwan I et al. · bioRxiv : the preprint server for biology 2025 · PMID 40766446 · doi:10.1101/2025.07.28.666693

This dataset

Open GSE303084 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 GSE303084_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 PRJNA1293956 and SRA study SRP602321. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40766446) ↗

Study design

72 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 72 sample titles: 72 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

GSM9117456 Sample 89 high flow low pressure
GSM9117457 Sample 89 low flow low pressure
GSM9117458 Sample 89 static
GSM9117459 Sample 100 high flow low pressure
GSM9117460 Sample 100 low flow low pressure
GSM9117461 Sample 100 static
GSM9117462 Sample 103 high flow low pressure
GSM9117463 Sample 103 low flow low pressure
GSM9117464 Sample 103 static
GSM9117465 Sample 108 high flow low pressure
GSM9117466 Sample 108 low flow low pressure
GSM9117467 Sample 108 static
GSM9117468 Sample 109 high flow low pressure
GSM9117469 Sample 109 low flow low pressure
GSM9117470 Sample 109 static
GSM9117471 Sample 118 high flow low pressure
GSM9117472 Sample 118 low flow low pressure
GSM9117473 Sample 118 static
GSM9117474 Sample 120 high flow low pressure
GSM9117475 Sample 120 low flow low pressure
GSM9117476 Sample 120 static
GSM9117477 Sample 131 high flow low pressure
GSM9117478 Sample 131 low flow low pressure
GSM9117479 Sample 131 static
GSM9117480 Sample 174 high flow low pressure
GSM9117481 Sample 174 low flow low pressure
GSM9117482 Sample 174 static
GSM9117483 Sample 92 high flow high pressure
GSM9117484 Sample 92 high flow low pressure
GSM9117485 Sample 92 low flow high pressure
GSM9117486 Sample 92 low flow low pressure
GSM9117487 Sample 92 static
GSM9117488 Sample 95 high flow high pressure
GSM9117489 Sample 95 high flow low pressure
GSM9117490 Sample 95 low flow high pressure
GSM9117491 Sample 95 low flow low pressure
GSM9117492 Sample 95 static
GSM9117493 Sample 105 high flow high pressure
GSM9117494 Sample 105 high flow low pressure
GSM9117495 Sample 105 low flow high pressure

+ 32 more — browse all 72 samples with per-sample file links →

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GSE294621 Vascular-type heterogeneity is associated with differential gene expression profiles of endothelial cells under shear stress 74 samples shares endothelial, stress
GSE312011 A transcriptomic signature that predicts pre-hypertension in adolescence and higher systolic blood pressure in childhood 31 samples shares hypertension, pressure
GSE311520 Hutchinson Gilford Progeria Syndrome alters the endothelial genetic response to laminar shear stress 24 samples shares endothelial, stress
GSE228133 Deficiency of Smooth Muscle ADAR1 RNA Editing Exacerbates Vascular Remodeling and Pulmonary Hypertension [pul43_RNA-seq] 12 samples shares hypertension, pulmonary
GSE314709 Single-cell multiomics uncovers an endothelial mechanosensitive PIEZO1-IL-33 axis driving pulmonary fibrosis 8 samples shares endothelial, pulmonary
GSE335339 PUS7-Mediated Pseudouridylation of TGFBI Drives Vascular Remodeling in Pulmonary Hypertension [BID-seq] 20 samples shares hypertension, pulmonary

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.