Hutchings, R. | bioRxiv Science

bioRxiv2026

A Large Animal Model of Heritable Pulmonary Arterial Hypertension UsingGene-edited BMPR2 Sheep

Pulmonary Arterial Hypertension (PAH) is a rare vascular disorder characterized by elevated pressure in pulmonary arteries, eventually leading to right ventricular failure. Approximately 50% of pediatric disease and 20% of adult disease can be linked to a genetic mutation, with nearly 70% of these cases involving mutations in the bone morphogenetic protein receptor type 2 (BMPR2) locus. Investigations using rodent models have made significant advances in our understanding of BMPR2 signaling; however, limited data exist regarding the onset and course of PAH, and etiologies for phenotypic expression in these patients remain unknown. In this work, we describe the development of a novel ovine model of heritable PAH. Because homozygous disruption of BMPR2 is embryonic lethal, we developed heterozygous BMPR2 sheep by using a PAM-disrupting synonymous single stranded oligodeoxyribonucleotide alongside a single guide RNA and Cas9 mediated gene editing strategy. The resulting BMPR2(+/-) lambs demonstrated cardiac and pulmonary vascular pathology that are consistent with BMPR2 mutation-driven PAH observed in humans. Given the genetic and physiological similarities of BMPR2(+/-)sheep to humans with heritable PAH, this large animal model will serve as a vital platform for mechanistic molecular studies and will provide a much-needed pre-clinical model for extensive treatment evaluations.

molecular biology↗

bioRxiv2020

Citizen science, cells and CNNs - deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.

cell biology↗