Development of a novel biomaterial model of collagen mediated genetic disease for study of collagen organisation and drug development

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Osteogenesis imperfecta (OI) is a group of 17 inherited connective tissue disorders mediated by mutations in collagen genes that lead to bone deformation and fragility. Currently, in vivo testing is the standard practice for treatments, with over 20 animal models representing OI. These models are technically limited, but also with regards to accessibility and reproducibility of collagen alignment and organisation. A validated in vitro method would allow for accurate preclinical modelling.Mesenchymal stem cells (MSC) can differentiate into bone forming cells and create a bone-like collagen extracellular matrix. Work in Prof. Reilly's group has demonstrated that the organisation and quality of the collagen can be modelled in a tissue specific manner. We propose that the genetic mutations that cause OI will also affect the ability of cells to organise collagen correctly, and that using this scaffold-cell system will provide important insight into OI pathogenesis. The aim of this project is to develop characterised and validated in vitro model of OI collagen pathology. This will be achieved by using electo-spinning to produce polycaprolactone scaffolds on which cells will be grown following established protocols. The relationship between scaffold fibre characteristics and collagen structure will be mapped using light microscopy and SEM for scaffold characteristics, and histology and second harmonic generation microscopy for cellular and extracellular factors. Mechanical testing will be used to determine the role of collagen organisation in tissue structure. OI mutations identified from patient populations will be introduced to MSC cells using CRISPR endogenous gene editing enabling comparison between healthy vs OI collagen. This will allow for investigation of the molecular pathways and testing of therapeutic agents. A high throughput image analysis protocol using ImageJ will be developed for systematic analysis of large data sets.