OSTEOMICS: Identifying Regulators of Bone Homeostasis

Bone Diseases, Bone Fracture, Bone Marrow Disease, Osteoarthritis, Osteoporosis

Diseases of bone associated with ageing, including osteoporosis (OP) and osteoarthritis (OA), reduce bone mass, bone strength and joint integrity. Current non-surgical approaches are limited to pharmaceutical agents that are not disease modifying and have poor patient tolerability due to side effect profiles. Developing a fundamental understanding of cellular bone homeostasis, including how key cell types affect tissue health, and offering novel therapeutic targets for prevention of bone disease is therefore essential. This is the focus of OSTEOMICS. A number of factors have been linked to increased risk of bone disease, including genetic predisposition, diet, smoking, ageing, autoimmune disorders and endocrine disorders. In our study, we will recruit patients undergoing elective and non-elective orthopaedic surgery and obtain surgical bone waste for analysis. This will capture a cohort of patients with bone disorders like OP and OA, in addition to patients without overt clinical bone disease. We will study the relationship between the molecular biology of bone cells, bone structure, genetics (DNA) and environmental factors with the aim of identifying and validating novel therapeutic targets. We will leverage modern single cell technologies to understand the diversity of cell types found in bone. These technologies have now led to the characterisation of virtually every tissue in the body, however bone and bone-adjacent tissues are massively underrepresented due to the anatomical location and underlying technical challenges. Early protocols to demineralise bone and perform single cell profiling have now been developed. We will systematically scale up these efforts to observe how genetic variation at the population level leads to alterations in bone structure and quality. Over the next 10 years, we will generate data to comprehensively characterise bone across health and disease, use machine learning to drive analysis, and experimentally validate hypotheses - which will ultimately contribute to developing the next generation of therapeutic agents.

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United Kingdom