Multifactorial contribution of bone nanoscale composition to tissue quality in osteoporosis

SUMMARY There is a long-standing quest to better understand what causes our bones to break, especially as a devastating and widespread consequence of osteoporosis. Beyond assessment of bone quantity (e.g., bone mineral density), it is well-know that tissue-level quality plays a key role in determining bone strength and fragility. We and others have shown that microscale tissue composition is intrinsically related to skeletal diseases; however, a limitation of this approach is that it cannot capture features of the nanoscale building blocks of bone quality and integrity (mineralized collagen fibrils and bundles on the order of 500 nm). Thus, analysis of bone composition at high spatial resolution is needed to elucidate the nanoscale origins of impaired bone health. Additionally, there is a paucity of research into the combined role of compositional properties in bone tissue quality, which is essential to reveal the multifactorial nature underlying poor bone features in osteoporosis. To address these critical gaps in knowledge, we aim to apply innovative approaches to enlighten the multifactorial relationship between bone nanoscale composition and reduced bone tissue quality in osteoporosis. We hypothesize that nanoscale compositional properties of bone are significant correlates to predict histological diagnosis and bone morphologic features associated with osteoporosis. In Aim 1, we propose to determine and quantify nanoscale compositional properties of healthy and osteoporotic bones. Readily available clinical bone biopsies will first be evaluated for standard histopathological diagnosis, as well as by micro-computed tomography (microCT) of bone morphometry. The samples will then be assessed by state-of-the-art optical photothermal infrared (O-PTIR) spectroscopy and imaging, which allows breakthrough analysis of intact tissue composition at 500 nm spatial resolution. Our supportive preliminary data show the acquisition and quantification of diverse bone nanoscale compositional properties of mineral and collagen within individual osteon and trabeculae. Additionally, mineral stoichiometry will be determined by scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX). With this rich dataset, we will perform a comprehensive analysis of comparisons and correlations among bone features in healthy and osteoporotic bones to identify relevant nanoscale compositional properties associated with typical osteoporotic bone quality. In Aim 2, we propose to apply machine learning methods to elucidate the multifactorial relationship between bone nanoscale composition and osteoporosis. The goal will be to predict histopathological diagnosis and morphometric features of normal and osteoporotic bones based on input of combined nanoscale compositional properties. We will initially apply multivariable partial least square (PLS) cross-validation, then focus on cutting-edge deep learning methods. This innovative approach will break new ground towards elucidating which bone nanoscale compositional properties underlie poor bone quality in osteoporosis and will inform future clinical studies into new therapeutic tissue targets to improve bone health.