Osteoarthritis (OA) is a major health concern affecting more than 20 million people in US. The disease is predominantly characterized by a gradual degeneration of the load-bearing tissue in joint, articular cartilage. Before the earliest clinical diagnosis of OA, a series of complex and depth-dependent events at various molecular and structural levels has already taken place inside cartilage. A lack of non-invasive and molecular-specific markers to detect the early degradation events in cartilage has so far prevented a fundamental understanding of the development of OA, as well as early diagnosis of and intervention in OA. Due to its multi-level hierarchical organization, multidisciplinary measurements that interrogate cartilage at different technical modalities are warranted. Due to its depth-dependent and heterogeneous structure, a thorough understanding of tissue's response to external loading requires microscopic imaging. Recently, we have successfully imaged the load-induced ultrastructural adaptability in cartilage using at high resolution. We use static loading as a tool to force the tissue to reach a new equilibrium with the environment in order to probe cartilage's intrinsic properties and structural adaptability in a depth-resolved manner in imaging. In essence, static loading becomes a controllable mechanism to induce additional contrast and to enhance weak contrast in our imaging work. The overarching goal of this proposal is to detect the early changes in the in situ molecular architecture of diseased articular cartilage. We hypothesize that the load-induced changes in cartilage at the structural and molecular levels can be detected by a combination of microscopic imaging modalities and that the degradation in cartilage due to diseases or mechanical injury could affect load-induced ultrastructural changes, which will be calibrated by immunohistochemistry imaging. The three specific aims of this study will determine a set of multidisciplinary parameters that detects various changes in tissue's response to static loading due to biochemical digestion, natural lesion, and repetitive/dynamic loading. In combination, this proposal will go beyond the level of describing and characterizing the imaging signals.
It aims to put these imaging techniques to work as the predictors of disease progression, and monitors of injury and repair. Osteoarthritis, which is a major health concern affecting more than 20 million people in US, is predominantly characterized by a gradual degeneration of the load-bearing tissue in joint, articular cartilage. This project aims to detect the early changes in the in situ molecular architecture of diseased cartilage using a set of multidisciplinary microscopic imaging techniques.
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