Fan Xiwei, Xu Hong, Prasadam Indira, Sun Antonia Rujia, Wu Xiaoxin, Crawford Ross, Wang Yanping, Mao Xinzhan
Department of Orthopaedic Surgery, The Second Xiangya Hospital of Central South University, Changsha, China.
Traumatic Orthopaedic Research Lab, The Second Xiangya Hospital of Central South University, Changsha, China.
Aging Dis. 2024 Dec 30. doi: 10.14336/AD.2024.1538.
Osteoarthritis (OA) is a multifaceted degenerative joint disorder affected by various risk factors such as age, mechanical stress, inflammation, and metabolic influences. These elements contribute to its diverse phenotypes and endotypes, underscoring the disease's inherent complexity. The involvement of multiple tissues and their interplay further complicates OA's investigation. The current limitations in spatial phenotyping technologies, coupled with the intricate web of multifactorial interactions, have hindered the discovery of reliable early diagnostic markers and the development of tailored therapeutic strategies. However, recent advances in spatiotemporal analysis have revolutionised researchers' capacity to explore OA's spatiotemporal dynamics. These advancements provide unprecedented insights into the disease's progression, revealing patient-specific clinical presentations, tissue and joint structure alterations, and microscopic to molecular changes in tissue cell populations and extracellular matrices. This paper summarises the latest developments in utilising state-of-the-art technologies for the deep phenotyping of OA's spatiotemporal variations, emphasising their critical role in elucidating OA's pathophysiology and how this can change clinical practice and advancing personalised treatment approaches, and finally lead to better clinical outcomes.
骨关节炎（OA）是一种多方面的退行性关节疾病，受多种风险因素影响，如年龄、机械应力、炎症和代谢影响。这些因素导致了其多样的表型和内型，凸显了该疾病固有的复杂性。多种组织的参与及其相互作用进一步使骨关节炎的研究变得复杂。空间表型分析技术目前的局限性，加上多因素相互作用的复杂网络，阻碍了可靠的早期诊断标志物的发现以及量身定制的治疗策略的开发。然而，时空分析的最新进展彻底改变了研究人员探索骨关节炎时空动态的能力。这些进展为疾病的进展提供了前所未有的见解，揭示了患者特异性的临床表现、组织和关节结构改变，以及组织细胞群体和细胞外基质中从微观到分子的变化。本文总结了利用先进技术对骨关节炎时空变化进行深度表型分析的最新进展，强调了它们在阐明骨关节炎病理生理学中的关键作用，以及这如何改变临床实践并推进个性化治疗方法，最终带来更好的临床结果。
