Abstract
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.