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Abstract Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.

Purpose: To investigate the relationship between the supratrochlear rim and isolated patellar chondromalacia (PC) using magnetic resonance imaging (MRI) scans of the knee. Methods: Patients without patellofemoral pain (control group) and patients with patellofemoral pain and diagnosed with stage III or IV PC based on MRI (defect group) were retrospectively identified. Patients with a history of patellar subluxation were excluded. We used patient MRI scans to perform 20 anatomical measurements of the patellofemoral joint. We also performed 2 measurements of the anterior femoral curvature. A total of 30 patients (29 ± 8.7 years) were in the control group, and 20 patients were in the defect group (29.4 ± 9.7 years). Results: The maximum curvature (P < .001) and mean curvature (P < .001) of the anterior femoral condyle were found statistically significantly different between the groups. Patellotrochlear index (P = .03) and Insall-Salvati index (P < .001) were also found statistically significantly different between the 2 groups. Patella type III and trochlear dysplasia grade B were found more common in the defect group. Conclusions: We have shown through MRI knee measurements that the isolated patellar chondromalacia in patients without a history of patellar subluxation and dislocation is correlated with the increased anterior femoral curvature in combination with patella alta. Level of Evidence: Level III, progostic case-control.