Osteoarthritis is the most common disease in articular cartilage. Raman spectroscopy is a promising tool for early detection of degenerative changes in cartilage matrix. In this study, surgically resected humeral heads of 14 patients were subjected to pathological analysis using Raman spectroscopy with principal component analysis and hierarchical clustering analysis. In the result, Raman spectral data of each specimen were divided into three major cluster reflecting the alteration in molecular composition of cartilage matrix. We also found histological characteristics in the cluster, suggesting that Raman spectrum is a biomarker to determine the condition of the cartilage tissue.
SignificanceRaman spectroscopy is a well-established analytical method in the fields of chemistry, industry, biology, pharmaceutics, and medicine. Previous studies have investigated optical imaging and Raman spectroscopy for osteoarthritis (OA) diagnosis in weight-bearing joints such as hip and knee joints. However, to realize early diagnosis or a curable treatment, it is still challenging to understand the correlations with intrinsic factors or patients’ background.AimTo elucidate the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage.ApproachOsteoarthritic cartilage specimens excised from the humeral heads of 14 patients who underwent shoulder arthroplasty were assessed by a confocal Raman microscope and histological staining. The Raman spectroscopic dataset of degenerative cartilage was further analyzed by principal component analysis and hierarchical cluster analysis.ResultsMultivariate association of the Raman spectral data generated three major clusters. The first cluster of patients shows a relatively high Raman intensity of collagen. The second cluster displays relatively low Raman intensities of proteoglycans (PGs) and glycosaminoglycans (GAGs), whereas the third cluster shows relatively high Raman intensities of PGs and GAGs. The reduced PGs and GAGs are typical changes in OA cartilage, which have been confirmed by safranin–O staining. In contrast, the increased Raman intensities of collagen, PGs, and GAGs may reflect the instability of the cartilage matrix structure in OA patients.ConclusionsThe results obtained confirm the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage. Unsupervised machine learning methods successfully yielded a clinically meaningful classification between the shoulder OA patients. This approach not only has potential to confirm severity of cartilage defects but also to determine the origin of an individual’s OA by evaluating the cartilage quality.
Osteoarthritis (OA) is a very common joint disease in the aging population. Main symptom of OA is accompanied by degenerative changes of articular cartilage. Cartilage contains mostly type II collagen and proteoglycans, so it is difficult to access the quality and morphology of cartilage tissue in situ by conventional diagnostic tools (X-ray, MRI and echography) directly or indirectly. Raman spectroscopy is a label-free technique which enables to analyze molecular composition in degenerative cartilage. In this proposal, we aim to develop Raman spectroscopic system for the quality assessment of articular cartilage during arthroscopic surgery. Toward this goal, we are focusing on the proteoglycan content and collagen fiber alignment in cartilage matrix which may be associated with degenerative changes in OA, and we designed an original Raman device for remote sensing during arthroscopic surgery. In this project, we define the grading system for cartilage defect based on Raman spectroscopy, and we complete the evaluation of the Raman probing system which makes it possible to detect early stage of degenerative cartilage as a novel tool for OA diagnosis using human subject.
Osteoarthritis (OA) is very common joint disease in the aging population. Main symptom of OA is accompanied by degenerative changes of articular cartilage. Raman spectroscopy is a label-free technique which enables to analyze molecular composition in degenerative cartilage. We generated an animal OA model surgically induced by knee joint instability and performed Raman spectroscopic analysis for the articular cartilage. In the result, Raman spectral data of the articular cartilage showed drastic changes in comparison between OA and control side. The relative intensity of phosphate band increases in the degenerative cartilage.
Osteoarthritis (OA) is very common joint disease in the aging population. Main symptom of OA is accompanied by degenerative changes of articular cartilage. Cartilage contains mostly type II collagen and proteoglycans, so it is difficult to access the quality and morphology of cartilage tissue in situ by conventional diagnostic tools (X-ray, MRI and echography) directly or indirectly. Raman spectroscopy is a label-free technique which enables to analyze molecular composition in degenerative cartilage. In this study, we generated an animal OA model surgically induced by knee joint instability, and the femurs were harvested at two weeks after the surgery. We performed Raman spectroscopic analysis for the articular cartilage of distal femurs in OA side and unaffected side in each mouse. In the result, there is no gross findings in the surface of the articular cartilage in OA. On the other hand, Raman spectral data of the articular cartilage showed drastic changes in comparison between OA and control side. The major finding of this study is that the relative intensity of phosphate band (960 cm-1) increases in the degenerative cartilage. This may be the result of exposure of subchondral bone due to thinning of the cartilage layer. In conclusion, Raman spectroscopic technique is sufficient to characterize articular cartilage in OA as a pilot study for Raman application in cartilage degeneration and regeneration using animal models and human subjects.
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