Spatial mapping of proteoglycan content in articular cartilage using near-infrared (NIR) spectroscopy

Abstract

Diagnosis of articular cartilage pathology in the early disease stages using current clinical diagnostic imaging modalities is challenging, particularly because there is often no visible change in the tissue surface and matrix content, such as proteoglycans (PG). In this study, we propose the use of near infrared (NIR) spectroscopy to spatially map PG content in articular cartilage. The relationship between NIR spectra and reference data (PG content) obtained from histology of normal and artificially induced PG-depleted cartilage samples was investigated using principal component (PC) and partial least squares (PLS) regression analyses. Significant correlation was obtained between both data (R 2 = 91.40%, p<0.0001). The resulting correlation was used to predict PG content from spectra acquired from whole joint sample, this was then employed to spatially map this component of cartilage across the intact sample. We conclude that NIR spectroscopy is a feasible tool for evaluating cartilage contents and mapping their distribution across mammalian joint. 503-512 (2010). 11. X. Bi, X. Yang, M. P. G. Bostrom, and N. P. Camacho, "Fourier transform infrared imaging spectroscopy investigations in the pathogenesis and repair of cartilage," Biochim.

Optical spectroscopic determination of human meniscus composition

“…The findings in this study are supported by results from earlier studies on articular cartilage, which consists of similar matrix components as meniscus, although the collagen types differ. The NIR region where correlation was optimized is also consistent with earlier studies on cartilage . It is worth noting at this stage that the present optical method is not proposed as a replacement for the reference techniques, but merely as a means of estimating the composition of meniscus during arthroscopy.…”

“…The proposed optical technique takes advantage of light interaction with meniscus matrix on a molecular level, and is sensitive to micro‐ and macroscopic changes in the tissue structure and composition . Thus, it could be useful for rapid tracking of changes in meniscus composition, resulting from either matrix degeneration or tissue recovery/regeneration in follow up procedures after repair surgeries.…”

Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy

“…As such, the main changes observed in samples with early matrix alteration are related to changes in PG content, which influences the water content and potentially the superficial collagen structure, all of which are detectable using NIRS. 1,2,22,30 This result suggests that cartilage degeneration, and thus changes in its spectral features, may not follow a linear trend, and therefore may benefit from a non-linear modelling approach such as neural networks, including shallow and deep networks.…”

“…There has been growing interest in utilizing fast optical methods, particularly near infrared spectroscopy (NIRS), for the evaluation of cartilage conditions. 1,2,[5][6][7]19,22,27,28,33 NIRS is capable of characterizing cartilage physical 5,27 and functional 6,27 properties, and composition. 1,22,28 Furthermore, NIRS has been proposed for assessing 4,23,32 cartilage integrity and monitoring the progression of OA using an animal model, 3 which provides in vivo and ex vivo data during methods development and optimization.…”

Vibrational spectroscopy of articular cartilage

“…A PLS regression model built with the calibration samples was applied to the spectroscopic mapping data of a bovine patella with a focal defect. The analysis revealed a loss of proteoglycans in the focal defect area (86). In another study based on enzymatic degradation of the ECM, multivariate modeling of NIR spectra was used to distinguish normal and enzymatically degraded bovine patellae.…”