Oinas, J. et al. Imaging of Osteoarthritic Human Articular Cartilage using Fourier Transform Infrared Microspectroscopy Combined with Multivariate and Univariate Analysis. Sci. Rep. 6, 30008; doi: 10.1038/srep30008 (2016).
Imaging of osteoarthritic human articular cartilage using fourier transform infrared microspectroscopy combined with multivariate and univariate analysis
|Author:||Oinas, Joonas1,2; Rieppo, Lassi1,2; Finnilä, Mikko A.1,2,3;|
1Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Finland
2Medical Research Center, University of Oulu and Oulu University Hospital, Finland
3Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
4Department of Surgery, Oulu University Hospital, Finland
5Research Group of Cancer and Translational Medicine, Faculty of Medicine, University of Oulu, Finland
6Department of Diagnostic Radiology, Oulu University Hospital, Finland
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2016121431296
|Publish Date:|| 2016-12-15
The changes in chemical composition of human articular cartilage (AC) caused by osteoarthritis (OA) were investigated using Fourier transform infrared microspectroscopy (FTIR-MS). We demonstrate the sensitivity of FTIR-MS for monitoring compositional changes that occur with OA progression. Twenty-eight AC samples from tibial plateaus were imaged with FTIR-MS. Hyperspectral images of all samples were combined for K-means clustering. Partial least squares regression (PLSR) analysis was used to compare the spectra with the OARSI grade (histopathological grading of OA). Furthermore, the amide I and the carbohydrate regions were used to estimate collagen and proteoglycan contents, respectively. Spectral peak at 1338 cm−1 was used to estimate the integrity of the collagen network. The layered structure of AC was revealed using the carbohydrate region for clustering. Statistically significant correlation was observed between the OARSI grade and the collagen integrity in the superficial (r = −0.55) and the deep (r = −0.41) zones. Furthermore, PLSR models predicted the OARSI grade from the superficial (r = 0.94) and the deep (r = 0.77) regions of the AC with high accuracy. Obtained results suggest that quantitative and qualitative changes occur in the AC composition during OA progression, and these can be monitored by the use of FTIR-MS.
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