Raman spectroscopy’s potential to identify diseased cartilage in osteoarthritis

Osteoarthritis is the leading cause of chronic disability in the elderly. In 2003, the World Health Organisation estimated that it affects 9.6% of men and 18% of women over the age of 60ⁱ. Patient treatment could be significantly improved if we could detect the first signs of cartilage degeneration earlier.

A group of researchers and clinicians at University College London, Royal Veterinary College and Charing Cross Hospital, led by Dr Riana Gaifulina, have investigated this. They have demonstrated that Raman spectroscopy could determine the grade of cartilage erosion and overall osteoarthritis disease state. This could lead to an earlier identification and treatment of osteoarthritic degeneration.

Rapid non-invasive Raman spectroscopy can detect small molecular changes in cartilage constituents. They looked for the target molecule, sulphated glycosaminoglycan (sGAG), in cartilage samples, using a Renishaw inVia™ confocal Raman microscope to acquire single spectra across the cartilage surface. This gave a comprehensive and representative analysis of the samples, so they could assess the progression of cartilage degradation. The researchers were able to identify the sGAG S-O stretching bond as an indicator of late stage cartilage damage by using Renishaw's WiRE™ curve fitting algorithm to analyse the spectral range, 980 cm^-1 to 1120 cm^-1 and, in particular, the 1063:1003 cm^-1 peak ratio.

The researchers also performed principal component analysis (PCA) on both the high wavenumber and fingerprint spectral regions, and found that the high wavenumber region could more easily discriminate early disease.

Then, knowing which peaks are indicative of disease, the researchers switched to the Renishaw RA816 Biological Analyser, as this is ideally suited to routine –use. They rapidly Raman imaged healthy and diseased areas of cartilage within multiple samples with Renishaw's fast StreamLine™ mapping technology. They found that Raman mapping could reveal significant differences between healthy cartilage and apparently normal looking cartilage from a lesioned joint, as well as the spatial distribution of cartilage constituents.