Multivariate Analysis of Raman Spectra for Discriminating Human Collagens

Background: The NHS spends £4.3 billion annually to address musculoskeletal conditions, encompassing age-related bone disorders like osteoarthritis and osteoporosis. Traditional X-ray diagnostic methods are commonly employed for bone disorder diagnosis, primarily assessing gross anatomical bone structure changes. However, these methods are unable to identify subtle biochemical alterations within the bone. More detailed information, particularly about protein changes, may lead to enhanced diagnostics and treatment. Raman spectroscopy is a non-invasive, laser-based technique capable of detecting changes in the collagen component of bone. Despite its long-standing application in discerning mineral and protein changes within bone, there is limited evidence on Raman spectral signatures of purified human collagens and their differentiation. This study aimed to test the hypothesis that Raman spectroscopy could detect different types of collagen in the human body.
Results: A Raman microspectrometer with a 785nm laser was used to measure unmineralized human collagens types I – VI and collagenous extracellular matrix (ECM) secreted by MG63 osteoblast-like cells. The results demonstrated the efficacy of Raman spectroscopy and subsequent multivariate analysis in distinguishing human collagen types I – VI. This implies that Raman spectroscopy, coupled with multivariate analysis, can identify pure human collagens and offers reference spectra similar to natural human collagen in the bone extracellular matrix.
Significance: This study establishes Raman spectroscopy as a tool for identifying and characterizing human collagens, aiding in the diagnosis of connective tissue disorders. The creation of a spectral reference library for pure human collagen types I – VI holds potential for medical diagnostics, analytical chemistry, and materials science applications.