“The biological and functional properties of articular cartilage depend mainly on the concentration, structure, and interaction between collagen and proteoglycan (PG), the two primary compositional molecules within the extracellular matrix, and water. Structurally, collagen is woven into a three-dimensional (3D) fibril network that enmeshes the negatively charged PG, allowing the tissue’s absorption of positively charged ions that are osmotically balanced by an influx of water. This specific arrangement of collagen/PG/water provides articular cartilage with its compressive resistance and mechanical resiliency. Either a disruption of the collagen network or a decrease in the PG content in cartilage would result in deterioration of the tissue’s biomechanical properties, which may develop into pathological conditions such as osteoarthritis. The ability to quantitatively determine the concentration distributions of both collagen and PG in articular cartilage, therefore, becomes critically important to monitor the progressive degradation of tissue.
Fourier transform infrared (FT-IR) spectroscopy is sensitive
to the vibrational motions of the molecules’ dipole moments in tissue specimens. Coupled with an infrared microscope, FT-IR imaging (FT-IRI) makes it possible to spatially resolve various chemical signatures with fine spatial pixel size of 6.25 um and spectral resolution … and has been used in recent years to study cartilage tissue. …There have also been attempts to link the individual absorption peak areas in FT-IRI to the molecular concentrations
in cartilage. These peak-intensity/area based approaches are problematic because the co-existence of multiple molecular components in any biological tissue often results in significant overlap of many absorption bands in the middle infrared region, diminishing any possibility of linking the
infrared absorption directly to the molecular concentration. Due to this reason, a curve-fitting method was recently used to overcome the overlap of absorption bands in PG analysis.
Principal component regression (PCR) is a common chemometric method based on factor analysis that fully utilizes all spectral data in multivariate analysis. As such, it is possible for PCR to identify the number of molecular components in a mixed sample and to calculate the component concentrations in
unknown samples according to the standard spectral library. The current investigation, the first project that correlated the quantitative biochemistry with the combined FT-IRI and PCR
approach in cartilage research, aimed to determine the molecular concentrations (collagen and PG) in cartilage quantitatively….”
Xia's laboratory is funded by two grants from the National Institutes of Health.
Postdoc JianHua Yin and Prof Yang Xia recently published a study of cartilage in the journal Applied Spectroscopy.
Created by Brad Roth (firstname.lastname@example.org) on Friday, December 10, 2010 Modified by Brad Roth (email@example.com) on Friday, December 10, 2010 Article Start Date: Friday, December 10, 2010