Fourier Transform Infrared Spectroscopic Imaging and Multivariate Regression for Prediction of Proteoglycan Content of Articular Cartilage

Fourier Transform Infrared (FT-IR) spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG) contents of articular cartilage (AC). However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR) and principal component regression (PCR) methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O –stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization) or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used

Scatter plots between optical density of safranin O and different univariate FT-IR PG parameters.

<p>A) Integrated area of carbohydrate region (984–1140 cm⁻¹). B) Integrated area of carbohydrate region normalized with amide I (1584–1720 cm⁻¹). C) Intensity of ^2nd derivative peak located at 1374 cm⁻¹. D) Intensity of 2^nd derivative peak located at 1062 cm⁻¹. Statistical significance of <i>p</i><0.001 is indicated by two asterisks (**).</p

Mean absorption spectrum and second derivative spectrum of the data set.

<p>A) Mean infrared absorption spectrum of AC and B) mean second derivative spectrum of AC.</p

An enzymatically degraded (left) and a normal (right) sample.

<p>Safranin O –stained sections are shown in A and B. Predicted PG contents by the PLSR model are shown in C and D. The loss of superficial PGs is easily seen in C. Depthwise distribution profiles of the same samples analyzed by the PLSR model (black) and by digital densitometry (red) are shown in E and F. The difference between the predicted and measured values are marked with dashed lines in E and F. RMSEs were 0.30 OD and 0.13 OD for the enzymatically modified sample and intact sample, respectively.</p

Scatter plots between optical density of safranin O and FT-IR multivariate models.

<p>A) Predicted PG content obtained from PLSR model. B) Predicted PG content obtained from PCR model. Statistical significance of <i>p</i><0.001 is indicated by two asterisks (**).</p

Root-mean-square error of cross-validation (RMSECV) with different number of components for PLSR and PCR.

<p>Based on RMSECV, 7 components were selected for both models as RMSECV did not decrease significantly with additional components.</p

Site-specific ultrasound reflection properties and superficial collagen content of bovine knee articular cartilage

Previous quantitative 2D-ultrasound imaging studies have demonstrated that the ultrasound reflection measurement of articular cartilage surface sensitively detects degradation of the collagen network, whereas digestion of cartilage proteoglycans has no significant effect on the ultrasound reflection. In this study, the first aim was to characterize the ability of quantitative 2D-ultrasound imaging to detect site-specific differences in ultrasound reflection and backscattering properties of cartilage surface and cartilage-bone interface at visually healthy bovine knee (n = 30). As a second aim, we studied factors controlling ultrasound reflection properties of an intact cartilage surface. The ultrasound reflection coefficient was determined in time (R) and frequency domains (IRC) at medial femoral condyle, lateral patello-femoral groove, medial tibial plateau and patella using a 20 MHz ultrasound imaging instrument. Furthermore, cartilage surface roughness was quantified by calculating the ultrasound roughness index (URI). The superficial collagen content of the cartilage was determined using a FT-IRIS-technique. A significant site-dependent variation was shown in cartilage thickness, ultrasound reflection parameters, URI and superficial collagen content. As compared to R and IRC, URI was a more sensitive parameter in detecting differences between the measurement sites. Ultrasound reflection parameters were not significantly related to superficial collagen content, whereas the correlation between R and URI was high. Ultrasound reflection at the cartilage-bone interface showed insignificant site-dependent variation. The current results suggest that ultrasound reflection from the intact cartilage surface is mainly dependent on the cartilage surface roughness and the collagen content has a less significant role

Collagen network primarily controls poisson's ratio of bovine articular cartilage in compression

The equilibrium Young's modulus of articular cartilage is known to be primarily determined by proteoglycans (PGs). However, the relation between the Poisson's ratio and the composition and structure of articular cartilage is more unclear. In this study, we determined Young's modulus and Poisson's ratio of bovine articular cartilage in unconfined compression. Subsequently, the same samples, taken from bovine knee (femoral, patellar and tibial cartilage) and shoulder (humeral cartilage) joints, were processed for quantitative microscopic analysis of PGs, collagen content, and collagen architecture. The Young's modulus, Poisson's ratio, PG content (estimated with optical density measurements), collagen content, and birefringence showed significant topographical variation (p < 0.05) among the test sites. Experimentally the Young's modulus was strongly determined by the tissue PG content (r = 0.86, p < 0.05). Poisson's ratio revealed a significant negative linear correlation (r = -0.59, p < 0.05) with the collagen content, as assessed by the Fourier transform infrared imaging. Finite element analyses, conducted using a fibril reinforced biphasic model, indicated that the mechanical properties of the collagen network strongly affected the Poisson's ratio. We conclude that Poisson's ratio of articular cartilage is primarily controlled by the content and organization of the collagen network

Mechano-acoustic determination of Young's modulus of articular cartilage

The compressive stiffness of an elastic material is traditionally characterized by its Young's modulus. Young's modulus of articular cartilage can be directly measured using unconfined compression geometry by assuming the cartilage to be homogeneous and isotropic. In isotropic materials, Young's modulus can also be determined acoustically by the measurement of sound speed and density of the material. In the present study, acoustic and mechanical techniques, feasible for in vivo measurements, were investigated to quantify the static and dynamic compressive stiffness of bovine articular cartilage in situ. Ultrasound reflection from the cartilage surface, as well as the dynamic modulus were determined with the recently developed ultrasound indentation instrument and compared with the reference mechanical and ultrasound speed measurements in unconfined compression (n = 72). In addition, the applicability of manual creep measurements with the ultrasound indentation instrument was evaluated both experimentally and numerically. Our experimental results indicated that the sound speed could predict 47% and 53% of the variation in the Young's modulus and dynamic modulus of cartilage, respectively. The dynamic modulus, as determined manually with the ultrasound indentation instrument, showed significant linear correlations with the reference Young's modulus (r = 0.445, p < 0.01, n = 70) and dynamic modulus (r = 0.779, p < 0.01, n = 70) of the cartilage. Numerical analyses indicated that the creep measurements, conducted manually with the ultrasound indentation instrument, were sensitive to changes in Young's modulus and permeability of the tissue, and were significantly influenced by the tissue thickness. We conclude that acoustic parameters, i.e. ultrasound speed and reflection, are indicative to the intrinsic mechanical properties of the articular cartilage. Ultrasound indentation instrument, when further developed, provides an applicable tool for the in vivo detection of cartilage mechano-acoustic properties. These techniques could promote the diagnostics of osteoarthrosis