Characterization of articular cartilage by combining microscopic analysis with a fibril-reinforced finite-element model
Introduction
Articular cartilage consists of a three-dimensional collagen network, proteoglycans (PGs), interstitial water and chondrocytes (Buckwalter and Martin, 1995; Mow et al., 1991). Several external and internal factors, e.g. abnormal or impact mechanical loading or joint inflammation, may lead to degradation of collagen and PGs with a consequence of impaired functional properties of the tissue (Armstrong and Mow, 1982; Mow et al., 1992). Cartilage injury may further lead to osteoarthritis (OA) (Buckwalter and Martin, 1995). Structural changes in the cartilage matrix can be revealed by imaging techniques, such as high resolution magnetic resonance imaging (MRI) or quantitative microscopy (Park et al., 2003; Rieppo et al., 2003; Wheaton et al., 2005). However, these imaging techniques are unable to quantitate changes in functional properties of the cartilage, sensitively impaired in early OA (Saarakkala et al., 2003). Those changes can only be reliably revealed by experimental mechanical tests. However, optimal interpretation of the mechanical data necessitates the use of a valid theoretical model (Guilak et al., 1994; Korhonen et al., 2003; Mow et al., 1991; Saarakkala et al., 2004).
Several theoretical models have been developed to characterize mechanical behavior of articular cartilage. Most of them cannot capture the effect of depth-dependent collagen structure on the mechanical characteristics (Cohen et al., 1998; DiSilvestro et al., 2001; Li et al., 1999; Soltz and Ateshian, 2000). Li et al. (2000) modeled the depth-wise mechanical behavior of collagen, however, actual collagen network structure was not included. Inhomogeneous and anisotropic structure of articular cartilage can be retrieved from MRI or microscopical analyses (Király et al., 1997; Nieminen et al., 2001). By taking into account the structural inhomogeneity and depth dependency, determination of the early stages of cartilage degeneration may be more realistic. In addition, prediction of the onset and progression of OA, e.g. due to abnormal mechanical loading after meniscectomy, may be improved (LeRoux et al., 2000; Wilson et al., 2003, Wilson et al., 2006). Inherently, stress and strain distributions will change in cartilage during tissue degeneration, inducing severe variations e.g. in the mechanical signals sensed by the chondrocytes (Guilak et al., 1994; Mow et al., 1992). Alterations in the depth-dependent collagen architecture may play a critical role in these pathologies. The fibril-reinforced poroviscoelastic (FRPVE) model (Wilson et al., 2004) includes depth-dependent collagen orientation and takes into account the compression tension nonlinearity of articular cartilage. Thereby, the microscopical FRPVE model may be used to characterize local structure–function relationships and interstitial stress/strain patterns of cartilage.
Earlier, structure–function relationships in articular cartilage have been studied by combining structural analysis and mechanical tests (LeRoux et al., 2000; Nieminen et al., 2000). Most of these studies have not utilized theoretical models in order to obtain more comprehensive relationships between the structure and mechanical behavior of collagen network, PGs and fluid. A few studies have combined theoretical models with the structural analysis of collagen or PGs (Lu et al., 2004; Tran-Khanh et al., 2005), but they lack the realistic anisotropic collagen orientation or strain-dependent permeability of cartilage and do not consider site-dependent changes in the mechanical and structural properties of the tissue.
In the present study, the main hypothesis was that the experimentally determined collagen and PG contents can be related to corresponding model components, i.e. fibril network stiffness and PG matrix stiffness, respectively. These relations have not been addressed simultaneously before. Thereby, we aimed to investigate the relationships between the FRPVE model parameters of bovine articular cartilage and the tissue collagen and PG content, as assessed experimentally using quantitative microscopy. For this purpose, mechanical testing, microscopic analyses and finite-element (FE) analyses were carried out for cartilage samples from the bovine knee and shoulder joints. Topographical variations in the values of model parameters, as derived from the analyses of experimental stress-relaxation measurements, and predictions for stress and lateral displacement distributions are also presented.
Section snippets
Sample preparation
Bovine knee and shoulder joints were supplied by a local slaughterhouse (Atria Oy, Kuopio, Finland). Cylindrical, full thickness cartilage samples from bovine femur, tibia, patella and humerus were harvested using a razor blade and a biopsy punch of 3.7 mm in diameter (Korhonen et al., 2002). Before mechanical testing, the sample diameters were measured with a stereomicroscope (Nikon SMZ-10, Nikon Inc., Japan) (Table 1).
Mechanical testing
Stepwise stress-relaxation experiments in unconfined compression geometry
Results
The experimental stress-relaxation tests could be successfully simulated, as shown by the accurately fitted model curves to the experimental data (Fig. 2). E0 and Eε correlated linearly with the collagen content (r=0.74, p<0.001 and r=0.82, p<0.001, respectively, Fig. 3, Table 2), as measured with FTIRI. Lower, but significant linear associations were found between the fibril network moduli (E0 and Eε) and PG content (OD) (r=0.54, p<0.01 and r=0.55, p<0.01, respectively, Table 2). Em and M
Discussion
In the present study, structure–function relationships of bovine articular cartilage were characterized by combining information from the quantitative microscopy with the depth-dependent FE analyses. Model parameters correlated significantly with the collagen and PG contents. All structural and mechanical parameters showed site-specific variations in the bovine knee and humeral cartilage. Concomitantly, stress and strain distributions, as characterized by the FRPVE model, showed specific
Acknowledgments
Financial support from the Kuopio University Hospital, Kuopio, Finland (EVO 5031335); The Academy of Finland (Project nos. 206255 and 205886); The Alberta Heritage Foundation for Medical Research, Alberta, Canada; Sigrid Jusélius Foundation, Helsinki, Finland; The Invalid Foundation, Helsinki, Finland; The Finnish Cultural Foundation of Northern Savo, Kuopio, Finland; Foundation for Technology Advancement, Helsinki, Finland; and Biomaterial Graduate School, Helsinki, Finland is acknowledged.
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