Study of the collagen structure in the superficial zone and physiological state of articular cartilage using a 3D confocal imaging technique

Introduction

The collagen structure in the superficial zone of articular cartilage is critical to the tissue's durability. Early osteoarthritis is often characterized with fissures on the articular surface. This is closely related to the disruption of the collagen network. However, the traditional histology can not offer visualization of the collagen structure in articular cartilage because it uses conventional optical microscopy that does not have insufficient imaging resolution to resolve collagen from proteoglycans in hyaline articular cartilage. This study examines the 3D collagen network of articular cartilage scored from 0 to 2 in the scoring system of International Cartilage Repair Society, and aims to develop a 3D histology for assessing early osteoarthritis.

Methods

Articular cartilage was visually classified into five physiological groups: normal cartilage, aged cartilage, cartilage with artificial and natural surface disruption, and fibrillated. The 3D collagen matrix of the cartilage was acquired using a 3D imaging technique developed previously. Traditional histology was followed to grade the physiological status of the cartilage in the scoring system of International Cartilage Repair Society.

Results

Normal articular cartilage contains interwoven collagen bundles near the articular surface, approximately within the lamina splendens. However, its collagen fibres in the superficial zone orient predominantly in a direction spatially oblique to the articular surface. With age and disruption of the articular surface, the interwoven collagen bundles are gradually disappeared, and obliquely oriented collagen fibres change to align predominantly in a direction spatially perpendicular to the articular surface. Disruption of the articular surface is well related to the disappearance of the interwoven collagen bundles.

Conclusion

A 3D histology has been developed to supplement the traditional histology and study the subtle changes in the collagen network in the superficial zone during early physiological alteration of articular cartilage. The fibre confocal imaging technology used in this study has allowed developing confocal arthroscopy for in vivo studying the chondrocytes in different depth of articular cartilage. Therefore, the current study has potential to develop an in vivo 3D histology for diagnosis of early osteoarthritis.

Matrix-induced autologous chondrocyte implantation in sheep: objective assessments including confocal arthroscopy

The assessment of cartilage repair has largely been limited to macroscopic observation, magnetic resonance imaging (MRI), or destructive biopsy. The aims of this study were to establish an ovine model of articular cartilage injury repair and to examine the efficacy of nondestructive techniques for assessing cartilage regeneration by matrix-induced autologous chondrocyte implantation (MACI). The development of nondestructive assessment techniques facilitates the monitoring of repair treatments in both experimental animal models and human clinical subjects. Defects (Ø 6 mm) were created on the trochlea and medial femoral condyle of 21 sheep randomized into untreated controls or one of two treatment arms: MACI or collagen-only membrane. Each group was divided into 8-, 10-, and 12-week time points. Repair outcomes were examined using laser scanning confocal arthroscopy (LSCA), MRI, histology, macroscopic ICRS grading, and biomechanical compression analysis. Interobserver analysis of the randomized blinded scoring of LSCA images validated our scoring protocol. Pearson correlation analysis demonstrated the correlation between LSCA, MRI, and ICRS grading. Testing of overall treatment effect independent of time point revealed significant differences between MACI and control groups for all sites and assessment modalities (Asym Sig < 0.05), except condyle histology. Biomechanical analysis suggests that while MACI tissue may resemble native tissue histologically in the early stages of remodeling, the biomechanical properties remain inferior at least in the short term. This study demonstrates the potential of a multisite sheep model of articular cartilage defect repair and its assessment via nondestructive methods.

Laser scanning confocal arthroscopy of a fresh cadaveric knee joint

OBJECTIVE

Osteoarthritis (OA) inflicts an enormous burden upon sufferers and healthcare systems worldwide. Continuing efforts to elucidate the aetiology of OA have indicated the need for non-destructive methods of in vivo microstructural assessment of articular cartilage (AC). In this study, we describe the first use of a recently developed laser scanning confocal arthroscope (LSCA) to image the cartilage of a fresh frozen cadaveric knee from a patient with OA.

DESIGN

Using an adaptation of the International Cartilage Repair Society (ICRS) joint mapping protocol, the joint was divided into three discrete regions (femoral condyle, patella and tibial plateau) for grading according to the ICRS (Outerbridge) system. The LSCA was used to generate images from each area within the three regions. Following imaging, the joint was sectioned and histology was performed on the corresponding sites with histological grading (modified-Mankin).

RESULTS

Quantitative results of ICRS, LSCA and histological OA assessment were compared using intraclass correlation (ICC) and Pearson correlation analysis. The LSCA enabled visualisation of chondrocyte morphology and cell density, with classical OA changes such as chondrocyte clustering, surface fibrillation and fissure formation evident. Obvious qualitative similarities between LSCA images and histology were observed, with fair to moderate agreement (P<0.05) demonstrated between modalities.

CONCLUSIONS

In this study, we have shown the viability of the LSCA for non-destructive imaging of the microstructure of OA knee cartilage. LSCA technology is potentially a valuable research and clinical tool for the non-destructive assessment of AC microstructure in early to late OA.

Confocal arthroscopy-based patient-specific constitutive models of cartilaginous tissues - II: prediction of reaction force history of meniscal cartilage specimens

The theoretical framework developed in a companion paper (Part I) is used to derive estimates of mechanical response of two meniscal cartilage specimens. The previously developed framework consisted of a constitutive model capable of incorporating confocal image-derived tissue microstructural data. In the present paper (Part II) fibre and matrix constitutive parameters are first estimated from mechanical testing of a batch of specimens similar to, but independent from those under consideration. Image analysis techniques which allow estimation of tissue microstructural parameters form confocal images are presented. The constitutive model and image-derived structural parameters are then used to predict the reaction force history of the two meniscal specimens subjected to partially confined compression. The predictions are made on the basis of the specimens' individual structural condition as assessed by confocal microscopy and involve no tuning of material parameters. Although the model does not reproduce all features of the experimental curves, as an unfitted estimate of mechanical response the prediction is quite accurate. In light of the obtained results it is judged that more general non-invasive estimation of tissue mechanical properties is possible using the developed framework.

Confocal arthroscopy-based patient-specific constitutive models of cartilaginous tissues—I: development of a microstructural model

Current development of a laser scanning confocal arthroscope within our school will enable 3D microscopic imaging of joint tissues in vivo. Such an instrument could be useful, for example, in assessing the microstructural condition of the living tissues without physical biopsy. It is envisaged also that linked to a suitable microstructural constitutive formulation, such imaging could allow non-invasive patient-specific estimation of tissue mechanical performance. Such a procedure could have applications in surgical planning and simulation, and assessment of engineered tissue replacements, where tissue biopsy is unacceptable. In this first of two papers the development of a suitable constitutive framework for generating such estimates is reported. A microstructure-based constitutive formulation for cartilaginous tissues is presented. The model extends existing fibre composite-type models and accounts for strain-rate sensitivity of the tissue mechanical response through incorporation of a viscoelastic fibre phase. Importantly, the model is constructed so as to allow direct incorporation of structural data from confocal images. A finite element implementation of the formulation suitable for incorporation within commercial codes is also presented.

Automated classification of articular cartilage surfaces based on surface texture

In this study the automated classification system previously developed by the authors was used to classify articular cartilage surfaces with different degrees of wear. This automated system classifies surfaces based on their texture. Plug samples of sheep cartilage (pins) were run on stainless steel discs under various conditions using a pin-on-disc tribometer. Testing conditions were specifically designed to produce different severities of cartilage damage due to wear. Environmental scanning electron microscope (SEM) (ESEM) images of cartilage surfaces, that formed a database for pattern recognition analysis, were acquired. The ESEM images of cartilage were divided into five groups (classes), each class representing different wear conditions or wear severity. Each class was first examined and assessed visually. Next, the automated classification system (pattern recognition) was applied to all classes. The results of the automated surface texture classification were compared to those based on visual assessment of surface morphology. It was shown that the texture-based automated classification system was an efficient and accurate method of distinguishing between various cartilage surfaces generated under different wear conditions. It appears that the texture-based classification method has potential to become a useful tool in medical diagnostics.