Original Article
Investigations of micron and submicron wear features of diseased human cartilage surfaces
Proc IMechE Part H: J Engineering in Medicine 2015, Vol. 229(2) 164–174 Ó IMechE 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0954411915572496 pih.sagepub.com
Zhongxiao Peng1, Juan C Baena1 and Meiling Wang1,2
Abstract Osteoarthritis is a common disease. However, its causes and morphological features of diseased cartilage surfaces are not well understood. The purposes of this research were (a) to develop quantitative surface characterization techniques to study human cartilages at a micron and submicron scale and (b) to investigate distinctive changes in the surface morphologies and biomechanical properties of the cartilages in different osteoarthritis grades. Diseased cartilage samples collected from osteoarthritis patients were prepared for image acquisition using two different techniques, that is, laser scanning microscopy at a micrometer scale and atomic force microscopy at a nanometer scale. Three-dimensional, digital images of human cartilages were processed and analyzed quantitatively. This study has demonstrated that high-quality three-dimensional images of human cartilage surfaces could be obtained in a hydrated condition using laser scanning microscopy and atomic force microscopy. Based on the numerical data extracted from improved image quality and quantity, it has been found that osteoarthritis evolution can be identified by specific surface features at the micrometer scale, and these features are amplitude and functional property related. At the submicron level, the spatial features of the surfaces were revealed to differ between early and advanced osteoarthritis grades. The effective indentation moduli of human cartilages effectively revealed the cartilage deterioration. The imaging acquisition and numerical analysis methods established allow quantitative studies of distinctive changes in cartilage surface characteristics and better understanding of the cartilage degradation process.
Keywords Wear features, image acquisition in three dimensions, numerical characterizations, effective indentation modulus, surface morphology, osteoarthritis
Date received: 15 September 2014; accepted: 12 January 2015
Introduction Osteoarthritis (OA) is a common disease caused by cartilage degradation and/or damage. Fundamental causes of OA are not fully understood, and objective and quantitative OA marker(s) are yet to be developed. It is recognized that the surface morphologies and biomechanical properties of articular cartilage change with the pathologic processes.1,2 Thus, studying the surface morphologic and biomechanical features can reveal cartilage defects for OA assessment and understanding.3 Due to its critical role as the bearing surface of synovial joints, the surface topographies of human knee cartilage were studied using magnetic resonance imaging (MRI),4–6 arthroscopy7 and polarized light microscopy8,9 at a millimeter scale. Cohen et al.10 used MRI to measure the thickness, contact areas, and curvature characteristics of knee cartilage. Surface smoothness
was assessed to be a potential OA marker.11 This is because human articular cartilage is anisotropic, that is, its structural distribution and mechanical and physical properties change along the direction perpendicular to the cartilage top surface. The surface morphology of the articular cartilage is directly related to the mechanical response of the surface under various loading conditions. Therefore, it is important to identify the main
1
School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW, Australia 2 College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China Corresponding author: Juan C Baena, School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW 2052, Australia. Email: [email protected]
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features of the cartilage surface that reveal the cartilage degradation progress. Furthermore, the quantitative characterization of structural changes in the cartilage surface is useful for understanding the process and for developing objective OA assessment criteria. The majority of the existing studies on the surface examinations of articular cartilages were either qualitative or based on conventional surface roughness descriptor (Ra or Sa). Ra and Sa are two-dimensional (2D) and three-dimensional (3D) amplitude parameters which calculate a roughness value on a profile (line) and on a surface (area), respectively. They have limitations in describing surface characteristics such as the predominance of ridges or scratches. Existing studies have reported that some 2D numerical parameters including Ra fail to describe significant changes in the surface morphology.12–15 Also, most of previous studies were carried out at a millimeter scale instead of micron or submicron level where an OA process initiates and accelerates to a large scale.16 More recently, scanning electron microscopy (SEM), atomic force microscopy (AFM) and laser scanning confocal microscopy (LSCM) are used to examine cartilage surfaces in three dimensions and in micron or submicron resolutions. Ghosh et al.17 used both SEM and AFM to study bovine cartilage surfaces and made comparisons of these two techniques for surface roughness measurements. Two commonly used parameters, Ra and Sa, were used in their study. Similarly, numerical studies were carried out to investigate the surface morphologies and biomechanical properties of sheep articular cartilages in various OA conditions using AFM.18 Six numerical parameters, including two field and four feature parameters, were found to be effective in describing changes in the surface topographies of worn cartilages. Later, the established numerical characterization techniques were applied to investigate distinctive morphologic features of human cartilage surfaces.19 In addition to SEM and AFM, LSCM was utilized to acquire 3D surface morphological data of sheep knee joints for developing suitable imaging and image analysis techniques.19 The surface features of human samples were then imaged using the established techniques,20 characterized using numerical parameters and compared to the features analyzed at the nanometer scale. However, the image quality and quantity of the laser images reported in that published work19 need to be improved. The image quality and raw surface data that the image contains are essential for accurate surface characterizations. For this reason, this work was carried out to improve the quality of the laser images by selecting suitable settings of the imaging facility (laser scanning microscopy (LSM)), so that noise in the image could be further minimized and reliable cartilage surface data could be acquired. In addition to good quality images, a sufficient quantity of surface data is required to ensure the accuracy and reliability of the surface morphological measurements. To generate reliable results,
an improved imaging and image processing technique was established, so high-quality images over large surface areas were captured and studied to obtain representative surface information. In parallel, AFM was used to acquire 3D images of hydrated cartilage sample at a nanometer scale. A total of 35 numerical parameters were applied to analyze the surface morphologies of the LSM and AFM images, and distinctive surface features in both micron and submicron resolutions were extracted to gain better understanding of structural changes in the surfaces.
Methodology Sample collection and preparation Human cartilage samples were collected during knee replacement operations through an orthopedic surgeon in Queensland, Australia. Prior to sample collections, OA patients were consented under the ethical approval for this study (MHSNQ Reference No.: MHS20100401-01). Over a 2-year period, a total of 28 cartilage samples in OA grades I, II and III were collected from patients aged 53–89 years. Together with the cartilage samples, their X-Ray, MRI and details which were relevant to their OA diseases were gathered. In addition, a camera was often used in the arthroscopy surgery to capture the images of the cartilage surfaces. Once fresh cartilages were obtained from hospitals, two sets of samples were prepared: one set for image acquisition using LSM and the other for nano-scale study using AFM. The cartilage samples were extracted from the femoral condyle section of the human knee joints. The selection of the location where the samples were extracted was defined after identifying the OA condition of the cartilage, which was graded by an orthopedic surgeon according to the Outerbridge grading systems.21 All sample extraction was conducted in a PC2 laboratory. Key steps on how to extract and prepare these samples are presented below. Sample preparation procedures were different for LSM and AFM examinations. For LSM imaging, samples of 15 mm 3 15 mm in size were extracted by using an electrical oscillating saw. A total of 12 samples with 4 samples in each OA grade were prepared. These samples were individually stored in petri dishes and immersed in phosphate buffered saline (PBS) solution with a pH value of 7.4. They were then stored at 220 °C. To prepare samples for AFM imaging, small cartilage samples were extracted using a biopsy punch with an internal diameter of approximately 4 mm. The thickness of the extracted samples was within a range of 2– 4 mm. According to literature,22 the thickness of healthy cartilage is in a range of 1.5 to about 3 mm, while the thickness of a diseased cartilage can range from close to a full thickness for an early OA cartilage to nearly 0 mm for a severe OA one. The thickness of the extracted samples was in a range of 2–4 mm to ensure that the top surface of the cartilage was
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supported and hydrated by the material underneath, so surface deformation would not occur during the image acquisition process. The cartilages were kept hydrated with drops of PBS during the extracting process. The samples were glued to sterile petri dishes, which were then filled with sterile PBS and protease inhibitor solution, and stored at 220 °C. The stored cartilage specimens were thawed at 4 °C for 12 h before AFM measurements were conducted.
Image acquisition and nano-indentation 3D images of the prepared cartilage samples were acquired using LSM and AFM separately. The key hardware settings and conditions are briefly summarized as follows. A 3D laser scanning microscope, manufactured by KEYENCE (model: VK-X210), was used to capture 3D surface information of the specimens in a noncontact mode and at a micrometer scale. The system uses a violet laser of a wavelength of 408 nm to acquire digital images. Prior to the imaging, a sample was thawed for 24 h from 220 °C to 4 °C. The thawed sample was located in a sample holder designed specifically to maintain the sample hydrated during the image acquisition process. In the imaging process, the laser scanning facility, set in a reflected mode, scanned the surface in the X/Y plane to acquire the spatial surface data. By moving the stage vertically controlled using a step motor with a step size in a range of 10 nm to 1 mm, a set of 2D images containing valley and peak features was acquired. The height information of the surface was then obtained by re-constructing a series of 2D images into a 3D image. The 3D image was used for the surface characterizations. On each cartilage sample, 12 LSM images, in a size of 0.68 mm 3 0.5 mm of each image, were obtained using a 203 objective lens and a step size of 0.05 mm. These images were stitched together to obtain a large surface area of approximately 2 3 2 mm2 for visual inspection and numerical analysis. In total, 144 images were taken from 12 human samples in OA grades I, II and III. In parallel, AFM images were acquired on the diseased samples in OA grades I–III. The imaging process was carried out in a Peak Force Quantitative Nanomechanical Mapping (QNM) and fluid mode. Silicon nitride probes DNP-10 with a nominal tip radius of 20 nm and a nominal spring constant of 0.35 N/m were used to image the cartilage surfaces. During the imaging process, forces in a range of 0.3– 16.5 nN were applied to the surfaces, resulting in a constant deformation of 150 nm on the surfaces. The scanning resolution was 256 3 256 pixels, and the scanning area was 5 3 5 mm2. Three to four locations were imaged on each cartilage sample. Along with the surface characterizations at the nano-scale, AFM nano-indentation was performed on a TopoMetrix (now Veeco Instruments, Plainview, NY, USA) Explorer TMX-2000 scanning probe microscope (SPM). The purpose of the nano-indentation
tests was to investigate which features, surface or nanomechanical properties could be used as an effective indicator for OA assessment. The deflection (nA)–displacement (nm) data were obtained using V-shaped Si3N4 pyramidal tips (the nominal spring constant k = 0.56 N m21; the nominal tip radius of curvature Rtip = 40 nm and the half angle a = 22.5°). The maximum loading force was ;80 nN, resulting in a maximum indentation depth of approximately 1800 nm. A total of 12 cartilage samples were indented. On each cartilage sample, 2–4 locations were randomly indented. In total, 10 repeats were made at each location. The effective indentation modulus was calculated using the pyramidal model23 shown in equation (1) F(d) =
1:4906E tan (a) 2 d 2(1 y 2 )
ð1Þ
where a is the half angle of the pyramidal tip, d is the indentation depth of the tip into a cartilage sample, E is the effective indentation modulus of a cartilage sample, F is the indentation force and n is Poisson’s ratio of human knee cartilage. Poisson’s ratio of human knee cartilage was taken as 0.5.24,25
Image processing and analyses Before performing quantitative image analyses, the LSM and AFM images were plane-corrected to remove image bow and scan distortions. Noise was also filtered out. The images were then analyzed to study the cartilage surface topographical characteristics at both micro- and nanometer scales. Numerical parameters used in this study are from four surface parameter sets, namely, amplitude, hybrid, functional and spatial descriptors.26 A total of 35 surface parameters were used in this study to characterize the surface morphologies of the human knee cartilages. The definitions or meanings of important parameters are briefly presented below. Sa and Sq are the amplitude parameters representing the arithmetic average and the standard deviation of the height distribution, respectively. The amplitude parameters evaluate specific roughness properties such as statistical average, extreme properties and shape of the height distribution histogram.27Sk and Sdc10_50 are the functional parameters that describe the bearing characteristics and fluid retention properties of the surface. The Sdc10_50 parameter represents the height of the interval (10, 50) of the area bearing ratio curve or Abbott curve. The Sk parameter, known as the core roughness depth, evaluates the height of the core roughness of the Abbott curve presented between the reduced peak height and the reduced valley depth.27,28 The fractal dimension (Sfd) and correction length parameter Str37 are classified as spatial parameters. These parameters characterize the spatial properties of the surface such as the peak density and directionality of the surface texture.27 The Sfd parameter, known as the
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fractal dimension, gives the rate at which height (in topographic images) increases with the scale of observation, and is calculated based on the analysis of the Fourier amplitude spectrum. A large Sfd value (approaching 3) indicates the surface looks similar at different scales. Str, the texture aspect ratio, is a measure of the spatial isotropy or directionality of the surface texture. Str37 is used to measure the isotropic property at the 37% ratio of the autocorrelation function. Its value is between 0 and 1. A surface with a dominant lay has a Str37 value of approximately 0, while a spatially isotropic texture will have a Str37 value of 1 or close. Following the quantitative characterizations, statistical analyses were conducted to evaluate and select effective numerical parameters which could describe distinctive surface features and their evolutions in the OA progress. The analyses were carried out using analysis of variance (ANOVA) to assess the evolution of the cartilage degradation. Before conducting the statistical analyses, the natural log (Ln) was applied to the data to improve the normal distribution of the data and obtain a homogenous distribution of the residual.29 Figure 1(a) and (b) shows the distribution of the data before and after the data transformation, respectively. The transformation of the data using the Ln function
improved considerably its normal distribution and the homogeneity of the residual. The significant difference between OA grades was then evaluated by using oneway ANOVA to determine whether the means of the samples in the three OA grades were statistically different (p value \ 0.05). Only the parameters with a significance level less than 0.05 (confidence level = 95%) were selected as potential significant ones. Post hoc tests (p value \ 0.05) were conducted to determine where the significant differences were, that is, to evaluate the statistical significance between two OA conditions using the selected parameters.
Results The cartilage surface textures of human knee joints affected by OA were studied both qualitatively and quantitatively. Visual inspection of the cartilage images was carried out to examine the surface features in section ‘‘Qualitative characterizations of the cartilage surface morphologies using LSM and AFM techniques,’’ while the numerical and statistical results are presented in section ‘‘Quantitative characterizations of the surface morphologies of human knee cartilages at micro- and nanometer scales.’’
Figure 1. Distribution of the data and residuals of the numerical parameter Sdc10_50: (a) before and (b) after the data transformation using Ln.
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Figure 2. Evolution of the surface morphologies of diseased human knee cartilages in OA grades I, II and III. The images were taken using a laser scanning microscope with a 203 magnification objective lens: (a) OA grade I surface of a 53-year-old female OA patient, (b) OA grade I surface of a 65-year-old male patient, (c) OA grade II surface of a 64-year-old male, (d) OA grade II surface of a 65year-old male patient, (e) OA grade III cartilage surface collected from a 89-year-old male patient, and (f) OA grade III cartilage surface of a 76-year-old female patient.
Qualitative characterizations of the cartilage surface morphologies using LSM and AFM techniques Figure 2 shows LSM images of six cartilages in OA grades I, II and III. Figure 2(a) and (b) displays relatively smooth surfaces classified to be in OA grade I. It can be seen that the surfaces became rougher with the
deterioration of the OA conditions. In particular, the peaks got higher and valleys became deeper with the OA progression (Figure 2(c)–(f)). The above observed changes at the micron scale were also found in the AFM images obtained at the submicron level. Figure 3 shows the AFM images of
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Figure 3. 3D AFM images of cartilages in OA grades (a) I, (b) II and (c) III.
cartilages in OA grades I, II and III. At the nanometer scale, the surfaces became corrugated with deeper features appearing on the surfaces in a medium and advanced OA condition. Quantitative analysis results presented in the next section will be able to provide numerical data on the morphological changes.
Quantitative characterizations of the surface morphologies of human knee cartilages at microand nanometer scales The LSM and AFM images were analyzed separately using the same image analysis package (Scanning
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Table 1. The mean values and SDs of selected numerical parameters used to characterize the AFM and LSM images of diseased cartilage surface. Numerical parameters
OA grades
p value
I mean 6 SD
II mean 6 SD
III mean 6 SD
LSM Sa (nm) Sq (nm) Sdc10_50 (nm) Sk (nm)
3960 6 1267 5317 6 1914 6148 6 2164 11,457 6 3334
13,900 6 5004 18620 6 6908 25,606 6 10,652 38,157 6 13,889
20,123 6 8854 26,977 6 11,249 36,437 6 14,174 52,899 6 23,804
0.000 0.000 0.000 0.000
AFM Sa (nm) Sfd (nm) Str37 (nm)
35 6 11 2.21 6 0.054 0.627 6 0.158
35 6 20 2.26 6 0.052 0.673 6 0.122
41 6 15 2.13 6 0.102 0.779 6 0.118
0.867 0.014 0.163
SD: standard deviations; AFM: atomic force microscopy; LSM: laser scanning microscopy; OA: osteoarthritis.
Probe Image Processor (SPIP), Image Metrology A/S, Denmark) and numerical parameters. The roughness of the samples was quantified using Sa, and the results are presented in Table 1. It can be seen that at the micron scale, the Sa values increased noticeably with the OA grades. These quantitative results match with the visual inspection outcome, that is, the cartilage surface became rougher when the OA grade increased. In comparison, the changes in the surface roughness at the nanometer level were marginal with an overall increasing trend with OA progression. The results of the other key numerical parameters selected in this study are explained in the following two paragraphs. In addition to the Sa results shown in Table 1, at the micrometer scale, another amplitude parameter Sq and two functional parameters Sdc10_50 and Sk were found to be able to describe a significant difference between the cartilage samples in the three OA conditions. Based on the statistical analysis results, Sdc10_50 (10%–50% height intervals of bearing curve) was identified to be the most effective parameter for the OA cartilage characterization, followed by Sq, Sa and Sk. The nano-scaled numerical results revealed that some selected numerical parameters may assist in differentiating the diseased cartilages at that resolution. For example, Sfd, fractal dimension, could be used to discriminate the nano-scaled surface textures of OA grades II and III cartilages as suggested by the ANOVA and a Tukey post hoc test analysis (p value = 0.014). The surface roughness Sa and the texture aspect ratio Str37 both revealed a continuously increasing trend from OA grade I to grade III. The trends of the surface roughness values at the micron and nanometer scale are the same although the values are different due to the difference in the measurement resolutions. However, this study has found that Sa and Str37 were unable to describe a significant difference between all OA grades. Reasons for this outcome will be discussed in section ‘‘Discussion.’’ In addition to the above studies of the nano-surface topographical changes of the human cartilages in the progression of OA, the effective indentation moduli
Figure 4. The changes in the effective indentation moduli of human cartilages in OA grades I, II and III measured at indentation depths of 300 and 1000 nm.
were examined and their alterations are presented in Figure 4. It can be seen that the effective indentation moduli revealed a decreasing trend from OA grades I– III. Post hoc analysis showed that the differences between the effective indentation moduli of the human cartilages in OA grades I and III were statistically significant with a p value less than 0.05, indicating that the effective indentation moduli of human cartilages can be used to discriminate cartilages in early and late OA conditions.
Discussion This study focused on quantitatively studying changes in the micron and submicron morphological features and the nano-scaled effective indentation moduli of the diseased cartilage surfaces in the OA process. The work presented here has demonstrated that observable surface alternations have been found using the improved image acquisition and analysis techniques. A number of improvements were made on the quality and quantity of the samples and the LSM images in this study. In comparison to the LSM images and results presented in our earlier work,19 the micro-scaled numerical results presented in this article were obtained
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Table 2. Selected numerical parameters to characterize the distinctive surface features of diseased cartilages at the microand nanometer scales. Scale
Numerical parameters to differentiate OA grades
Micrometer
Amplitude (height) features including Sa and Sq and functional parameters Sdc10_50 and Sk Spatial parameter Sfd
Nanometer OA: osteoarthritis.
by analyzing nearly 150 images of cartilage samples. Furthermore, the image acquisition and processing procedures were improved by using 203 magnification with a step size of 0.05 mm on larger cartilage samples (15 mm 3 15 mm). Having large and hydrated samples with the bone attached ensured a more stable cartilage surface, so better quality images were obtained. Another improvement was that a large area of each sample was analyzed by acquiring 12 images from each sample and stitching them together. The above results and following discussions are based on these improvements made for more representative and reliable results than those presented previously.
Scale-related surface characteristics This study has confirmed that different surface features were captured and revealed at two resolutions.17 As shown in Table 2, at the micron scale, the functional and amplitude parameters were identified to be the most suitable to describe the evolutions of the surfaces from OA grades I–III. The results of the amplitude parameters, in particular Sa and Sq, were consistent with the existing OA grading criteria based on visual inspections.21 For example, Sa showed a clear increasing trend with OA progress. Furthermore, this study has also found that the functional parameter Sdc10_50 could be used to differentiate the three OA grades. Sdc10_50 reveals the height differences of the curve between the 10% and 50% of the bearing area curve.26 Figure 2 shows that there was an increase in both peaks and pits on the surfaces with an increase in the severity of the OA grades. It is believed that the above surface roughness and bearing curve changes are related to the structure alternations of the cartilage, in particular, the collagen structures at different depths. How this surface morphological and structural changes influence the OA process will be discussed in section ‘‘Further understanding of surface changes in the OA process.’’ It is worth noting that, at the micron scale, parameters in hybrid and spatial groups were found to be insignificant for characterizing distinctive changes in the cartilage surfaces. It is believed that the micro-scaled changes in the surface textures initiate at the molecular scale and
spread to the higher levels of the architecture of articular cartilage causing damages to the structure and function.30 At the submicron level, changes in the surface features were revealed in Figure 3. The fine surface asperities of the OA grade I cartilage in Figure 3(a) became larger in both the X-Y (plane) and Z (height) directions with the OA progress. Interestingly, the changes in Sa between the OA grades were marginal (Table 1), indicating that this commonly used surface roughness parameter was unable to differentiate the OA grades at this resolution. Furthermore, none of the amplitude parameters were identified to be significant enough for differentiating all OA conditions. Some of them could be used to reveal distinctive changes between a healthy cartilage surface and a diseased one.20 Based on the previous work and additional data collected and analyzed in this work, it has been found that fractal dimension (Sfd) was able to differentiate OA grade III cartilages from OA grade II. Sfd is widely used to characterize an infinite self-similarity and the complexity of the surface texture.31 Our results showed that the values of Sfd increased from an early OA condition (grade I) to a moderate OA and then decreased when OA reached a late stage (OA grade III), revealing that the complexity of the surface varied with changes in the OA conditions. Str37 describes the anisotropic property of the surface texture. Overall, the Str37 values had an increasing trend from OA grade I to III, indicating that the surface texture became more directional (i.e. anisotropy) as OA conditions progressed. Although both the imaging techniques are able to capture the changes in the surface morphologies in the OA process, this study found that LSM has advantages in acquiring more surface data over a short period of time in comparison to AFM. Figure 5 illustrates the appearance of a diseased cartilage at two different scales, allowing us to compare the surface information extracted at both submicron and micron scales (Figure 5(a) and (b), respectively). The large micron-image in Figure 5(c) shows the heterogeneity of the surface composed of ridges and pits observed at a microscopic level. In comparison, limited surface information was captured by the AFM at the submicron scale. Having said that, the AFM images can reveal the nano-scaled changes in the surfaces in the initial degradation process which is critical for understanding root causes of OA.
Further understanding of surface changes in the OA process The numerical analysis results obtained in this study have revealed that, once OA initiates at the submicron level, detectable changes in the surface morphologies of the diseased cartilages are spatially related. More specifically, the nano-scaled feature that could be used to distinct the difference in OA conditions are the complexity of the surface texture. It is believed that these
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Figure 5. Images of a human knee cartilage surface with OA grade III: (a) AFM image in an area of 5 mm 3 5 mm, (b) LSM image acquired using a 203 magnification objective lens and of a size of 680 mm 3 500 mm, and (c) a large cartilage surface (approximately 2000 mm 3 1900 mm) obtained by stitching 12 LSM images together (i.e. 3 in the X-direction and 4 in the Y-direction).
alterations are related to changes in the materials properties, the collagen structures and their orientations in the wear process. As OA progresses, micro-scaled changes in the surface become distinctive and quantitatively measurable. This is one of very few studies on the numerical characterizations of the surface morphological evolutions at the nanometer scale. The identified numerical parameters were able to differentiate advanced OA grades from the early ones. However, no numerical parameters were found to be effective in revealing the surface evolutions between all three different grades at the nanoscale. Based on the results, it is believed that it is not
possible or very difficult to use the commonly used amplitude parameters (Sa and Sq) to fully describe the nano-scaled surface changes due to two possible reasons. They are (a) that the small sampling area of this imaging technique makes it difficult and time consuming to obtain representative data over a required sampling area and (b) that the morphological changes at the nano-scale are probably not significant enough to allow reliable and accurate OA assessment between the three OA grades. In contrast, this work has found that distinct morphological changes in the surface were captured at the micrometer scale. From the LSM images of the
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diseased cartilages, there was no clear evidence of surface cracking on later-stage OA cartilages. However, an increase in both ridges and pits was observed when the condition of the cartilage worsened. The change in the surface roughness has been widely reported and confirmed by the quantitative results in this study. The observed evolution of the functional properties of the surface is reported for the first time. A continuous increase in the bearing area ratio (Sdc10_50) may indicate a deterioration of the anti-wear property of the surface, causing a high wear rate of the cartilage, a rougher surface and more cartilage loss.
Change in the mechanical property and in relation to the surface features In comparison to the surface morphological features, it appeared that the changes in the mechanical property gave a better indication of the cartilage degradation at the submicron level. The effective indentation moduli showed a decreasing trend, indicating that the nanostiffness of the cartilages deteriorated. The statistical result presented in section ‘‘Results’’ confirmed that the reduction in the effective indentation moduli was significant enough to reveal the difference between the samples in OA grades I, II and III. As reported in Buckwalter’s work,32 the change in the stiffness of the cartilage is probably due to the disruption of the balance in the collagen–proteoglycan solid matrix, that is, the decrease of matrix synthesis, resulting in a softening effect of the tissue.
Limitations and future work There are certain limitations in this study. Healthy cartilages were not included due to difficulties in obtaining healthy samples, and thus, the understanding of the entire degradation process could not be studied. The nano-scaled surface analysis and nano-mechanical results had a large deviation. It is believed that this deviation was due to the examination of locations on various components of the cartilage.33 More testing needs to be carried out to understand how the properties will be affected by the location and cartilage components. Furthermore, it is known that the mechanical properties and surface morphologies of cartilages are affected by a number of factors, including the condition, sample location, age and gender.34–36 Although other factors may also play a role in cartilage degradation, their influence can be quite complex and were not investigated in this study. Also, the image acquisition and analysis techniques presented here are for investigating the structural changes in cartilage surfaces. They are not ready for clinical applications. Once the cartilage degradation process has been well understood and the technique has been further developed, it may have potential to be applied clinically.
Conclusion With the support of advanced imaging facilities in 3D and quantitative analysis techniques, numerical characterizations were conducted on the surfaces of diseased human cartilage samples at both the micro- and nanometer scales. The results reported in this article have revealed the evolution of the surface morphologies of the cartilages in three OA grades. This study has found that at the submicron level, spatial changes occurred and could be used to differentiate early and advanced OA grades. The effective indentation moduli of human cartilages decreased continuously, giving a good indication of the degradation process from OA grades I to III. At the micrometer scale, changes in the surface roughness and functional properties of the surface were found to be significant enough for OA condition assessment. Through the nano- to micrometer surface characterizations together with the nano-mechanical property investigations, insights into the surface changes have been achieved. Further investigations of relationships of the changes in the nano-scaled surface textures and nano-mechanical properties and age and/or gender effects are needed to assist in understanding the OA process, its fundamental causes and distinctive characteristics for future developments of effective and objective diagnostic techniques. Acknowledgements The authors would like to thank Dr James Price for collecting the clinical samples and providing associated data for the project, Ms Lynn Ferris at UNSW Australia and Professor N. Ketheesan from James Cook University, Australia, for allowing the use of their PC2 labs for the sample preparations and the Mark Wainwright Analytical Centre (Biomedical Imaging Facility) at UNSW Australia for providing the AFM facility for the study. This research was funded by Australia Research Council (ARC) under the discovery funding scheme. Declaration of conflicting interests The authors declare that there is no conflict of interest. Funding This research was financially supported by Australian Research Council (ARC) through the project (DP1093975). References 1. Grushko G, Schneiderman R and Maroudas A. Some biochemical and biophysical parameters for the study of the pathogenesis of osteoarthritis: a comparison between the processes of ageing and degeneration in human hip cartilage. Connect Tissue Res 1989; 19: 149–176. 2. Hudelmaier M, Glaser C, Hohe J, et al. Age-related changes in the morphology and deformational behavior of knee joint cartilage. Arthritis Rheum 2001; 44(11): 2556–2561.
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3. Altman RD. Overview of osteoarthritis. Am J Med 1987; 83(4B): 65–69. 4. Uhl M, Ihling C, Allmann KH, et al. Human articular cartilage: in vitro correlation of MRI and histologic findings. Eur Radiol 1998; 8: 1123–1129. 5. Faber S, Eckstein F, Lukasz S, et al. Gender differences in knee joint cartilage thickness, volume and articular surface areas: assessment with quantitative threedimensional MR imaging. Skeletal Radiol 2001; 30: 144– 150. 6. Buck RJ, Wyman BT, Le Graverand MP, et al. An efficient subset of morphological measures for articular cartilage in the healthy and diseased human knee. Magn Reson Med 2010; 63: 680–690. 7. Johansson A, Kuiper JH, Sundqvist T, et al. Spectroscopic measurement of cartilage thickness in arthroscopy: ex vivo validation in human knee condyles. Arthroscopy 2012; 28: 1513–1523. 8. Alhadlaq H, Xia Y, Moody J, et al. Detecting structural changes in early experimental osteoarthritis of tibial cartilage by microscopic magnetic resonance imaging and polarised light microscopy. Ann Rheum Dis 2004; 63: 709–717. 9. Quinn TM, Hunziker EB and Ha¨uselmann HJ. Variation of cell and matrix morphologies in articular cartilage among locations in the adult human knee. Osteoarthritis Cartilage 2005; 13: 672–678. 10. Cohen ZA, McCarthy DM, Kwak SD, et al. Knee cartilage topography, thickness and contact areas from MRI: in-vitro calibration and in-vivo measurements. Osteoarthritis Cartilage 1999; 7: 95–109. 11. Tummala S, Bay-Jensen AC, Karsdal MA, et al. Diagnosis of osteoarthritis by cartilage surface smoothness quantified automatically from knee MRI. Cartilage 2011; 2(1): 50–59. 12. Accardi MA, Dini D and Cann PM. Experimental and numerical investigation of the behaviour of articular cartilage under shear loading—interstitial fluid pressurisation and lubrication mechanisms. Tribol Int 2011; 44: 565–578. 13. Verberne G, Merkher Y, Halperin G, et al. Techniques for assessment of wear between human cartilage surfaces. Wear 2009; 266: 1216–1223. 14. Chan SMT, Neu CP, DuRaine G, et al. Atomic force microscope investigation of the boundary-lubricant layer in articular cartilage. Osteoarthritis Cartilage 2010; 18: 956–963. 15. Coles JM, Blum JJ, Jay GD, et al. In situ friction measurement on murine cartilage by atomic force microscopy. J Biomech 2008; 41(3): 541–548. 16. Imer R, Akiyama T, Stolz M, et al. The measurement of biomechanical properties of porcine articular cartilage using AFM. Arch Histol Cytol 2009; 72(5): 251–259. 17. Ghosh S, Bowen J, Jiang K, et al. Investigation of techniques for the measurement of articular cartilage surface roughness. Micron 2013; 44: 179–184. 18. Tian Y, Wang J, Peng Z, et al. A new approach to numerical characterisation of wear particle surfaces in threedimensions for wear study. Wear 2012; 282–283: 59–68.
19. Peng Z and Wang M. Three dimensional surface characterization of human cartilages at a micron and nanometre scale. Wear 2013; 301: 210–217. 20. Wang M, Wang J, Peng Z, et al. Wear characterisation of articular cartilage surfaces at a nano-scale using atomic force microscopy. Tribol Int 2013; 63: 235–242. 21. Outerbridge R and Dunlop J. The problem of chondromalacia patellae. Clin Orthop Relat Res 1975; 110: 177– 196. 22. Shepherd DET and Seedhom BB. Thickness of human articular cartilage in joints of the lower limb. Ann Rheum Dis 1999; 58: 27–34. 23. Bilodeau G. Regular pyramid punch problem. J Appl Mech 1992; 59: 519–523. 24. Hori R and Mockros L. Indentation tests of human articular cartilage. J Biomech 1976; 9: 259–268. 25. Kempson GE, Freeman MAR and Swanson SAV. The determination of a creep modulus for articular cartilage from indentation tests on the human femoral head. J Biomech 1971; 4: 239–250. 26. ISO 25178-2:2006. Geometrical product specifications (GPS)-surface texture: areal—part 2: terms, definitions and surface texture parameters. 27. Blunt L and Jiang X. Advanced techniques for assessment surface topography, development of a basis for 3D surface texture standards ‘‘SURFSTAND’’. London: Kogan Page Science, 2003. 28. Leach R. Chapter 8 - Surface topography characterisation. In: Leach R (ed.) Fundamental principles of engineering nanometrology. 2nd ed. Oxford: William Andrew, 2014, pp.241–294. 29. Montgomery DC, Runger GC and Hubele NF. Engineering statistics. 5th ed. Hoboken, NJ: John Wiley & Sons, 2011. 30. Stolz M, Gottardi R, Raiteri R, et al. Early detection of aging cartilage and osteoarthritis in mice and patient samples using atomic force microscopy. Nat Nanotechnol 2009; 4: 1862192. 31. Florindo JB and Bruno OM. Fractal descriptors based on Fourier spectrum applied to texture analysis. Physica A 2012; 391: 4909–4922. 32. Buckwalter JA, Mankin HJ and Grodzinsky AJ. Articular cartilage and osteoarthritis. Instr Course Lect 2005; 54: 465–480. 33. Wen CY, Wu CB, Tang B, et al. Collagen fibril stiffening in osteoarthritic cartilage of human beings revealed by atomic force microscopy. Osteoarthritis Cartilage 2012; 20(8): 916–922. 34. Karvonen RL, Negendank WG, Teitge RA, et al. Factors affecting articular cartilage thickness in osteoarthritis and aging. J Rheumatol 1994; 21(7): 1310–1318. 35. Jiang Y, Mishima H, Sakai S, et al. Gene expression analysis of major lineage-defining factors in human bone marrow cells: effect of aging, gender, and age-related disorders. J Orthop Res 2008; 26(7): 910–917. 36. Julkunen P, Kiviranta P, Wilson W, et al. Characterization of articular cartilage by combining microscopic analysis with a fibril-reinforced finite-element model. J Biomech 2007; 40: 1862–1870.
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Investigations of micron and submicron wear features of diseased human cartilage surfaces
Proc IMechE Part H: J Engineering in Medicine 2015, Vol. 229(2) 164–174 Ó IMechE 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0954411915572496 pih.sagepub.com
Zhongxiao Peng1, Juan C Baena1 and Meiling Wang1,2
Abstract Osteoarthritis is a common disease. However, its causes and morphological features of diseased cartilage surfaces are not well understood. The purposes of this research were (a) to develop quantitative surface characterization techniques to study human cartilages at a micron and submicron scale and (b) to investigate distinctive changes in the surface morphologies and biomechanical properties of the cartilages in different osteoarthritis grades. Diseased cartilage samples collected from osteoarthritis patients were prepared for image acquisition using two different techniques, that is, laser scanning microscopy at a micrometer scale and atomic force microscopy at a nanometer scale. Three-dimensional, digital images of human cartilages were processed and analyzed quantitatively. This study has demonstrated that high-quality three-dimensional images of human cartilage surfaces could be obtained in a hydrated condition using laser scanning microscopy and atomic force microscopy. Based on the numerical data extracted from improved image quality and quantity, it has been found that osteoarthritis evolution can be identified by specific surface features at the micrometer scale, and these features are amplitude and functional property related. At the submicron level, the spatial features of the surfaces were revealed to differ between early and advanced osteoarthritis grades. The effective indentation moduli of human cartilages effectively revealed the cartilage deterioration. The imaging acquisition and numerical analysis methods established allow quantitative studies of distinctive changes in cartilage surface characteristics and better understanding of the cartilage degradation process.
Keywords Wear features, image acquisition in three dimensions, numerical characterizations, effective indentation modulus, surface morphology, osteoarthritis
Date received: 15 September 2014; accepted: 12 January 2015
Introduction Osteoarthritis (OA) is a common disease caused by cartilage degradation and/or damage. Fundamental causes of OA are not fully understood, and objective and quantitative OA marker(s) are yet to be developed. It is recognized that the surface morphologies and biomechanical properties of articular cartilage change with the pathologic processes.1,2 Thus, studying the surface morphologic and biomechanical features can reveal cartilage defects for OA assessment and understanding.3 Due to its critical role as the bearing surface of synovial joints, the surface topographies of human knee cartilage were studied using magnetic resonance imaging (MRI),4–6 arthroscopy7 and polarized light microscopy8,9 at a millimeter scale. Cohen et al.10 used MRI to measure the thickness, contact areas, and curvature characteristics of knee cartilage. Surface smoothness
was assessed to be a potential OA marker.11 This is because human articular cartilage is anisotropic, that is, its structural distribution and mechanical and physical properties change along the direction perpendicular to the cartilage top surface. The surface morphology of the articular cartilage is directly related to the mechanical response of the surface under various loading conditions. Therefore, it is important to identify the main
1
School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW, Australia 2 College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China Corresponding author: Juan C Baena, School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW 2052, Australia. Email: [email protected]
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features of the cartilage surface that reveal the cartilage degradation progress. Furthermore, the quantitative characterization of structural changes in the cartilage surface is useful for understanding the process and for developing objective OA assessment criteria. The majority of the existing studies on the surface examinations of articular cartilages were either qualitative or based on conventional surface roughness descriptor (Ra or Sa). Ra and Sa are two-dimensional (2D) and three-dimensional (3D) amplitude parameters which calculate a roughness value on a profile (line) and on a surface (area), respectively. They have limitations in describing surface characteristics such as the predominance of ridges or scratches. Existing studies have reported that some 2D numerical parameters including Ra fail to describe significant changes in the surface morphology.12–15 Also, most of previous studies were carried out at a millimeter scale instead of micron or submicron level where an OA process initiates and accelerates to a large scale.16 More recently, scanning electron microscopy (SEM), atomic force microscopy (AFM) and laser scanning confocal microscopy (LSCM) are used to examine cartilage surfaces in three dimensions and in micron or submicron resolutions. Ghosh et al.17 used both SEM and AFM to study bovine cartilage surfaces and made comparisons of these two techniques for surface roughness measurements. Two commonly used parameters, Ra and Sa, were used in their study. Similarly, numerical studies were carried out to investigate the surface morphologies and biomechanical properties of sheep articular cartilages in various OA conditions using AFM.18 Six numerical parameters, including two field and four feature parameters, were found to be effective in describing changes in the surface topographies of worn cartilages. Later, the established numerical characterization techniques were applied to investigate distinctive morphologic features of human cartilage surfaces.19 In addition to SEM and AFM, LSCM was utilized to acquire 3D surface morphological data of sheep knee joints for developing suitable imaging and image analysis techniques.19 The surface features of human samples were then imaged using the established techniques,20 characterized using numerical parameters and compared to the features analyzed at the nanometer scale. However, the image quality and quantity of the laser images reported in that published work19 need to be improved. The image quality and raw surface data that the image contains are essential for accurate surface characterizations. For this reason, this work was carried out to improve the quality of the laser images by selecting suitable settings of the imaging facility (laser scanning microscopy (LSM)), so that noise in the image could be further minimized and reliable cartilage surface data could be acquired. In addition to good quality images, a sufficient quantity of surface data is required to ensure the accuracy and reliability of the surface morphological measurements. To generate reliable results,
an improved imaging and image processing technique was established, so high-quality images over large surface areas were captured and studied to obtain representative surface information. In parallel, AFM was used to acquire 3D images of hydrated cartilage sample at a nanometer scale. A total of 35 numerical parameters were applied to analyze the surface morphologies of the LSM and AFM images, and distinctive surface features in both micron and submicron resolutions were extracted to gain better understanding of structural changes in the surfaces.
Methodology Sample collection and preparation Human cartilage samples were collected during knee replacement operations through an orthopedic surgeon in Queensland, Australia. Prior to sample collections, OA patients were consented under the ethical approval for this study (MHSNQ Reference No.: MHS20100401-01). Over a 2-year period, a total of 28 cartilage samples in OA grades I, II and III were collected from patients aged 53–89 years. Together with the cartilage samples, their X-Ray, MRI and details which were relevant to their OA diseases were gathered. In addition, a camera was often used in the arthroscopy surgery to capture the images of the cartilage surfaces. Once fresh cartilages were obtained from hospitals, two sets of samples were prepared: one set for image acquisition using LSM and the other for nano-scale study using AFM. The cartilage samples were extracted from the femoral condyle section of the human knee joints. The selection of the location where the samples were extracted was defined after identifying the OA condition of the cartilage, which was graded by an orthopedic surgeon according to the Outerbridge grading systems.21 All sample extraction was conducted in a PC2 laboratory. Key steps on how to extract and prepare these samples are presented below. Sample preparation procedures were different for LSM and AFM examinations. For LSM imaging, samples of 15 mm 3 15 mm in size were extracted by using an electrical oscillating saw. A total of 12 samples with 4 samples in each OA grade were prepared. These samples were individually stored in petri dishes and immersed in phosphate buffered saline (PBS) solution with a pH value of 7.4. They were then stored at 220 °C. To prepare samples for AFM imaging, small cartilage samples were extracted using a biopsy punch with an internal diameter of approximately 4 mm. The thickness of the extracted samples was within a range of 2– 4 mm. According to literature,22 the thickness of healthy cartilage is in a range of 1.5 to about 3 mm, while the thickness of a diseased cartilage can range from close to a full thickness for an early OA cartilage to nearly 0 mm for a severe OA one. The thickness of the extracted samples was in a range of 2–4 mm to ensure that the top surface of the cartilage was
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supported and hydrated by the material underneath, so surface deformation would not occur during the image acquisition process. The cartilages were kept hydrated with drops of PBS during the extracting process. The samples were glued to sterile petri dishes, which were then filled with sterile PBS and protease inhibitor solution, and stored at 220 °C. The stored cartilage specimens were thawed at 4 °C for 12 h before AFM measurements were conducted.
Image acquisition and nano-indentation 3D images of the prepared cartilage samples were acquired using LSM and AFM separately. The key hardware settings and conditions are briefly summarized as follows. A 3D laser scanning microscope, manufactured by KEYENCE (model: VK-X210), was used to capture 3D surface information of the specimens in a noncontact mode and at a micrometer scale. The system uses a violet laser of a wavelength of 408 nm to acquire digital images. Prior to the imaging, a sample was thawed for 24 h from 220 °C to 4 °C. The thawed sample was located in a sample holder designed specifically to maintain the sample hydrated during the image acquisition process. In the imaging process, the laser scanning facility, set in a reflected mode, scanned the surface in the X/Y plane to acquire the spatial surface data. By moving the stage vertically controlled using a step motor with a step size in a range of 10 nm to 1 mm, a set of 2D images containing valley and peak features was acquired. The height information of the surface was then obtained by re-constructing a series of 2D images into a 3D image. The 3D image was used for the surface characterizations. On each cartilage sample, 12 LSM images, in a size of 0.68 mm 3 0.5 mm of each image, were obtained using a 203 objective lens and a step size of 0.05 mm. These images were stitched together to obtain a large surface area of approximately 2 3 2 mm2 for visual inspection and numerical analysis. In total, 144 images were taken from 12 human samples in OA grades I, II and III. In parallel, AFM images were acquired on the diseased samples in OA grades I–III. The imaging process was carried out in a Peak Force Quantitative Nanomechanical Mapping (QNM) and fluid mode. Silicon nitride probes DNP-10 with a nominal tip radius of 20 nm and a nominal spring constant of 0.35 N/m were used to image the cartilage surfaces. During the imaging process, forces in a range of 0.3– 16.5 nN were applied to the surfaces, resulting in a constant deformation of 150 nm on the surfaces. The scanning resolution was 256 3 256 pixels, and the scanning area was 5 3 5 mm2. Three to four locations were imaged on each cartilage sample. Along with the surface characterizations at the nano-scale, AFM nano-indentation was performed on a TopoMetrix (now Veeco Instruments, Plainview, NY, USA) Explorer TMX-2000 scanning probe microscope (SPM). The purpose of the nano-indentation
tests was to investigate which features, surface or nanomechanical properties could be used as an effective indicator for OA assessment. The deflection (nA)–displacement (nm) data were obtained using V-shaped Si3N4 pyramidal tips (the nominal spring constant k = 0.56 N m21; the nominal tip radius of curvature Rtip = 40 nm and the half angle a = 22.5°). The maximum loading force was ;80 nN, resulting in a maximum indentation depth of approximately 1800 nm. A total of 12 cartilage samples were indented. On each cartilage sample, 2–4 locations were randomly indented. In total, 10 repeats were made at each location. The effective indentation modulus was calculated using the pyramidal model23 shown in equation (1) F(d) =
1:4906E tan (a) 2 d 2(1 y 2 )
ð1Þ
where a is the half angle of the pyramidal tip, d is the indentation depth of the tip into a cartilage sample, E is the effective indentation modulus of a cartilage sample, F is the indentation force and n is Poisson’s ratio of human knee cartilage. Poisson’s ratio of human knee cartilage was taken as 0.5.24,25
Image processing and analyses Before performing quantitative image analyses, the LSM and AFM images were plane-corrected to remove image bow and scan distortions. Noise was also filtered out. The images were then analyzed to study the cartilage surface topographical characteristics at both micro- and nanometer scales. Numerical parameters used in this study are from four surface parameter sets, namely, amplitude, hybrid, functional and spatial descriptors.26 A total of 35 surface parameters were used in this study to characterize the surface morphologies of the human knee cartilages. The definitions or meanings of important parameters are briefly presented below. Sa and Sq are the amplitude parameters representing the arithmetic average and the standard deviation of the height distribution, respectively. The amplitude parameters evaluate specific roughness properties such as statistical average, extreme properties and shape of the height distribution histogram.27Sk and Sdc10_50 are the functional parameters that describe the bearing characteristics and fluid retention properties of the surface. The Sdc10_50 parameter represents the height of the interval (10, 50) of the area bearing ratio curve or Abbott curve. The Sk parameter, known as the core roughness depth, evaluates the height of the core roughness of the Abbott curve presented between the reduced peak height and the reduced valley depth.27,28 The fractal dimension (Sfd) and correction length parameter Str37 are classified as spatial parameters. These parameters characterize the spatial properties of the surface such as the peak density and directionality of the surface texture.27 The Sfd parameter, known as the
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fractal dimension, gives the rate at which height (in topographic images) increases with the scale of observation, and is calculated based on the analysis of the Fourier amplitude spectrum. A large Sfd value (approaching 3) indicates the surface looks similar at different scales. Str, the texture aspect ratio, is a measure of the spatial isotropy or directionality of the surface texture. Str37 is used to measure the isotropic property at the 37% ratio of the autocorrelation function. Its value is between 0 and 1. A surface with a dominant lay has a Str37 value of approximately 0, while a spatially isotropic texture will have a Str37 value of 1 or close. Following the quantitative characterizations, statistical analyses were conducted to evaluate and select effective numerical parameters which could describe distinctive surface features and their evolutions in the OA progress. The analyses were carried out using analysis of variance (ANOVA) to assess the evolution of the cartilage degradation. Before conducting the statistical analyses, the natural log (Ln) was applied to the data to improve the normal distribution of the data and obtain a homogenous distribution of the residual.29 Figure 1(a) and (b) shows the distribution of the data before and after the data transformation, respectively. The transformation of the data using the Ln function
improved considerably its normal distribution and the homogeneity of the residual. The significant difference between OA grades was then evaluated by using oneway ANOVA to determine whether the means of the samples in the three OA grades were statistically different (p value \ 0.05). Only the parameters with a significance level less than 0.05 (confidence level = 95%) were selected as potential significant ones. Post hoc tests (p value \ 0.05) were conducted to determine where the significant differences were, that is, to evaluate the statistical significance between two OA conditions using the selected parameters.
Results The cartilage surface textures of human knee joints affected by OA were studied both qualitatively and quantitatively. Visual inspection of the cartilage images was carried out to examine the surface features in section ‘‘Qualitative characterizations of the cartilage surface morphologies using LSM and AFM techniques,’’ while the numerical and statistical results are presented in section ‘‘Quantitative characterizations of the surface morphologies of human knee cartilages at micro- and nanometer scales.’’
Figure 1. Distribution of the data and residuals of the numerical parameter Sdc10_50: (a) before and (b) after the data transformation using Ln.
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Figure 2. Evolution of the surface morphologies of diseased human knee cartilages in OA grades I, II and III. The images were taken using a laser scanning microscope with a 203 magnification objective lens: (a) OA grade I surface of a 53-year-old female OA patient, (b) OA grade I surface of a 65-year-old male patient, (c) OA grade II surface of a 64-year-old male, (d) OA grade II surface of a 65year-old male patient, (e) OA grade III cartilage surface collected from a 89-year-old male patient, and (f) OA grade III cartilage surface of a 76-year-old female patient.
Qualitative characterizations of the cartilage surface morphologies using LSM and AFM techniques Figure 2 shows LSM images of six cartilages in OA grades I, II and III. Figure 2(a) and (b) displays relatively smooth surfaces classified to be in OA grade I. It can be seen that the surfaces became rougher with the
deterioration of the OA conditions. In particular, the peaks got higher and valleys became deeper with the OA progression (Figure 2(c)–(f)). The above observed changes at the micron scale were also found in the AFM images obtained at the submicron level. Figure 3 shows the AFM images of
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Figure 3. 3D AFM images of cartilages in OA grades (a) I, (b) II and (c) III.
cartilages in OA grades I, II and III. At the nanometer scale, the surfaces became corrugated with deeper features appearing on the surfaces in a medium and advanced OA condition. Quantitative analysis results presented in the next section will be able to provide numerical data on the morphological changes.
Quantitative characterizations of the surface morphologies of human knee cartilages at microand nanometer scales The LSM and AFM images were analyzed separately using the same image analysis package (Scanning
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Table 1. The mean values and SDs of selected numerical parameters used to characterize the AFM and LSM images of diseased cartilage surface. Numerical parameters
OA grades
p value
I mean 6 SD
II mean 6 SD
III mean 6 SD
LSM Sa (nm) Sq (nm) Sdc10_50 (nm) Sk (nm)
3960 6 1267 5317 6 1914 6148 6 2164 11,457 6 3334
13,900 6 5004 18620 6 6908 25,606 6 10,652 38,157 6 13,889
20,123 6 8854 26,977 6 11,249 36,437 6 14,174 52,899 6 23,804
0.000 0.000 0.000 0.000
AFM Sa (nm) Sfd (nm) Str37 (nm)
35 6 11 2.21 6 0.054 0.627 6 0.158
35 6 20 2.26 6 0.052 0.673 6 0.122
41 6 15 2.13 6 0.102 0.779 6 0.118
0.867 0.014 0.163
SD: standard deviations; AFM: atomic force microscopy; LSM: laser scanning microscopy; OA: osteoarthritis.
Probe Image Processor (SPIP), Image Metrology A/S, Denmark) and numerical parameters. The roughness of the samples was quantified using Sa, and the results are presented in Table 1. It can be seen that at the micron scale, the Sa values increased noticeably with the OA grades. These quantitative results match with the visual inspection outcome, that is, the cartilage surface became rougher when the OA grade increased. In comparison, the changes in the surface roughness at the nanometer level were marginal with an overall increasing trend with OA progression. The results of the other key numerical parameters selected in this study are explained in the following two paragraphs. In addition to the Sa results shown in Table 1, at the micrometer scale, another amplitude parameter Sq and two functional parameters Sdc10_50 and Sk were found to be able to describe a significant difference between the cartilage samples in the three OA conditions. Based on the statistical analysis results, Sdc10_50 (10%–50% height intervals of bearing curve) was identified to be the most effective parameter for the OA cartilage characterization, followed by Sq, Sa and Sk. The nano-scaled numerical results revealed that some selected numerical parameters may assist in differentiating the diseased cartilages at that resolution. For example, Sfd, fractal dimension, could be used to discriminate the nano-scaled surface textures of OA grades II and III cartilages as suggested by the ANOVA and a Tukey post hoc test analysis (p value = 0.014). The surface roughness Sa and the texture aspect ratio Str37 both revealed a continuously increasing trend from OA grade I to grade III. The trends of the surface roughness values at the micron and nanometer scale are the same although the values are different due to the difference in the measurement resolutions. However, this study has found that Sa and Str37 were unable to describe a significant difference between all OA grades. Reasons for this outcome will be discussed in section ‘‘Discussion.’’ In addition to the above studies of the nano-surface topographical changes of the human cartilages in the progression of OA, the effective indentation moduli
Figure 4. The changes in the effective indentation moduli of human cartilages in OA grades I, II and III measured at indentation depths of 300 and 1000 nm.
were examined and their alterations are presented in Figure 4. It can be seen that the effective indentation moduli revealed a decreasing trend from OA grades I– III. Post hoc analysis showed that the differences between the effective indentation moduli of the human cartilages in OA grades I and III were statistically significant with a p value less than 0.05, indicating that the effective indentation moduli of human cartilages can be used to discriminate cartilages in early and late OA conditions.
Discussion This study focused on quantitatively studying changes in the micron and submicron morphological features and the nano-scaled effective indentation moduli of the diseased cartilage surfaces in the OA process. The work presented here has demonstrated that observable surface alternations have been found using the improved image acquisition and analysis techniques. A number of improvements were made on the quality and quantity of the samples and the LSM images in this study. In comparison to the LSM images and results presented in our earlier work,19 the micro-scaled numerical results presented in this article were obtained
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Table 2. Selected numerical parameters to characterize the distinctive surface features of diseased cartilages at the microand nanometer scales. Scale
Numerical parameters to differentiate OA grades
Micrometer
Amplitude (height) features including Sa and Sq and functional parameters Sdc10_50 and Sk Spatial parameter Sfd
Nanometer OA: osteoarthritis.
by analyzing nearly 150 images of cartilage samples. Furthermore, the image acquisition and processing procedures were improved by using 203 magnification with a step size of 0.05 mm on larger cartilage samples (15 mm 3 15 mm). Having large and hydrated samples with the bone attached ensured a more stable cartilage surface, so better quality images were obtained. Another improvement was that a large area of each sample was analyzed by acquiring 12 images from each sample and stitching them together. The above results and following discussions are based on these improvements made for more representative and reliable results than those presented previously.
Scale-related surface characteristics This study has confirmed that different surface features were captured and revealed at two resolutions.17 As shown in Table 2, at the micron scale, the functional and amplitude parameters were identified to be the most suitable to describe the evolutions of the surfaces from OA grades I–III. The results of the amplitude parameters, in particular Sa and Sq, were consistent with the existing OA grading criteria based on visual inspections.21 For example, Sa showed a clear increasing trend with OA progress. Furthermore, this study has also found that the functional parameter Sdc10_50 could be used to differentiate the three OA grades. Sdc10_50 reveals the height differences of the curve between the 10% and 50% of the bearing area curve.26 Figure 2 shows that there was an increase in both peaks and pits on the surfaces with an increase in the severity of the OA grades. It is believed that the above surface roughness and bearing curve changes are related to the structure alternations of the cartilage, in particular, the collagen structures at different depths. How this surface morphological and structural changes influence the OA process will be discussed in section ‘‘Further understanding of surface changes in the OA process.’’ It is worth noting that, at the micron scale, parameters in hybrid and spatial groups were found to be insignificant for characterizing distinctive changes in the cartilage surfaces. It is believed that the micro-scaled changes in the surface textures initiate at the molecular scale and
spread to the higher levels of the architecture of articular cartilage causing damages to the structure and function.30 At the submicron level, changes in the surface features were revealed in Figure 3. The fine surface asperities of the OA grade I cartilage in Figure 3(a) became larger in both the X-Y (plane) and Z (height) directions with the OA progress. Interestingly, the changes in Sa between the OA grades were marginal (Table 1), indicating that this commonly used surface roughness parameter was unable to differentiate the OA grades at this resolution. Furthermore, none of the amplitude parameters were identified to be significant enough for differentiating all OA conditions. Some of them could be used to reveal distinctive changes between a healthy cartilage surface and a diseased one.20 Based on the previous work and additional data collected and analyzed in this work, it has been found that fractal dimension (Sfd) was able to differentiate OA grade III cartilages from OA grade II. Sfd is widely used to characterize an infinite self-similarity and the complexity of the surface texture.31 Our results showed that the values of Sfd increased from an early OA condition (grade I) to a moderate OA and then decreased when OA reached a late stage (OA grade III), revealing that the complexity of the surface varied with changes in the OA conditions. Str37 describes the anisotropic property of the surface texture. Overall, the Str37 values had an increasing trend from OA grade I to III, indicating that the surface texture became more directional (i.e. anisotropy) as OA conditions progressed. Although both the imaging techniques are able to capture the changes in the surface morphologies in the OA process, this study found that LSM has advantages in acquiring more surface data over a short period of time in comparison to AFM. Figure 5 illustrates the appearance of a diseased cartilage at two different scales, allowing us to compare the surface information extracted at both submicron and micron scales (Figure 5(a) and (b), respectively). The large micron-image in Figure 5(c) shows the heterogeneity of the surface composed of ridges and pits observed at a microscopic level. In comparison, limited surface information was captured by the AFM at the submicron scale. Having said that, the AFM images can reveal the nano-scaled changes in the surfaces in the initial degradation process which is critical for understanding root causes of OA.
Further understanding of surface changes in the OA process The numerical analysis results obtained in this study have revealed that, once OA initiates at the submicron level, detectable changes in the surface morphologies of the diseased cartilages are spatially related. More specifically, the nano-scaled feature that could be used to distinct the difference in OA conditions are the complexity of the surface texture. It is believed that these
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Figure 5. Images of a human knee cartilage surface with OA grade III: (a) AFM image in an area of 5 mm 3 5 mm, (b) LSM image acquired using a 203 magnification objective lens and of a size of 680 mm 3 500 mm, and (c) a large cartilage surface (approximately 2000 mm 3 1900 mm) obtained by stitching 12 LSM images together (i.e. 3 in the X-direction and 4 in the Y-direction).
alterations are related to changes in the materials properties, the collagen structures and their orientations in the wear process. As OA progresses, micro-scaled changes in the surface become distinctive and quantitatively measurable. This is one of very few studies on the numerical characterizations of the surface morphological evolutions at the nanometer scale. The identified numerical parameters were able to differentiate advanced OA grades from the early ones. However, no numerical parameters were found to be effective in revealing the surface evolutions between all three different grades at the nanoscale. Based on the results, it is believed that it is not
possible or very difficult to use the commonly used amplitude parameters (Sa and Sq) to fully describe the nano-scaled surface changes due to two possible reasons. They are (a) that the small sampling area of this imaging technique makes it difficult and time consuming to obtain representative data over a required sampling area and (b) that the morphological changes at the nano-scale are probably not significant enough to allow reliable and accurate OA assessment between the three OA grades. In contrast, this work has found that distinct morphological changes in the surface were captured at the micrometer scale. From the LSM images of the
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diseased cartilages, there was no clear evidence of surface cracking on later-stage OA cartilages. However, an increase in both ridges and pits was observed when the condition of the cartilage worsened. The change in the surface roughness has been widely reported and confirmed by the quantitative results in this study. The observed evolution of the functional properties of the surface is reported for the first time. A continuous increase in the bearing area ratio (Sdc10_50) may indicate a deterioration of the anti-wear property of the surface, causing a high wear rate of the cartilage, a rougher surface and more cartilage loss.
Change in the mechanical property and in relation to the surface features In comparison to the surface morphological features, it appeared that the changes in the mechanical property gave a better indication of the cartilage degradation at the submicron level. The effective indentation moduli showed a decreasing trend, indicating that the nanostiffness of the cartilages deteriorated. The statistical result presented in section ‘‘Results’’ confirmed that the reduction in the effective indentation moduli was significant enough to reveal the difference between the samples in OA grades I, II and III. As reported in Buckwalter’s work,32 the change in the stiffness of the cartilage is probably due to the disruption of the balance in the collagen–proteoglycan solid matrix, that is, the decrease of matrix synthesis, resulting in a softening effect of the tissue.
Limitations and future work There are certain limitations in this study. Healthy cartilages were not included due to difficulties in obtaining healthy samples, and thus, the understanding of the entire degradation process could not be studied. The nano-scaled surface analysis and nano-mechanical results had a large deviation. It is believed that this deviation was due to the examination of locations on various components of the cartilage.33 More testing needs to be carried out to understand how the properties will be affected by the location and cartilage components. Furthermore, it is known that the mechanical properties and surface morphologies of cartilages are affected by a number of factors, including the condition, sample location, age and gender.34–36 Although other factors may also play a role in cartilage degradation, their influence can be quite complex and were not investigated in this study. Also, the image acquisition and analysis techniques presented here are for investigating the structural changes in cartilage surfaces. They are not ready for clinical applications. Once the cartilage degradation process has been well understood and the technique has been further developed, it may have potential to be applied clinically.
Conclusion With the support of advanced imaging facilities in 3D and quantitative analysis techniques, numerical characterizations were conducted on the surfaces of diseased human cartilage samples at both the micro- and nanometer scales. The results reported in this article have revealed the evolution of the surface morphologies of the cartilages in three OA grades. This study has found that at the submicron level, spatial changes occurred and could be used to differentiate early and advanced OA grades. The effective indentation moduli of human cartilages decreased continuously, giving a good indication of the degradation process from OA grades I to III. At the micrometer scale, changes in the surface roughness and functional properties of the surface were found to be significant enough for OA condition assessment. Through the nano- to micrometer surface characterizations together with the nano-mechanical property investigations, insights into the surface changes have been achieved. Further investigations of relationships of the changes in the nano-scaled surface textures and nano-mechanical properties and age and/or gender effects are needed to assist in understanding the OA process, its fundamental causes and distinctive characteristics for future developments of effective and objective diagnostic techniques. Acknowledgements The authors would like to thank Dr James Price for collecting the clinical samples and providing associated data for the project, Ms Lynn Ferris at UNSW Australia and Professor N. Ketheesan from James Cook University, Australia, for allowing the use of their PC2 labs for the sample preparations and the Mark Wainwright Analytical Centre (Biomedical Imaging Facility) at UNSW Australia for providing the AFM facility for the study. This research was funded by Australia Research Council (ARC) under the discovery funding scheme. Declaration of conflicting interests The authors declare that there is no conflict of interest. Funding This research was financially supported by Australian Research Council (ARC) through the project (DP1093975). References 1. Grushko G, Schneiderman R and Maroudas A. Some biochemical and biophysical parameters for the study of the pathogenesis of osteoarthritis: a comparison between the processes of ageing and degeneration in human hip cartilage. Connect Tissue Res 1989; 19: 149–176. 2. Hudelmaier M, Glaser C, Hohe J, et al. Age-related changes in the morphology and deformational behavior of knee joint cartilage. Arthritis Rheum 2001; 44(11): 2556–2561.
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