The articular cartilage in synovial joints has a specialized three-dimensional (3D) structure and biochemical composition that provide low-friction, wear-resistance, and load-bearing properties to the tissue. Mechanical loading is essential to tissue homeostasis and influences gene expression, chondrocyte metabolism, extracellular matrix maintenance, and associated interstitial fluid permeability (Vincent and Wann, 2019; Grodzinsky et al., 2000). Therefore, the biomechanical properties of cartilage are unique indicators of tissue homeostasis versus degeneration. Indeed, research into cartilage-related changes during the development of degenerative diseases such as osteoarthritis (OA) has demonstrated that early tissue dysfunction involves loss of proteoglycans and increased water content, resulting in reduced compressive strength and higher tissue permeability (Stolz et al., 2009; Setton et al., 1994). Disease progression leads to further structural damage and altered mechanics (Kleemann et al., 2005), ultimately resulting in tissue failure.

Mouse models are commonly used in pre-clinical studies focused on cartilage repair and pathophysiology of OA (McCoy, 2015). Mice are advantageous because of the well-established development of genetically modified strains (Helminen et al., 2002) and the availability of diverse model systems mimicking mechanisms of spontaneous and induced tissue degeneration and regeneration (Bedelbaeva et al., 2010; Kyostio-Moore et al., 2011; Murphy et al., 2020; Christiansen et al., 2015). In this regard, mouse models serve as powerful tools for the targeted assessment of cellular and molecular processes, and the discovery of novel therapeutics related to cartilage regeneration and OA. Yet, while the structural integrity and biochemical composition of murine cartilage are routinely assessed through histological and molecular approaches, the evaluation of how these features translate into mechanical function is limited. The main challenge in mechanical function assessment stems from the small joint size and thin cartilage found in mice relative to other species (Malda et al., 2013). Prior efforts to overcome these challenges include finite element modeling and optimization of small-scale indentation techniques (Stolz et al., 2009; Cao et al., 2006; Berteau et al., 2016; Das Neves Borges et al., 2014).

Mechanical indentation (including atomic force microscopy [AFM] techniques), performed using either creep or stress-relaxation protocols, is widely employed for assessing the biomechanical behavior of cartilage and OA-related changes in many species (Athanasiou et al., 1991; Risch et al., 2021; Hoch et al., 1983) and considered the gold standard for small animal joints (Lakin et al., 2017). Unlike confined and unconfined compression tests, no sectioning or subsampling of tissue (i.e. cylindrical explants) is required (Mansour, 2013). Instead, the cartilage tissue and its subchondral bone interface is kept intact, providing more physiologically relevant data. Indentation is also advantageous in that it is non-destructive and allows for repeated measures in situ (Lu and Mow, 2008). However, the natural curvature of joint articular surfaces poses a challenge to testing, as indentation must be conducted perpendicularly to the surface (Swann and Seedhom, 1989).

Recently, a novel automated indentation technique has been developed for the mechanical assessment of cartilage (Sim et al., 2017). This commercially available multi-axial apparatus (Mach-1, Biomomentum Inc, Laval, QC) is capable of detecting specimen surface orientation at each position of measurement and subsequently indent normal to the surface (Sim et al., 2017; Biomomentum, 2020). As such, it can map entire cartilage surfaces using a single setup with high spatial density and has been previously shown to discriminate between healthy and diseased human cartilage samples (Sim et al., 2017; Seidenstuecker et al., 2019). This apparatus has been recently employed by Woods and colleagues (Woods et al., 2021) to evaluate altered biomechanics in a mouse model of cartilage degeneration. However, the study was conducted in the non-load-bearing region of the mouse knee joint. Considering that the knee range of motion in mice is between 40 and 145^o (i.e. unable to fully extend) (Jia et al., 2018), with normal gait range between 90.5 and 120^o (extension-flexion) (Hu et al., 2017), the contact regions in the distal femoral condyles are located further posteriorly. Careful consideration of species-specific differences in knee joint anatomy and kinematics is imperative for proper translation of pre-clinical models (Oláh et al., 2021).

Woods and colleagues (Woods et al., 2021) were also unable to account for site-specific cartilage thickness variations in their measurements, instead of using the mean cartilage thickness obtained via histological analysis in their assessment (Woods et al., 2021). Other studies have employed mean cartilage thickness values retrieved through histology or imaging techniques to characterize and model tissue mechanical parameters in mice (Cao et al., 2006; Das Neves Borges et al., 2014). Implicit in this approach is the underlying assumption that cartilage thickness is relatively uniform among medial and lateral compartments (e.g. femoral condyles or tibial plateaus), and along their anteroposterior or mediolateral axis. Yet, regional variations in thickness are recognized within these cartilage surfaces (Malda et al., 2013; Das Neves Borges et al., 2014; Li et al., 2005) and known to impact small-scale indentation measurements (Swann and Seedhom, 1989; Hayes et al., 1972).

Hence, the purpose of this study was to investigate the reliability of automated indentation mapping in the assessment of healthy femoral articular cartilage in mice and characterize site-specific variations in cartilage thickness. We also employed concurrent contrast-enhanced 3D x-ray microscopy (XRM) imaging to validate the cartilage thickness measurements from our optimized needle probing (NP) protocol (Jurvelin et al., 1995). Finally, this approach was used to investigate biomechanical differences in a clinically relevant mouse model of cartilage degeneration (Rhee et al., 2005). Together, we show that automated indentation is reliable and able to characterize topographic and mechanical variations across condyle cartilage locations in intact cartilage. Moreover, this technique was able to identify regional changes in cartilage thickness and stiffness in degenerated cartilage. A comprehensive and standardized biomechanical evaluation of cartilage in repair and disease can greatly contribute to our understanding of tissue structure-function interplay, thereby enhancing the clinical relevance of mouse models in this area.

Using a novel microscale instrumented apparatus, we were able to detect and quantify spatial variations in biomechanical parameters across murine cartilage surfaces, both within healthy and degenerated femoral condyles. Compared to recent mouse studies using this commercially available apparatus (Woods et al., 2021; Lavoie et al., 2015), the optimization of sample mounting and NP protocols allowed for unprecedented quantitative mapping of the mechanical behavior and associated cartilage thickness on load-bearing regions of distal femurs with higher spatial density.

For healthy, 4-month-old C57BL/6 mice, test-retest micro-indentation measurements were reproducible at any given testing site, also indicating that the instantaneous deformation and sustained compression steps in the stress-relaxation protocol did not compromise the mechanical behavior of the freshly harvested cartilage tissue. Heterogeneity in peak forces was identified across anatomical locations. Notably, the medial condyle surface yielded approximately two times higher peak forces on average than its lateral counterpart. Moreover, both condyles displayed a shift toward lower values at posterior regions. Previous studies have shown significant variation in mechanical properties within different cartilage surfaces in healthy joints (Korhonen et al., 2002; Froimson et al., 1997), or even over a single surface (Hoch et al., 1983; Samosky et al., 2005). Considering the structural and material 3D complexities of articular cartilage, it is intuitive that reaction forces in indentation response are influenced by depth of indentation and applied strain rate (Bae et al., 2006). As thickness variations across the cartilage surfaces are unknown prior to indentation test, the relative intra-tissue strain at each testing site varied due to the fixed indentation depth imposed, thereby influencing the measured reaction forces. In general, higher peak reaction forces in intact cartilage were associated with lower thickness values. However, the effect of site-specific cartilage geometry, such as thickness, when characterizing the mechanical properties of cartilage in indentation is often overlooked (Sim et al., 2017; Moshtagh et al., 2016).

Studies in mice commonly rely on mean thickness values from histology, requiring longer turnaround and lacking spatial specificity. The optimized NP protocol used here was able to resolve spatial thickness variations across condyle surfaces and closely localizes cartilage thickness measures to the footprint of indentation on mouse distal femurs. Moreover, 3D visualization of the femoral cartilage surfaces after XRM imaging validated the positioning and distribution of the testing grid used, allowing us to pinpoint individual testing sites and compute their thicknesses. A highly significant correlation between pairwise cartilage thickness values from NP and XRM, per measurement site, was observed. Variations seen between averaged thickness values could be explained by differences in instrument resolution capabilities (Mach-1 z-axis: 0.5 µm vs XRM: 4.39 µm voxel size) and partial volume artifacts, which may affect the accuracy of tissue detection on the XRM segmentation. It is worth noting, however, that previous research (Das Neves Borges et al., 2014) on murine cartilage thickness has been performed using greater voxel sizes. Compromise of cartilage integrity by NP likely contributes to observed differences as well, leading to higher XRM measurements, possibly due to tissue swelling. Comparable mean cartilage thickness values between NP and XRM segmentation from contralateral femurs not subjected to biomechanical testing support this conclusion. Nevertheless, mean thickness values were in line with earlier reports for mouse cartilage (Oláh et al., 2021; Poulet et al., 2013; Kotwal et al., 2012), with marked greater cartilage thicknesses measured on the lateral condyle compared to the medial counterpart (Malda et al., 2013).

Localized cartilage thickness measurements by NP enabled us to account for the effect of cartilage geometry on indentation, with implementation of aspect ratio (indenter radius-to-cartilage-thickness) corrections as per Hayes’s (Hayes et al., 1972) analytical formulation when calculating compressive stiffness distributions at 20% strain (considered as physiological loading; Simha et al., 2007). A common area of concern in in situ indentation of thin materials, such as mouse articular cartilage, is the effect of substrates on the measured mechanical properties, wherein the indentation response of articular cartilage could be somewhat influenced by the underlying rigid bone, especially on thinner areas (Julkunen et al., 2008). While we recognize this limitation when employing microindentation, we argue that knowledge of thickness variations improves the reliability of the indentation assessment and aids interpretation of results following degradation and loss. According to the present results, there was no significant variation in mean thickness of healthy cartilage over a condyle surface, therefore biomechanical parameters were compared between matched regions and sub-regions when assessing degeneration.

Compared to C57BL/6 control, the loss of Prg4 function resulted in an increase in thickness and decrease in stiffness of the knee cartilage in 16-week-old mice. This is consistent with previous observations on hip articular cartilage of Prg4^−/− mice (same age), using AFM indentation (Coles et al., 2010). Apparent modulus values in our study were about an order of magnitude larger than those reported by Coles et al., 2010 for Prg4^−/− and healthy controls, a discrepancy we attribute to differences in compression rate (1 μm/s vs 20 μm/s) and indentation scale (micro- vs. nanoindentation depth), leading to differences in strain fields, and the level of structure that is probed (Bae et al., 2006). It is worth noting that anatomical locations of assessment also differed between studies. The cartilage thickness values determined by NP in our study are in a similar range to those reported by Karamchedu et al., 2016. In their micro-CT imaging study, the Prg4^−/− mice displayed cartilage thickness averaging 61.0±4.3 µm and 42.4±2.6 µm for the load-bearing regions of the lateral and medial condyle, respectively. Furthermore, Prg4^−/− cartilage was also shown to be significantly thicker than in control littermates (Karamchedu et al., 2016), even though mice were younger (10 weeks old) than in our study.

Reduction in the instantaneous compressive modulus in our study was confined to the medial compartment. We attribute that to differences in thickening in Prg4^−/− femoral cartilage compared to controls, 12.6 vs 41% on lateral and medial condyles, respectively, as well as Prg4^−/− compromised structural integrity (Rhee et al., 2005; Jay et al., 2007; Drewniak et al., 2012) having a bigger functional impact on the medial compartment, recognized as the main load-bearing region in most mammalian joints. Microscale indentation characterizes the overall tissue resistance to deformation (Simha et al., 2007) and disrupted architecture, and composition can impart tissue mechanical response. Due to the rapid compression rate employed in the present protocol, normal cartilage tissue is expected to deform with minimal change in volume (Armstrong, 1986), as its low permeability restrains fluid flow; thereby localizing the strain near the surface as fluid pressure pushes against the collagen meshwork. Thus, under instantaneous (rapid) loading the ability of cartilage to resist compression is known to be affected by the collagen fibril meshwork (Julkunen et al., 2008; Laasanen et al., 2003), particularly tangentially oriented collagen fibrils on the superficial tissue layer (Korhonen et al., 2002). In Prg4^−/− mice, the normal parallel organization of collagen fibrils adjacent to the surface is known to be disrupted (Jay et al., 2007), likely affecting fluid pressurization, helping explain the lower instantaneous modulus compared to controls.

Limitations of this study include evaluation of the anatomy of the distal murine femoral condyles and not the opposing articulation (i.e. proximal tibia). Also, we focused on a single timepoint of analysis; however, investigations related to aging and progressive degeneration are of interest as they are known to affect the biochemical composition and structural integrity of cartilage (Rhee et al., 2005; Rahmati et al., 2017; Julkunen et al., 2009a), thereby influencing its mechanical properties. Finally, due to the biphasic/poroviscoelastic nature of cartilage, future studies should consider the use of more complex analytical models (Mow et al., 1980; Julkunen et al., 2009b) that are able to capture the time-dependent viscoelastic behavior of cartilage in mice.

In this study, we have gained insights into the patterns of varying surface geometry and mechanics present within murine articular cartilage at the microscale. 3D indentation mapping was able to resolve site-specific differences in thickness and mechanical properties across knee cartilage surfaces in healthy mice. Moreover, it identified functional changes on the Prg4^−/− mouse model. This technique could also prove helpful for the study of other mouse models mimicking different mechanisms of, or therapies focused on, repair and degeneration of articular cartilage, as microscale indentation with high spatial density can provide a more comprehensive characterization of cartilage’s mechanical properties.

All data generated or analyzed during this study are included in the manuscript and supporting file.

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Author details

1. Anand O Masson

   1. Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada
   2. McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Canada

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7342-7038

2. Bryce Besler

   1. Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada
   2. McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Canada

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

3. W Brent Edwards

   1. Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada
   2. McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Canada
   3. Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Canada

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Roman J Krawetz

   1. Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada
   2. McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Canada
   3. Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Canada
   4. Department of Cell Biology and Anatomy, Cumming School of Medicine, University of Calgary, Calgary, Canada

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing

   For correspondence

   rkrawetz@ucalgary.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2576-4504

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