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Due to the ever-shrinking size of electronic devices and systems, the rapid development of micro- and nano-based technology and equipment has gained momentum. One such device is atomic force microscopy (AFM), which can scan biological surfaces and retrieve topographic or biomechanical information at both nano- and micrometer scales1,2. Among its vast features, this tool can be operated as a micro- as well as a nano-indenter to obtain information about the mechanical properties of various biological systems3,4,5,6. The data are collected by physical contact with the surface through a mechanical probe, which can be as small as about 1 nm at its tip7. The resulting deformation of the sample is then displayed based on the indentation depth of the cantilever tip and the force applied on the sample8.
Osteoarthritis (OA) is a long-term degenerative chronic disease characterized by deterioration of the articular cartilage in the joints and surrounding tissues, which can lead to complete exposure of the bone surfaces. The burden of OA is substantial; currently, half of all women and one-third of all men aged 65 and over suffer from OA9. Traumas, obesity, and the resulting altered biomechanics of the joint10 determine the articular cartilage degeneration, which is viewed as a common end result. The pioneering study of Ganz et al. posited that the early steps of the OA process may involve the biomechanical properties of cartilage11, and since then researchers have confirmed this hypothesis12. Likewise, it is generally accepted that the biomechanical properties of the tissue are functionally orchestrated by the ultrastructural organization as well as cell-cell and cell-matrix crosstalk. Any alterations can dramatically impact the overall tissue biomechanical functioning13. To date, OA diagnosis is clinical and is based on plain film radiography14. This approach is two-sided: firstly, the lack of a defined degenerative cut-off threshold to formulate the diagnosis of OA makes the condition difficult to quantify, and, secondly, imaging methods lack sensitivity and standardization and cannot detect localized cartilage damage15,16,17. To this end, the assessment of the mechanical properties of the cartilage has the decisive advantage that it describes a parameter that changes during the course of OA regardless of the etiology of the disease and has a direct influence on tissue functionality at a very early stage. Indentation instruments measure the force by which the tissue resists the indentation. This is, in fact, not a new concept; the earliest studies date back to the 1980s and 1990s. In this period, numerous studies suggested that indentation instruments designed for the arthroscopic measurements of articular cartilage could be well suited to detect degenerative changes in the cartilage. Even 30 years ago, some studies were able to demonstrate that indentation instruments were able to detect in vivo changes in the cartilage surface during tissue degeneration by conducting compressive stiffness measurements during arthroscopy18,19,20.
AFM indentation (AFM-IT) of the articular cartilage provides information about a pivotal mechanical property of the tissue, namely, stiffness. This is a mechanical parameter that describes the relation between an applied, nondestructive load and the resultant deformation of the indented tissue area21. AFM-IT has been shown to be capable of quantifying age-dependent modifications in stiffness in macroscopically unaffected collagen networks, thus, differentiating between the pathological changes associated with OA onset (grade 0 on the Outerbridge scale in articular cartilage)22. We have previously shown that AFM-ITs, on the basis of spatial chondrocyte organization as an image-based biomarker for early cartilage degeneration, allow for not only quantifying but also actually pinpointing the earliest degenerative mechanical changes. These findings have already been confirmed by others23,24. Hence, AFM-IT acts as an interesting tool to diagnose and identify early degenerative changes. These changes can be already measured at a cellular level, reshaping the understanding of the OA pathophysiological process.
In this protocol, we demonstrate a complete histological and biomechanical grading procedure of articular cartilage explants, from native cartilage explant preparation to AFM data acquisition and processing. Through a step-by-step approach, we show how to generate, grade, and visually classify articular cartilage tissue according to different stages of degeneration by means of 2D large mosaic imaging, followed by micro-AFM indentations.
Even though, currently, AFM-IT is one of the most sensitive tools to measure biomechanical changes in cartilage7, like any other instrumental technique, it has limitations and practical peculiarities25 that can lead to erroneous data acquisition. To this end, we subject to scrutiny the most common problems that arise during AFM measurements of the cartilage explants and describe, where possible, how to minimize or overcome them. These include topographical aspects of the samples and the difficulties to stabilize them in an AFM-compatible environment, physical peculiarities of the tissue's surface, and the resulting difficulties in performing AFM measurements on such surfaces. Examples of erroneous force-distance curves are also presented, emphasizing the conditions that may cause them. Additional limitations inherent to the geometry of the cantilever tip and the use of the Hertz model for the data analysis are also discussed.