Cartilage is composed of cells and an extracellular matrix, the latter being a composite of a collagen mesh interpenetrated by proteoglycans responsible for tissue osmotic swelling. The matrix composition and structure vary through the tissue depth. Mapping such variability requires tissue sectioning to gain access. The resulting surface roughness, and concomitant proteoglycan loss contribute to large uncertainties in elastic modulus estimates. To extract elasticity values for the bulk matrix which are not obfuscated by the indeterminate surface layer, we developed a novel experimental and data analysis methodology. We analyzed the surface roughness to optimize the probe size, and performed high-resolution (1 μm) elasticity mapping on thin (∼12 μm), epiphyseal newborn mouse cartilage sections cut parallel to the bone longitudinal axis or normal to the articular surface. Mild fixation prevented the major proteoglycan loss observed in unfixed specimens but not the stress release that resulted in thickness changes in the sectioned matrix. Our novel data analysis method introduces a virtual contact point as a fitting parameter for the Hertz model, to minimize the effects of surface roughness and corrects for the finite section thickness. Our estimates of cartilage elasticity converge with increasing indentation depth and, unlike previous data interpretations, are consistent with linearly elastic material. A high cell density that leaves narrow matrix septa between cells may cause the underestimation of elastic moduli, whereas fixation probably causes an overestimation. The proposed methodology has broader relevance to nano- and micro-indentation of soft materials with multiple length scales of organization and whenever surface effects (including roughness, electrostatics, van der Waals forces, etc.) become significant.
ASJC Scopus subject areas
- Condensed Matter Physics