The objective of this thesis was to expand the current understanding of how biomechanical factors mediate a variety of processes in articular cartilage, using an approach that focused on single cell biomechanics and mechanobiology. Single chondrocytes were mechanically tested, to derive salient biomechanical parameters that could aid in more accurate descriptions of the in vivo cellular mechanical environment. Building upon these results, single chondrocytes were then subjected to static and dynamic mechanical forces, and the resulting changes in the expression of key genes was measured using single cell real-time RT-PCR. These studies yielded several major findings relevant to chondrocytes and the nature of their responses to mechanical forces. Superficial zone chondrocytes were significantly stiffer than cells from the deeper layers of cartilage. This suggests that cells adapt to their mechanical environment by altering their properties, and that these zone-dependent differences could lead to varying cell responses to the same externally applied mechanical load. Chondrocytes were also found to have strain-dependent recovery properties. Specifically, the residual strain, volume fraction recovered, and recovery time after the cell was compressed were dependent on compressive strain. The most intriguing finding was that the dependence on compressive strain increased at approximately 25-30% strain, suggesting that this range of strain causes a fundamental change in cell biomechanical behavior. Furthermore, this strain range may represent an important threshold for discriminating whether a given mechanical stimulus has a beneficial or deleterious effect on chondrocytes. Finally, dynamic compression was shown to increase type II collagen and aggrecan gene expression compared to statically loaded single chondrocytes. This result was very exciting, as it demonstrated that studying the effects of mechanical forces on single cells was a viable approach. It was also shown that gene expression in single chondrocytes appears to be lognormally distributed. Thus, tests examining populations of cells may be biased by a small fraction of cells with very high levels of gene expression. These findings reinforce the notion that a single cell approach offers significant advantages over existing techniques, and may allow researchers to answer questions that were previously intractable with traditional methodologies.