ABSTRACT
Since the cytoskeleton is known to regulate many cell functions, an increasing amount of effort to characterize cells by their mechanical properties has occured. Despite the structural complexity and dynamics of the multicomponent cytoskeleton, mechanical measurements on single cells are often fit to simple models with two to three parameters, and those parameters are recorded and reported. However, different simple models are likely needed to capture the distinct mechanical cell states, and additional parameters may be needed to capture the ability of cells to actively deform. Our new approach is to capture a much larger set of possibly redundant parameters from cells' mechanical measurement using multiple rheological models as well as dynamic deformation and image data. Principal component analysis and network-based approaches are used to group parameters to reduce redundancies and develop robust biomechanical phenotyping. Network representation of parameters allows for visual exploration of cells' complex mechanical system, and highlights unexpected connections between parameters. To demonstrate that our biomechanical phenotyping approach can detect subtle mechanical differences, we used a Microfluidic Optical Cell Stretcher to mechanically stretch circulating human breast tumor cells bearing genetically-engineered alterations in c-src tyrosine kinase activation, which is known to influence reattachment and invasion during metastasis.
