Exploring the effects of extracellular fluid viscosity on cell migration and cancer metastasis

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PROJECT SUMMARY Mechanical cues and the local physical environment play fundamental roles in a host of cellular processes, ranging from stem cell differentiation to cell motility. While the significance of the stiffness of the extracellular microenvironment has been well-studied, the role of others physical factors, such as fluid viscosity and hydrostatic pressure, are less understood. The viscosities of biological fluids span orders of magnitude, and due to lymph circulation, disease development, and fluctuations in protein secretion, cells directly in contact with mucus, extracellular fluid (ECF), and saliva are often subjected to variations in viscosity. Additionally, abnormal ECF viscosity is associated with diseases such as cystic fibrosis, idiopathic pulmonary fibrosis, and cancer. In the context of cancer, leaky vasculature and matrix degradation within the tumor microenvironment lead to high local concentrations of plasma proteins and soluble collagen that could increase ECF viscosity. Moreover, mucins, the large, heavily-glycosylated extracellular proteins responsible for the high viscosity of mucus and saliva, are overexpressed in many malignancies. Despite that higher mucus/ECF viscosity is observed in chronic lung disease and cancer patients, it is not well studied, that how cells are further affected by the altered viscosity, as well as how altered viscosity can additionally promote the disease progression. A recent study revealed that counterintuitively cell migration speed increases in mesenchymal-like cell lines, as the cells navigate the aquatic environment of higher viscosity. I plan to characterize the effects of high viscosity in multiple cell types and cocultures that are physiologically relevant, to identify the mechanisms behind the changes in cell motility and cytoskeleton dynamics to elevated viscosity, and to build a quantitative model to predict the effect of viscosity in cell migration, and to predict the effect of potential pharmaceutical perturbations.