Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening. On a cellular level, fibrosis is associated with myofibroblast persistence. Myofibroblasts are a highly secretory and contractile phenotype that arise from resident cardiac fibroblasts, among other cell types, after an initiating injury. Of specific interest is the relationship between environmental stiffness and the myofibroblast activation process. Excess ECM produced by myofibroblasts significantly increases the stiffness of the surrounding tissue. Interestingly, increasing tissue stiffness is also a known stimulus of fibroblast activation, initiating a positive feedback loop ultimately resulting in fibrosis.
To better understand this process, the work began with the development of a dynamic stiffening 2D hydrogel model, which was shown to mechanically control myofibroblast activation. A machine learning model was then trained to identify and segment activated myofibroblasts from a population of cultured cells. Next, in order to better understand the activation process, we focused on individual cells, rather than population averages. We then fully characterized cell size and shape and used the measured features to create a model to predict activation state with high accuracy. Further, we used these features and other deep learning techniques to create a continuous classification system that more accurately captures the natural progression. Next, to study the behavior of these cell types in a more natural system, we moved to 3D cell culture. Hydrogels were designed to isolate the effects of environmental stiffness and degradability, and their effects on cell ECM secretion and contractility were quantified. Lastly, we combined our 3D cell culture system with collagen to create an IPN to promote the vasculogenesis of iPSC-EPs where we once again saw that hydrogel stiffness and degradability had a significant effect of the viability and behavior of this cell type.Chemical Engineerin
