2 research outputs found
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Genetic Control of Radical Crosslinking in a Semi-Synthetic Hydrogel
Enhancing materials with the qualities of living systems, including sensing, computation, and adaptation, is an important challenge in designing next-generation technologies. Living materials address this challenge by incorporating live cells as actuating components that control material function. For abiotic materials, this requires new methods that couple genetic and metabolic processes to material properties. Toward this goal, we demonstrate that extracellular electron transfer (EET) from Shewanella oneidensis can be leveraged to control radical cross-linking of a methacrylate-functionalized hyaluronic acid hydrogel. Cross-linking rates and hydrogel mechanics, specifically storage modulus, were dependent on various chemical and biological factors, including S. oneidensis genotype. Bacteria remained viable and metabolically active in the networks for a least 1 week, while cell tracking revealed that EET genes also encode control over hydrogel microstructure. Moreover, construction of an inducible gene circuit allowed transcriptional control of storage modulus and cross-linking rate via the tailored expression of a key electron transfer protein, MtrC. Finally, we quantitatively modeled hydrogel stiffness as a function of steady-state mtrC expression and generalized this result by demonstrating the strong relationship between relative gene expression and material properties. This general mechanism for radical cross-linking provides a foundation for programming the form and function of synthetic materials through genetic control over extracellular electron transfer.Center for Dynamics and Control of Material
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Understanding the fibroblast to myofibroblast activation process using dynamic hydrogels and machine learning
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