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Biophysical cues to enhance neuronal differentiation

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Mattiassi_Sabrina.pdf (3.84 MB)

Date

2021-05-31

Authors

Mattiassi, Sabrina

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University of Waterloo

Abstract

Biophysical cues are an important tool for neuronal tissue engineering and regenerative medicine. Cues such as topography and stiffness have been shown to enhance lineage and non-lineage based neuronal differentiation by increasing the rates of differentiation and maturation and by increasing the fraction of cells that commit to the neuronal lineage. Despite the breadth of studies showing their effectiveness, there is a paucity of information regarding how they affect new neuronal generation techniques and how these cues may interact with one another. The aim of this thesis is to investigate these gaps. Doing so, it has been found that hierarchical topographies can significantly enhance non-viral direct neuronal reprogramming of fibroblast. Synergistic effects observed on hierarchical patterns show that they can both increase the fraction of cells that commit to the neuronal lineage and improve subsequent maturation. Second, we have developed a platform to study the combined effects of stiffness and topography on lineage-based differentiation over an extended period. Using an existing polyacrylamide-based platform we have used carbodiimide crosslinking with charged polypeptide-intermediates to stably bound laminin to the surface. Both mouse and human neural progenitor cells and their derived neurons can adhere to these surfaces for extended periods of time. Third, using this developed platform we found that the effects of stiffness and topography on neuronal differentiation are intertwined. Their interaction seems to provide a moderating effect for each of the cues and suggests that the effect of topography on lineage commitment and maturation varies depending on the stiffness of the substrate.

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Keywords

neuronal differentiation, tissue engineering, regenerative medicine, direct neuronal reprogramming, nanotopography, stiffness

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http://hdl.handle.net/10012/17049

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Chemical Engineering