Development of Novel Technologies for Direct Cellular Patterning for the Establishment of Well Controlled Microenvironments to Facilitate Studies on Cellular Signaling, Sensing, and Other Diffusion-Based Phenomena
Average rating
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Star rating
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Author
Hynes, WilliamDate Published
2016-05-07
Metadata
Show full item recordAbstract
This work focuses on the utilization of novel bioprinting technologies for the investigation of cellular signaling, sensing, and other diffusion-based phenomena with spatiotemporal dependencies. Two different printing techniques were developed for the purpose of fabricating controlled microenvironments comprised of cells, nutrients, hydrogels, and soluble signaling molecules in a repeatable fashion. The first application explored was the development of a novel, bioprinted, cell-based biosensor as a nondestructive method for the monitoring of the cellular redox environment. Mammalian cells were engineered to express a redox sensitive protein and were patterned and immobilized within a photopolymerizing hydrogel matrix, resulting in biocompatible, three-dimensional microenvironments which supported cell growth and facilitated small molecule sensing. Exposure of the printed, redox sensitive cells to oxidative and reductive compounds and monitoring via confocal microscopy demonstrated proper and reversible functioning of the living biosensor. Bioprinting was also used to generate complex, micro-scale, multi-species populations of bacteria in order to evaluate the effects of distance and various forms of competition on syntrophic relationships. An artificial, syntrophic bacterial consortium was printed within controlled microenvironments confined by geometry and nutrient availability. The growth of the printed strains was monitored, analyzed, and compared to the predictions of an experimental, computational bacterial growth model known as COMETS. Results indicated that the general trends exhibited in vitro by most of the examined micro-scale interactions can be predicted in silico, and that the effects of microbial interactions on the micro-scale can differ considerably than those observed at the macro-scale.