Metabolism provides the energy and building blocks necessary for all cellular functions. A breakdown in metabolic function can give rise to devastating diseases, including cancer, diabetes, Parkinson's disease, and epilepsy. Yet, we have only a limited understanding of metabolic processes in individual living cells and tissues. The largest roadblock has been a general lack of tools for measuring metabolic transients that have high temporal and spatial resolution and also maintain the integrity of the cell. Genetically encoded fluorescent biosensors (GEFBs) have begun to bridge this gap. However, many first- generation GEFBs are still tremendously challenging or impractical to use due to low signal-to- noise (SNR), suboptimal dynamic and sensing ranges, and dependence on pH. The inherent slowness of the current approach to optimizing GEFBs has significantly hindered progress in this area. The proposed research seeks to overcome this critical barrier in the field of metabolism by establishing two new methods that will dramatically accelerate the development of biosensors optimized for use in living cells:
Aim 1) a high-throughput, high- content screen for rapid optimization of GEFBs;
and Aim 2) a multiple cloning toolkit for quick construction of novel dimerization-dependent GEFBs. The results of this work will help drive significant progress in the field of metabolism, and the GEFBs generated using these methods will be broadly applicable in multiple cell types, including neurons, cardiac and skeletal myocytes, and pancreatic ? cells.
Metabolism provides the energy and the building blocks necessary for life, and a breakdown in metabolic function can give rise to devastating diseases, not only diseases directly linked to metabolism (diabetes and obesity), but many others including cancer and neurodegenerative diseases like Parkinson's disease and Alzheimer's disease. Genetically encoded fluorescent biosensors (GEFBs) are a recently developed set of molecular tools that have offered a way to observe metabolic processes in living cells, but many GEFBs are still extremely challenging to use and give weak or unreliable signals. The proposed research will overcome this barrier by establishing two new methods that will dramatically accelerate the improvement of these critical biologically- relevent tools, making them broadly accessible across the field.
