The response of insulin producing beta cells to glucose stimulation is characterized by increased metabolic activity, leading to the production of signaling molecules such as ATP. A healthy response to glucose involves the initiation of oscillations in fuel consumption, oxidative phosphorylation, and ATP production, which lead to a pulsatile pattern of insulin secretion. Numerous investigations have demonstrated that diabetes and obesity interfere with this metabolic cascade. Accordingly, development of new approaches to the treatment of these metabolic disorders requires the ability to monitor key steps in this cascade. Beta cells, however, do not consist of a homogenous population of cells and thus, methodologies that study metabolic parameters should be applied at the resolution of the individual cell. We propose here to develop a system of assays that can be applied at the resolution of a single cell and profile the metabolic response to glucose, focusing on oscillatory ATP production. We will develop a new probe for ATP, which will be based on Fluorescence Resonance Energy Transfer (FRET) and can be imaged at sub-cellular resolution. The probe will be DNA encoded protein that can be delivered as a transgene to primary beta cells using viral vectors. The probe will not require exogenous co-factors and its activity will not consume ATP. The targeting of this probe to specific sub-cellular compartments, such as the mitochondria, using appropriate targeting sequences will allow for accurate monitoring of ATP availability at preferred sites of interest. We will further combine the imaging of ATP with current methodologies for the monitoring of membrane potential and NADH, allowing for simultaneous monitoring of three key steps in the metabolic cascade involved in fuel consumption. We will use these parameters to subdivide the population of beta cells into different metabolic phenotypes. Our long-term goal is to create a database covering the metabolic effect of a w|de spectrum of pharmalogical agents, hormones and factors. Such a database of metabolic profiles will be used to characterize and predict the effect of new compounds and culturing conditions on beta cells and will be useful in establishing a platform for quality standardization of tissue derived islet cells in terms of metabolic function.
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