By now, it is well understood that finding two genetically identical cells with the same protein levels is almost impossible. Our team has shown that these differences are not just in gene or protein expression but also in metabolism. This means some cells produce a lot of important biofuel precursors, while others produce much less. However, we still do not know why these non-genetic metabolic differences occur. The main reason is that we lack the tools to measure the necessary metabolic parameters with enough precision at the single-cell level. This project aims to overcome this limitation by developing an optical imaging platform that combines multiple imaging capabilities into a single device, thus, enabling the measurement of all needed metabolic parameters of individual cells. Using our expertise in imaging, optics, and artificial intelligence, we will enhance the imaging sensitivity and resolution of the proposed platform and simplify its hardware configuration using both classical and quantum methods. Additionally, we will use our skills in mass spectrometry, modeling, and biomarker discovery to understand the origins of non-genetic metabolic differences between biofuel over-producers and under-producers. We anticipate this project will identify new gene editing targets for optimizing biofuel-producing strains, thus contributing to a long-standing DOE goal of achieving a predictive understanding of complex biosystems through transformative science and instrumentation.
