Metabolic Acticity Using Nonlinear Imaging
My doctoral research is focused on advancing optical microscopy to enable more quantitative, real-time measurements of cellular metabolism in living systems. Many biological imaging techniques rely on external labels that can interfere with the processes being observed. My work seeks to transcend these limitations by building upon label-free methods that use the natural optical properties of the tissue itself.
The approach utilizes multimodal nonlinear microscopy techniques, which capture several signals simultaneously. A key aspect of this is imaging the autofluorescence of two molecules, NADH and FAD. These are essential coenzymes that act as central hubs in cellular energy production, effectively functioning as the cell’s rechargeable batteries. The balance between their oxidized and reduced states, which can be detected optically, provides a direct window into metabolic activity—for instance, whether a cell is relying on glycolysis or oxidative phosphorylation.
While this method provides rich, high-resolution images, moving from qualitative observation to robust quantification presents a significant scientific challenge. The raw intensity of the autofluorescence signal is an unreliable proxy for metabolic activity. Accurate measurements are confounded by numerous variables, including depth-dependent signal loss, tissue-induced optical distortions, and the complex biochemical environment of the cell.
The goal of my research is to develop a framework to better account for these variables, aiming to extract more reliable metabolic information from these intrinsic optical signals. Improved quantitative insights into cellular metabolism could offer a valuable tool for studying biological processes where energy dynamics are critical, such as understanding metabolic shifts in cancer or tracking the functional state of immune cells.