Through the induction of a fast thermal gradient, short pulses of infrared light provide a label-free method to stimulate and inhibit action potentials in neurons, but the biophysical effects that underlie this phenomenon are poorly understood. To understand this phenomenon, a computational model of metabolic rates and coenzyme binding dynamics in response to infrared light exposure was developed to investigate the effects of infrared light on cellular metabolism. The resulting model will facilitate our understanding of infrared neural stimulation to accelerate the development of infrared-light technologies which provide a noninvasive, nongenetic, and reversible method to stimulate or inhibit nerve activity.
Imaging the spatial and temporal effects of millisecond duration pulses of infrared light on neurons requires image frame rates approaching 1000+ Hz to capture neural activity. Autofluorescence imaging of the metabolic coenzymes reduced nicotinamide adenine dinucleotide and flavin adenine dinucleotide provides information about cellular metabolism and can be a surrogate measurement of neural activity. Here, we are combining fast fluorescence microscopy techniques with modeling and machine learning to image autofluorescence dynamics in cells following exposure to infrared light.
Autofluorescence lifetime imaging is a useful tool to quantify features of cellular metabolism. Here, we use multiphoton fluorescence lifetime imaging to measure NADH and FAD fluorescence lifetimes. We compared fluorescence intensity and lifetime features of T cells treated with a panel of metabolic inhibitors to correlate imaging features with metabolic pathways. Differences between autofluorescence features of T cells and cancer cells allow robust classification of cell type within simulations of complex tumor tissues. Autofluorescence lifetime imaging combined with automated image segmentation, analysis, and classification enables robust and label-free determination of cell type and function.
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