Multiphoton fluorescence lifetime imaging of the metabolic coenzymes reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) allows quantification of cellular metabolism. Due to the link between cellular metabolism and cell function, autofluorescence lifetime imaging provides many features for identification of cells with different phenotypes. Segmentation of multiphoton fluorescence lifetime images allows analysis of data at a single-cell level and quantification of cellular heterogeneity. In this study, Gaussian distribution modeling and machine learning classification algorithms are used for the identification of rare cells within autofluorescence lifetime image data.
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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