Cellular metabolism is dysregulated in many diseases. Single-cell measurements of metabolism are important since cellular heterogeneity influences patient outcomes. Single-cell segmentation and analysis of fluorescence lifetime images of the metabolic coenzymes, reduced nicotinamide adenine (phosphate) dinucleotide (NAD(P)H) and oxidized flavin adenine dinucleotide (FAD), provides a label-free method to interrogate metabolism at a cellular level. To facilitate cell-level analysis, we are developing automated segmentation algorithms. Additionally, we are creating and testing models for predicting metabolic phenotypes from fluorescence lifetime metrics. Our applications of single-cell metabolic phenotyping include evaluating responses of cancer cells to chemotherapy and characterizing macrophage phenotypes.
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.
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