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.
Reduced nicotinamide adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD) are coenzymes of cellular metabolism reactions, and their endogenous fluorescent signals are used to evaluate cell redox state and detect changes in cellular metabolism. Different cellular metabolic states can alter NADH and FAD fluorescence features. Here, a model is developed to determine T cell metabolic pathway utilization from autofluorescence lifetime imaging features. The model is trained and tested using cellular features extracted from NADH and FAD fluorescence lifetime images of activated and quiescent T cells with chemical inhibition of glycolysis, oxidative phosphorylation, glutaminolysis, and fatty acid synthesis. Feature analysis revealed the optical redox ratio (FAD intensity/ (NADH intensity + FAD intensity), the fluorescence lifetime redox ratio (fraction of bound NADH/fraction of bound FAD), and the fluorescence lifetime of free NADH are the highest weighed features for classification of T cells dependent on glycolysis versus oxidative metabolism. High classification accuracy is achieved for discrimination between quiescent and activated T cells, and modest classification accuracy is achieved for classification of T cells into metabolic subgroups. Autofluorescence features vary between cytoplasm and mitochondria and analyzing this difference can provide additional metabolic information. Altogether, these results demonstrate the potential for autofluorescence lifetime imaging features to classify T cell function and metabolic state.
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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