FLIM for Metabolic Imaging
Fluorescence Lifetime Imaging Microscopy (FLIM) provides a robust, label-free technique to study cellular metabolism by analyzing the fluorescence decay of endogenous NADH. In cells, free NADH (linked to glycolysis) and protein-bound NADH (linked to oxidative phosphorylation) emit with different fluorescence lifetimes [1, 2] (Figure 1). By measuring these lifetimes, FLIM enables the visualization and quantification of cellular energy production pathways with high spatial and temporal resolution [3].
Figure 1. In normal versus tumor cells, NADH exhibits different fluorescence lifetimes – being protein-bound in normal cells and free in tumor cells. FLIM detects these metabolic differences by quantifying NADH lifetime shifts, thereby revealing a reprogramming from oxidative phosphorylation to glycolysis.
The Phasor Approach to Lifetime Analysis
Using phasor analysis, FLIM data is transformed into an intuitive, two-dimensional plot where free and bound NADH populations are distinctly mapped [4]. This model-free method avoids complex curve-fitting, offering fast and reliable results. The linear relationship between free and bound NADH lifetimes defines a “metabolic trajectory,” simplifying the interpretation of mixed metabolic states within cells or tissues.
Defining and Calculating the Metabolic Index
The metabolic index is derived from the relative contributions of free and protein-bound NADH along the phasor trajectory [4, 5]. It quantitatively reflects whether a cell relies more on glycolysis or oxidative phosphorylation for its energy needs (Figure 2). High metabolic index values indicate glycolysis dominance, while lower values suggest oxidative metabolism. This calculation is particularly valuable in cancer research, where metabolic reprogramming is a key hallmark of tumor development.
Figure 2. To visualize the FLIM Metabolic Phasor Plot, phasor-plot analysis is performed along the metabolic trajectory, where each image pixel maps to a corresponding point on the phasor plot. Depending on its proximity to the characteristic lifetime of glycolysis (0.4 ns) or oxidative phosphorylation (3.4 ns), a color is assigned using a predefined colormap—light blue indicating glycolysis and violet indicating oxidative phosphorylation.
Bibliography
[1] Zheng, J. I. E. (2012). Energy metabolism of cancer: Glycolysis versus oxidative phosphorylation. Oncology letters, 4(6), 1151-1157.
https://www.spandidos-publications.com/10.3892/ol.2012.928?text=fulltext
[2] Kroemer, G., & Pouyssegur, J. (2008). Tumor cell metabolism: cancer’s Achilles’ heel. Cancer cell, 13(6), 472-482.
https://www.cell.com/cancer-cell/fulltext/S1535-6108(08)00160-8
[3] Blacker, T. S., Sewell, M. D., Szabadkai, G., & Duchen, M. R. (2019). Metabolic profiling of live cancer tissues using NAD (P) H fluorescence lifetime imaging.
Cancer Metabolism: Methods and Protocols, 365-387.
https://link.springer.com/protocol/10.1007/978-1-4939-9027-6_19
[4] Ranjit, S., Malacrida, L., Stakic, M., & Gratton, E. (2019). Determination of the metabolic index using the fluorescence lifetime of free and bound nicotinamide adenine dinucleotide using the phasor approach. Journal of biophotonics, 12(11), e201900156.
https://onlinelibrary.wiley.com/doi/abs/10.1002/jbio.201900156
[5] Shirshin, E. A., Shirmanova, M. V., Gayer, A. V., Lukina, M. M., Nikonova, E. E., Yakimov, B. P., … & Scully, M. O. (2022). Label-free sensing of cells with fluorescence lifetime imaging: The quest for metabolic heterogeneity. Proceedings of the National Academy of Sciences, 119(9), e2118241119.
https://www.pnas.org/doi/full/10.1073/pnas.2118241119