Beyond the microscope: targeting metabolic regions through FLIM algorithms and phasor pattern segmentation

Metabolic Fluorescence Lifetime Imaging Microscopy (FLIM) algorithms have emerged as powerful tools for unraveling the complex dynamics of cellular and tissue metabolism. FLIM allows for the investigation of metabolic processes by measuring the fluorescence lifetime of endogenous fluorophores as NADH and FAD/FMN. In this work we developed a procedure which aims to find metabolic regions of interest through the identification of patterns, for that we used a combination of 2-photon (2P) excited FLIM, coupled with Time-Correlated Single-Photon Counting (TCSPC) detection. All the data were collected from living cells and were analyzed through exponential fitting. For the pattern segmentation we have used the phasor plot approach. Using our method, we established an analysis procedure for various human cell lines. The quantification results align with the expected trends in NADH lifetimes across different cell lines. However, each cell line exhibits distinct patterns. This approach allowed us to identify and quantify distinct patterns and metabolic areas, for example related to mitochondria in different cell lines. Our goal with this methodology is to identify organelles, such as mitochondria, without the need for staining. This advancement is crucial for diagnosis and therapeutic monitoring, making metabolic imaging increasingly accurate and less invasive.