Fluorescence Lifetime Imaging Microscopy (FLIM) is a powerful technique in biomedical research that measures the time a fluorophore remains in its excited state before emitting a photon. This method provides insights into the molecular environment of fluorophores, offering advantages over traditional intensity-based fluorescence measurements. FLIM is sensitive to various biomedical processes, including disease progression and drug efficacy [1].
In the context of vaccine and drug development, metabolic analysis is crucial for understanding the molecular mechanisms involved in disease progression and treatment. FLIM can assess cellular metabolism by monitoring the fluorescence lifetimes of endogenous metabolic cofactors such as NAD(P)H and FAD. These cofactors are integral to metabolic pathways like glycolysis and oxidative phosphorylation and exhibit autofluorescence that can be measured using FLIM [2].
Recent studies have demonstrated FLIM’s potential in evaluating the efficacy of vaccines and drugs through metabolic analysis. For instance, FLIM has been used to analyze the metabolism of triple-negative breast cancer cells by measuring the fluorescence lifetimes of NAD(P)H and FAD, providing insights into the cells’ metabolic states [3].
In summary, FLIM is a valuable, non-invasive, and label-free method for assessing the efficacy of vaccines and drugs via metabolic analysis. By monitoring the fluorescence lifetimes of metabolic cofactors, FLIM offers insights into the metabolic activities of cells and tissues, making it a significant tool in biomedical research.
Our setup for assessment of drugs and vaccines efficacy:
Bibliography
[1] Lakowicz, J. R. (2006). Principles of Fluorescence Spectroscopy. Springer.
[2] Skala, M. C., Riching, K. M., Gendron-Fitzpatrick, A., Eickhoff, J., & Karnowski, A. (2010). In vivo multiphoton fluorescence lifetime imaging of protein-bound and free NADH in normal and precancerous epithelia. Journal of Biomedical Optics, 15(2), 027012.
[3] Walsh, A. J., Cook, R. S., Rexer, B., Arteaga, C. L., & Skala, M. C. (2014). Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer. Cancer Research, 74(18), 5184-5194.