Phasor analysis of fluorescence lifetime data and its application

Abstract

Fluorescence lifetime imaging microscopy (FLIM) is widely used in biomedical, materials and other fields. It not only has strong specificity and high sensitivity, but also has the capability of quantitative measurement because the fluorescence lifetime is not affected by the intensity of excitation, the concentration of fluorophores and photobleaching, and consequently is able to monitor the changes of microenvironment and reflecting the interaction between molecules. However, its application is limited to some extent by the complexity of data analysis. In order to make FLIM technology more suitable for fast analysis of high-throughput data, a variety of new algorithms for fluorescence lifetime analysis have emerged in recent years, such as phasor analysis, maximum likelihood estimation, first-order moment, Bayesian analysis, and compressed sensing. Among them, the phasor analysis (PA) method obtains the fluorescence lifetime by converting the fitting in the time domain to the direct calculation in the frequency domain. Compared with traditional least-square fitting method, it is not only simpler and faster, but also more suitable for the case of low photon counts. In addition, in the PA approach to FLIM, the fluorescence decay is directly converted into a phasor diagram by simple mathematics, where the phasor points originating from different pixels in the image are represented by the positions in the phasor plot, and thus the graphical representation obtained by PA method is convenient for data visualization and cluster analysis. Therefore, it has become a simple and powerful analysis method for FLIM, and is increasingly favored by researchers. In this paper, the basic principle of PA method and how we can use it are described in detail. And on this basis, the latest application research progress of the method in cell metabolism state measurement, protein interaction study, cell microenvironment measurement, auxiliary pathological diagnosis, and resolution improvement in super-resolution imaging are introduced and summarized. The advantages of PA method in these FLIM applications are focused on, providing useful reference for the research in related fields. Finally, the phasor analysis method for FLIM data analysis and the development trend of its application are prospected.