Unsupervised clustering FLIM-phasor from multifunctionalized nanoparticles in living cancer cells

In the present work, we developed a groundbreaking algorithm for the automated segmentation of fluorescence lifetime imaging microscopy (FLIM) images, enhancing the analysis of multifunctionalized nanoparticles within living cancer cells. FLIM, a powerful microscopy technique, generates images that capture the fluorescence lifetime across a sample at the pixel level, revealing critical details about the molecular environment. Traditionally, FLIM image analysis has relied on manual segmentation with the phasor plot approach, a graphical representation of FLIM data in a G and S coordinate system, which is susceptible to user bias and inconsistency. Here, we present an automated and free of user-biased thresholding and segmentation algorithm that streamlines with clustering techniques to automatically identify phasor-clusters in the phasor plot space, significantly reducing human error and ensuring reproducible and consistent image segmentation. We demonstrate its application in the context of FLIM images displaying a map of intensity heterogeneity where functionalized nanoparticles affect the cellular metabolism of HeLa cells, reported by NADH, a bright and a dim fluorescent source, respectively, both of biological relevance. This advancement streamlines the segmentation process of complex intensity images, and presents a new tool for precise biomedical imaging segmentation, offering a more objective and reproducible approach to FLIM image analysis.