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

In the present work, we developed a data-driven algorithm for the automated segmentation of fluorescence lifetime imaging microscopy (FLIM) images, enhancing the analysis of multifunctionalized nanoparticles (NPs) 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 aGandScoordinate 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, reducing user dependency and providing a reproducible strategy under tested conditions image segmentation. We demonstrate its application in the context of FLIM images displaying a map of intensity heterogeneity where functionalized NPs affect the cellular metabolism of HeLa cells, reported by NADH, a bright and a dim fluorescent source, respectively, both of biological relevance. This algorithm provides a transparent and reproducible approach for FLIM image analysis, showing good agreement with expert-defined segmentation under sufficient contrast conditions, while presenting limitations in low-contrast or noisy regimes.