Comparative study of in-vitro autofluorescence of normal versus non-melanoma-skin-cancer cells at different excitation wavelengths

Automatic segmentation of lysosomes and analysis of intracellular pH with Radachlorin photosensitizer and FLIM

We report application of the fluorescence lifetime imaging microscopy (FLIM) for analysis of distributions of intracellular acidity using a chlorin-e6 based photosensitizer Radachlorin. An almost two-fold increase of the photosensitizer fluorescence lifetime in alkaline microenvironments as compared to acidic ones allowed for clear distinguishing between acidic and alkaline intracellular structures. Clusterization of a phasor plot calculated from fits of the FLIM raw data by two Gaussian distributions provided accurate automatic segmentation of lysosomes featuring acidic contents. The approach was validated in colocalization experiments with LysoTracker fluorescence in living cells of four established lines. The dependence of photosensitizer fluorescence lifetime on microenvironment acidity allowed for estimation of pH inside the cells, except for the nuclei, where photosensitizer does not penetrate. The developed method is promising for combined application of the photosensitizer for both photodynamic treatment and diagnostics.

Towards label-free non-invasive autofluorescence multispectral imaging for melanoma diagnosis

This study focuses on the use of cellular autofluorescence which visualizes the cell metabolism by monitoring endogenous fluorophores including NAD(P)H and flavins. It explores the potential of multispectral imaging of native fluorophores in melanoma diagnostics using excitation wavelengths ranging from 340 nm to 510 nm and emission wavelengths above 391 nm. Cultured immortalized cells are utilized to compare the autofluorescent signatures of two melanoma cell lines to one fibroblast cell line. Feature analysis identifies the most significant and least correlated features for differentiating the cells. The investigation successfully applies this analysis to pre-processed, noise-removed images and original background-corrupted data. Furthermore, the applicability of distinguishing melanomas and healthy fibroblasts based on their autofluorescent characteristics is validated using the same evaluation technique on patient cells. Additionally, the study tentatively maps the detected features to underlying biological processes. This research demonstrates the potential of cellular autofluorescence as a promising tool for melanoma diagnostics.