Noise-Corrected Principal Component Analysis of fluorescence lifetime imaging data

Fluorescence Lifetime Imaging (FLIM) is an attractive microscopy method in the life sciences, yielding information on the sample otherwise unavailable through intensity-based techniques. A novel Noise-Corrected Principal Component Analysis (NC-PCA) method for time-domain FLIM data is presented here. The presence and distribution of distinct microenvironments are identified at lower photon counts than previously reported, without requiring prior knowledge of their number or of the dye's decay kinetics. A noise correction based on the Poisson statistics inherent to Time-Correlated Single Photon Counting is incorporated. The approach is validated using simulated data, and further applied to experimental FLIM data of HeLa cells stained with membrane dye di-4-ANEPPDHQ. Two distinct lipid phases were resolved in the cell membranes, and the modification of the order parameters of the plasma membrane during cholesterol depletion was also detected.

The presence and distribution of multiple fluorescent emitting species are resolved in Fluorescence Lifetime Imaging data using NC-PCA, a Principal Component Analysis tailored for single photon counting through a Poisson noise correction. NC-PCA allows sensitive resolution in low intensity situations characteristic of live cell imaging, and prior knowledge of the photophysics of the system is not required.

from #MedicinebyAlexandrosSfakianakis via xlomafota13 on Inoreader http://ift.tt/2hkL4ub
via IFTTT