Noise-Corrected Principal Component Analysis of fluorescence lifetime imaging data

Area of Science:

• Microscopy techniques
• Cell biology
• Biophysics

Background:

• Fluorescence Lifetime Imaging (FLIM) provides unique insights beyond intensity-based microscopy.
• Analyzing complex biological samples requires advanced data processing methods.

Purpose of the Study:

• To introduce a novel Noise-Corrected Principal Component Analysis (NC-PCA) for time-domain FLIM data.
• To improve the identification of microenvironments in biological samples using FLIM.
• To validate the NC-PCA method's performance at low photon counts.

Main Methods:

• Development of a noise correction algorithm for FLIM data, incorporating Poisson statistics.
• Application of NC-PCA to simulated and experimental time-domain FLIM data.
• Utilizing Time-Correlated Single Photon Counting (TCSPC) for data acquisition.

Main Results:

• NC-PCA successfully identified distinct microenvironments at lower photon counts than previously possible.
• The method does not require prior knowledge of the number of microenvironments or dye decay kinetics.
• Experimental FLIM data of HeLa cells revealed two distinct lipid phases in cell membranes.
• Changes in plasma membrane order parameters during cholesterol depletion were detected.

Conclusions:

• Noise-Corrected Principal Component Analysis is a powerful tool for analyzing FLIM data.
• NC-PCA enables the resolution of complex microenvironments in live cell membranes.
• This method advances the capabilities of FLIM for biological and biophysical studies.