Denoising of Fluorescence Lifetime Imaging Data via Principal Component Analysis

Fluorescence Lifetime Imaging Microscopy (FLIM) quantifies autofluorescence lifetime to assess cellular metabolism, therapeutic efficacy, and disease progression. These dynamic and heterogeneous processes complicate signal analysis. Fit-free analysis methods such as phasor analysis are increasingly used due to limitations of fit-based approaches. However, incorporating photon-counting shot noise often leads to moderate-to-high uncertainty in detecting subtle changes. Common noise-reduction strategies can introduce errors and cause data loss. We developed noise-corrected principal component analysis (NC-PCA), which selectively identifies and removes noise to isolate the signal of interest. We validated NC-PCA by analyzing FLIM images of patient-derived colorectal cancer organoids treated with various therapeutics. First, we show NC-PCA decreases uncertainty by up to 5.5-fold compared to conventional analysis and reduces data loss over 50-fold. Then, using a merged dataset, NC-PCA reveals multiple metabolic states. Overall, NC-PCA offers a powerful, generalizable tool to enhance FLIM analysis and improve detection of biologically relevant metabolic changes.