Year: 2025
Type: article
Abstract: 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 several 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.
Source: Scientific Reports
Authors: Soheil Soltani, Jack G. Paulson, Emma J. Fong, Shannon M. Mumenthaler, Andrea M. Armani
Institutions: Ellison Medical Institute, University of Southern California
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: G1399235550, Multiscale systems biology modeling to exploit tumor-stromal metabolic crosstalk in colorectal cancer, G3687380347, Collaborative Research: Electromagnet-integrated optical microscope stage with biocompatible magnetogel for investigating mechanobiology in 2D and 3D, G8876996369, University of Southern California Norris Comprehensive Cancer Center Support Grant (CCSG)
OpenAlex ID: W4416291689