Enhanced fluorescence lifetime imaging microscopy denoising via principal component analysis
Year: 2025
Type: preprint
Abstract: Fluorescence Lifetime Imaging Microscopy (FLIM) quantifies the autofluorescence lifetime to measure cellular metabolism, therapeutic efficacy, and disease progression. These dynamic processes are intrinsically heterogeneous, increasing the complexity of the signal analysis. Often noise reduction strategies that combine thresholding and non-selective data smoothing filters are applied. These can result in error introduction and data loss. To mitigate these issues, we develop noise-corrected principal component analysis (NC-PCA). This approach isolates the signal of interest by selectively identifying and removing the noise. To validate NC-PCA, a secondary analysis of FLIM images of patient-derived colorectal cancer organoids exposed to a range of therapeutics was performed. First, we demonstrate that NC-PCA decreases the uncertainty up to 4-fold in comparison to conventional analysis with no data loss. Then, using a merged data set, we show that NC-PCA, unlike conventional methods, identifies multiple metabolic states. Thus, NC-PCA provides an enabling tool to advance FLIM analysis across fields.
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: green
Funder: National Cancer Institute
OpenAlex ID: W4408084886