# Denoising of Fluorescence Lifetime Imaging Data via Principal Component Analysis

## TL;DR

This paper introduces a new method to reduce noise in FLIM data, improving the accuracy of detecting metabolic changes in cancer organoids.

## Contribution

The novel contribution is the development of noise-corrected PCA (NC-PCA) for denoising FLIM data.

## Key findings

- NC-PCA reduces uncertainty in FLIM data by up to 5.5-fold compared to conventional methods.
- NC-PCA reduces data loss over 50-fold while preserving biologically relevant signals.
- NC-PCA reveals multiple metabolic states in patient-derived colorectal cancer organoids.

## 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 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.