Biology subjects

Fong, E. J.

Publications and source records attributed to Fong, E. J..

3 recordsLinked to original sources

bioRxiv2025

Enhanced fluorescence lifetime imaging microscopy denoising via principal component analysis

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.

bioengineering↗

bioRxiv2024

Real-time affinity measurements of proteins synthesized in cell-free lysate using fluorescence correlation spectroscopy

Rapid, high throughput measurements of biomolecular interactions are essential across medicine and bioscience. Traditional methods for affinity-screening proteins require a long and costly process involving cell-based expression, purification, and titration of multiple concentrations to arrive at a binding curve. In contrast, we have developed a fast and simple approach that yields a wealth of information about the expression of the protein and its binding characteristics, all in a "one-pot reaction" and done in under several hours without the need for protein purification. The method uses cell-free protein synthesis to produce the protein of interest in the presence of its binding partner, while simultaneously using fluorescence correlation spectroscopy (FCS) to measure the increasing concentration of the protein and its binding to the binding partner. We characterize the sensitivity limits of this method by measuring the binding between the green fluorescent protein (GFP) and a low picomolar-affinity anti-GFP antibody and found that we can quantify KDs down to the high picomolar to low-nanomolar range. We further demonstrate the method in a potentially ultrahigh-throughput sample format, in which FCS measurements are collected inside microcapsules. This work lays the foundation for a platform aimed at production and in situ affinity screening of thousands of different proteins.

biophysics↗

bioRxiv2024

Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as a crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.

systems biology↗