Artificial intelligence (AI)/machine-learning (ML) algorithms have been heavily used in data processing in various biological and clinical applications. Sensitive biological signaling can be monitored using two-photon fluorescence lifetime imaging microscopy (FLIM). Lifetime fitting, processing, and analyzing FLIM data of biological specimens can be a challenging and time-consuming affair. The recently developed Fluorescence Lifetime Redox Ratio (FLIRR) focuses on tracking metabolic changes ‘before-and-after-treatment’ in live cells using only two lifetime parameters. FLIM data produces many data parameters which are all associated with drug response in living cells. To predict drug cellular responses, we have chosen the Becker & Hickl SPCImage software to fit the lifetime images and the resultant data was used in ML analysis. With the objective of achieving even more robust statistical power predicting earliest drug effects, we developed Python software and autoencoder (AE) models to analyze the multiple biophysical FLIM parameters acquired in 2p-FLIM images of drug response in cervical cancer cells. The use of systematic hyperparameter (HP) tuning shows the variation in performance across the different models, enabling the selection of the highest performing model and HPs for repetition. Our results show that our optimized multi-parameter trained AE models can statistically outperform single FLIRR time-course analysis in discriminating earliest metabolic changes following drug treatment.
Mitochondrial dysfunction is increasingly being recognized in many pathologies. Mitochondria, the power houses of cells have central roles to play in energy metabolism and apoptosis. Structure-Function studies designed towards characterizing and understanding defects in mitochondrial metabolism, dynamics and biogenesis in pathologies and response to treatments would provide insight into mitochondrial dysfunction. A 2-step imaging approach was used; (a) Zeiss 880/980 Airyscan Super Resolution microscopy to understand mitochondrial morphological response to treatment and (b) Fluorescence Lifetime Imaging (FLIM) -B&H TCSPC lifetime board coupled to a Zeiss 780 to track metabolic changes in HeLa cells by following the auto-fluorescent metabolic co-enzyme NAD(P)H. FLIM signatures, the lifetimes and the relative fractions of bound and free states of NAD(P)H and FAD are generated with multiphoton excitation by a pulsed femto-second infra-red laser. Publications suggest that FLIM multiphoton laser power requirements for NAD(P)H and FAD may not be well optimized, which could result in injurious effects to cells. We have characterized two photon (2p)- laser induced changes at the cellular level, particularly in mitochondria. Live-cell FLIM measurements were conducted on stage in HeLa cells by gradually increasing the laser average power, followed by the assessment of phototoxic effects. Our results show that NAD(P)H-a2%, the enzyme-bound fraction increases with rising laser average power, inducing cytotoxic damaging effects. As elevated NAD(P)H-a2% is also shown after drug treatment, sub-optimal laser power can be falsely interpreted as drug treatment response. Our study demonstrates how the laser power optimization at the specimen plane is critical in FLIM.
With different states of two intrinsic fluorophores, nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD), we developed a single-layer autoencoder (AE) for feature extraction, which outputs condensed features representing the full metabolic FLIM information with lower dimensionality. We also described distributions of AE features and fluorescence lifetime redox ratio (FLIRR) from single cells by Gaussian mixture models (GMM), and predicted the values of FLIRR based on feature data from each time point for the HeLa cell lines and Caucasian-American (LNCaP) prostate cancer cell lines by the polynomial regression model and the random forest regression model.
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