Comparison of phasor analysis and biexponential decay curve fitting of autofluorescence lifetime imaging data for machine learning prediction of cellular phenotypes.

Autor:	Hu L; Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States., Ter Hofstede B; Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States., Sharma D; Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States., Zhao F; Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States., Walsh AJ; Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in bioinformatics [Front Bioinform] 2023 Jun 29; Vol. 3, pp. 1210157. Date of Electronic Publication: 2023 Jun 29 (Print Publication: 2023).
DOI:	10.3389/fbinf.2023.1210157
Abstrakt:	Introduction: Autofluorescence imaging of the coenzymes reduced nicotinamide (phosphate) dinucleotide (NAD(P)H) and oxidized flavin adenine dinucleotide (FAD) provides a label-free method to detect cellular metabolism and phenotypes. Time-domain fluorescence lifetime data can be analyzed by exponential decay fitting to extract fluorescence lifetimes or by a fit-free phasor transformation to compute phasor coordinates. Methods: Here, fluorescence lifetime data analysis by biexponential decay curve fitting is compared with phasor coordinate analysis as input data to machine learning models to predict cell phenotypes. Glycolysis and oxidative phosphorylation of MCF7 breast cancer cells were chemically inhibited with 2-deoxy-d-glucose and sodium cyanide, respectively; and fluorescence lifetime images of NAD(P)H and FAD were obtained using a multiphoton microscope. Results: Machine learning algorithms built from either the extracted lifetime values or phasor coordinates predict MCF7 metabolism with a high accuracy (∼88%). Similarly, fluorescence lifetime images of M0, M1, and M2 macrophages were acquired and analyzed by decay fitting and phasor analysis. Machine learning models trained with features from curve fitting discriminate different macrophage phenotypes with improved performance over models trained using only phasor coordinates. Discussion: Altogether, the results demonstrate that both curve fitting and phasor analysis of autofluorescence lifetime images can be used in machine learning models for classification of cell phenotype from the lifetime data. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Hu, Ter Hofstede, Sharma, Zhao and Walsh.)
Databáze:	MEDLINE
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