Dataset Information

Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells.

ABSTRACT: Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation (TD_MLE) and that flimGANE provides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical.

SUBMITTER: Chen YI

PROVIDER: S-EPMC8752789 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells.

Chen Yuan-I YI Chang Yin-Jui YJ Liao Shih-Chu SC Nguyen Trung Duc TD Yang Jianchen J Kuo Yu-An YA Hong Soonwoo S Liu Yen-Liang YL Rylander H Grady HG Santacruz Samantha R SR Yankeelov Thomas E TE Yeh Hsin-Chih HC

Communications biology 20220111 1

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifeti ...[more]

PMID: 35017629

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Dataset Information

Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells.

Publications

Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells.

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