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

Journal: Communications biology
Published Date: Jan 11, 2022

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.

Authors

  • Yuan-I Chen
    Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
  • Yin-Jui Chang
    Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
  • Shih-Chu Liao
    ISS, Inc., 1602 Newton Drive, Champaign, IL, 61822, USA.
  • Trung Duc Nguyen
    Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
  • Jianchen Yang
    Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
  • Yu-An Kuo
    Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
  • Soonwoo Hong
    Department of Bio-convergence Engineering, Korea University, Seoul 02841, Republic of Korea.
  • Yen-Liang Liu
    Master Program for Biomedical Engineering, China Medical University, Taichung 40678, Taiwan.
  • H Grady Rylander
    Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
  • Samantha R Santacruz
    Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
  • Thomas E Yankeelov
    Department of Biomedical Engineering, The University of Texas at Austin, TX 78712, USA.
  • Hsin-Chih Yeh
    Department of Biomedical Engineering, The University of Texas at Austin, TX 78712, USA.

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