UNET-FLIM: A Deep Learning-Based Lifetime Determination Method Facilitating Real-Time Monitoring of Rapid Lysosomal pH Variations in Living Cells.

Journal: Analytical chemistry
PMID: 39902564

Abstract

Lifetime determination plays a crucial role in fluorescence lifetime imaging microscopy (FLIM). We introduce UNET-FLIM, a deep learning architecture based on a one-dimensional U-net, specifically designed for lifetime determination. UNET-FLIM focuses on handling low photon count data with high background noise levels, which are commonly encountered in fast FLIM applications. The proposed network can be effectively trained using simulated decay curves, making it adaptable to various time-domain FLIM systems. Our evaluations of simulated data demonstrate that UNET-FLIM robustly estimates lifetimes and proportions, even when the signal photon count is extremely low and background noise levels are high. Remarkably, UNET-FLIM's insensitivity to noise and minimal photon count requirements facilitate fast FLIM imaging and real-time lifetime analysis. We demonstrate its potential by applying it to monitor rapid lysosomal pH variations in living cells during in situ acetic acid treatment, all without necessitating any modifications to existing FLIM systems.

Authors

  • Danying Lin
  • Qin Kang
    Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
  • Jia Li
    Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan Tsuihang New District, Guangdong, 528400, PR China; School of Pharmacy, Zunyi Medical University, Zunyi, 563000, PR China; National Center for Drug Screening, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, PR China.
  • Mengjiao Nie
    Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
  • Yongtu Liao
    Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
  • Fangrui Lin
    Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
  • Bin Yu
    Department of Anesthesiology, Peking University First Hospital, Ningxia Women's and Children's Hospital, Yinchuan, China.
  • Junle Qu
    Shenzhen Key Laboratory of Ultrafast Laser Micro/Nano Manufacturing, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education/Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.

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