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Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy
Published on: January 18, 2017
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Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells
Yuan-I Chen1, Yin-Jui Chang1, Shih-Chu Liao2
1Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
Communications Biology
|January 12, 2022
Summary
A new deep learning method, flimGANE, rapidly generates accurate fluorescence lifetime imaging microscopy (FLIM) images. This approach excels in low-photon conditions, overcoming limitations of current FLIM techniques for biological research.
Area of Science:
- Biophotonics and Imaging
- Computational Biology
- Molecular and Cellular Biology
Background:
- Fluorescence lifetime imaging microscopy (FLIM) quantifies molecular states in cells, independent of concentration or excitation power.
- Existing FLIM methods are computationally demanding or unreliable with low photon counts.
Purpose of the Study:
- To introduce flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation), a novel deep learning method for FLIM image generation.
- To enable rapid and accurate FLIM imaging, especially under photon-starved conditions.
Main Methods:
- Development of a deep learning model, flimGANE, utilizing Generative Adversarial Network Estimation.
- Evaluation of flimGANE's speed and accuracy against the gold standard time-domain maximum likelihood estimation (TD_MLE).
Main Results:
- flimGANE achieves up to 2,800 times greater speed than TD_MLE.
- The method generates accurate, high-quality FLIM images even with low photon counts.
- Improved analysis of low-photon histograms for barcode identification, cellular structures, FRET, and metabolic states in live cells.
Conclusions:
- flimGANE offers a significant advancement in FLIM imaging, providing speed and reliability.
- Its capabilities are crucial for fundamental biological research and clinical applications requiring high-speed analysis.
