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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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  11. Deep Learning For Fluorescence Lifetime Predictions Enables High-throughput In Vivo Imaging

Deep Learning for Fluorescence Lifetime Predictions Enables High-Throughput In Vivo Imaging

Sofia Kapsiani1, Nino F Läubli1, Edward N Ward1

  • 1Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.

Journal of the American Chemical Society
|June 14, 2025

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Fluorescence Lifetime Macro Imager for Biomedical Applications
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View abstract on PubMed

Summary
This summary is machine-generated.

FLIMngo, a deep learning model, accurately quantifies fluorescence lifetime imaging microscopy (FLIM) data from photon-starved environments. This advancement significantly reduces data acquisition times, making FLIM a higher-throughput tool for live specimen analysis.

Area of Science:

  • Biomedical Optics
  • Machine Learning in Microscopy
  • Fluorescence Spectroscopy

Background:

  • Fluorescence lifetime imaging microscopy (FLIM) is crucial for studying microenvironment changes in biomedical research.
  • Traditional FLIM methods require high photon counts, leading to long acquisition times and limited throughput for live samples.
  • Current techniques struggle with photon-starved data, hindering FLIM's application in dynamic *in vivo* studies.

Purpose of the Study:

  • To introduce FLIMngo, a deep learning model for accurate FLIM data quantification in photon-starved conditions.
  • To enable high-throughput FLIM analysis with reduced data acquisition times and phototoxicity.
  • To enhance the applicability of FLIM for live, dynamic biological specimens.

Main Methods:

  • Development of FLIMngo, a deep learning model leveraging both temporal and spatial information in raw FLIM data.

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  • Quantification of FLIM data from decay curves with fewer than 50 photons per pixel.
  • Benchmarking against traditional phasor plot analysis and other deep learning methods using simulated and experimental data.

    • Main Results:

    • FLIMngo accurately predicts fluorescence lifetimes from photon-starved FLIM data, outperforming existing methods.
    • The model reduces FLIM data acquisition times to seconds, enabling higher throughput and minimizing phototoxicity.
    • Demonstrated successful application in quantifying protein aggregates in live *Caenorhabditis elegans*.

    Conclusions:

    • FLIMngo significantly enhances FLIM's utility as a high-throughput tool for live biological samples.
    • The model's ability to analyze photon-starved data opens new avenues for longitudinal studies in organisms like *C. elegans*.
    • FLIMngo is open-source and readily implementable, requiring no retraining for diverse applications.