gDAIS: a global analysis application for obtaining confidence in FLIM

Fluorescence Lifetime Imaging Microscopy (FLIM) is a popular imaging technique that provides users another dimension for investigating biomolecular states, interactions, and environments. It can, for instance, be used to monitor metabolism or discern different Förster resonance energy transfer (FRET) proximity relationships within cells. FLIM often reveals multiple lifetimes which are usually analyzed via multi-exponential fitting in the time domain and phasor localization in the frequency domain. Both methods yield maps of amplitudes (i.e., alphas/intensities/concentrations) associated with their lifetimes. The resulting "pixel histograms" or "phasor clouds" give the user pixel-weighted visualizations of the distribution of amplitudes vs. lifetime. Using this approach, however, pixel areas with low photon counts can improperly skew FLIM analysis. In contrast, a global decay-associated analysis of image data first optimizes the nonlinear parameters that are common across the image-namely, the lifetimes-along with their amplitudes, and then provides statistically rigorous confidence limits on both lifetimes and amplitudes. With these limits, one may assess cellular metabolism and FRET relationships with confidence. Additionally, global analysis is known to be capable of unmixing multiple components that are inseparable in conventional or graphical analyses. We present global Decay-Associated Image Software (gDAIS) for analyzing FLIM data which incorporates a fast regression method and yields images of the amplitudes and images of all associated uncertainties. These outputs can then be used to generate error limits for amplitude ratios (e.g., free:bound NADH).

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Published
2026-01-15
DOI
https://doi.org/10.1117/12.3081144
Primary Topic
Model-Driven Software Engineering Techniques
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article
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article

gDAIS: a global analysis application for obtaining confidence in FLIM

Dan L. Sackett, Christian A. Combs, Jay R. Knutson
Model-Driven Software Engineering Techniques
article

gDAIS: a global analysis application for obtaining confidence in FLIM

Dan L. Sackett, Christian A. Combs, Jay R. Knutson
article en

Abstract

Fluorescence Lifetime Imaging Microscopy (FLIM) is a popular imaging technique that provides users another dimension for investigating biomolecular states, interactions, and environments. It can, for instance, be used to monitor metabolism or discern different Förster resonance energy transfer (FRET) proximity relationships within cells. FLIM often reveals multiple lifetimes which are usually analyzed via multi-exponential fitting in the time domain and phasor localization in the frequency domain. Both methods yield maps of amplitudes (i.e., alphas/intensities/concentrations) associated with their lifetimes. The resulting "pixel histograms" or "phasor clouds" give the user pixel-weighted visualizations of the distribution of amplitudes vs. lifetime. Using this approach, however, pixel areas with low photon counts can improperly skew FLIM analysis. In contrast, a global decay-associated analysis of image data first optimizes the nonlinear parameters that are common across the image-namely, the lifetimes-along with their amplitudes, and then provides statistically rigorous confidence limits on both lifetimes and amplitudes. With these limits, one may assess cellular metabolism and FRET relationships with confidence. Additionally, global analysis is known to be capable of unmixing multiple components that are inseparable in conventional or graphical analyses. We present global Decay-Associated Image Software (gDAIS) for analyzing FLIM data which incorporates a fast regression method and yields images of the amplitudes and images of all associated uncertainties. These outputs can then be used to generate error limits for amplitude ratios (e.g., free:bound NADH).

Vol. 13856
National Institutes of Health (US), National Heart, Lung, and Blood Institute (US), Eunice Kennedy Shriver National Institute of Child Health and Human Development (US)
Openalex Percentile: Top 87%
Model-Driven Software Engineering Techniques
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