vFLIM: Machine Learning-enabled Light Sheet Fluorescence Lifetime Imaging
The lifetime of fluorescent molecules provides an orthogonal readout to fluorescence intensity, opening experimental possibilities of measuring changes in local molecular environments, mechanical tension, and metabolism, among other factors. These changes are best studied live and in vivo; however, limitations of slow imaging speeds, high phototoxicity, and increased data size and complexity have…
Fluorescent molecules possess a distinctive characteristic known as fluorescence lifetime, which offers an alternative method to measure fluorescence intensity. This unique attribute allows scientists to investigate alterations in the molecular surroundings, mechanical strain, metabolism, and more. However, observing these changes in living organisms presents several challenges, including sluggish imaging speeds, high phototoxicity, and the generation of extensive, intricate data sets.
In response to these obstacles, researchers have developed a comprehensive and adaptable pipeline comprising a light sheet fluorescence lifetime imaging microscope (FLIM) and a machine learning model for data processing. This innovative approach makes volumetric fluorescence lifetime imaging (vFLIM) feasible for long-term or high-speed monitoring in living systems.
To assess the effectiveness of their pipeline, the researchers subjected it to rigorous testing across a range of biological applications, model organisms, lifetime values, and spatial-temporal scales. The results demonstrate the diverse capabilities that this workflow unlocks, marking a significant advancement in making live vFLIM accessible to the wider bioimaging community.
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