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Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation

Biology

Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation

V. Zickus, M. Wu, et al.

This groundbreaking research on scan-less wide-field FLIM reveals a significant leap in cellular imaging, achieving an impressive acquisition rate of 1 Hz using a novel time-gated SPAD camera. With an astonishing 1000-fold acceleration in fluorescence lifetime estimation through a pre-trained neural network, this study promises transformative potential for real-time applications, conducted by Vytautas Zickus and colleagues.

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~3 min • Beginner • English
Abstract
Fluorescence lifetime imaging microscopy (FLIM) is a key technology that provides direct insight into cell metabolism, cell dynamics and protein activity. However, determining the lifetimes of different fluorescent proteins requires the detection of a relatively large number of photons, hence slowing down total acquisition times. Moreover, there are many cases, for example in studies of cell collectives, where wide-field imaging is desired. We report scan-less wide-field FLIM based on a 0.5 MP resolution, time-gated Single Photon Avalanche Diode (SPAD) camera, with acquisition rates up to 1 Hz. Fluorescence lifetime estimation is performed via a pre-trained artificial neural network with 1000-fold improvement in processing times compared to standard least squares fitting techniques. We utilised our system to image HT1080—human fibrosarcoma cell line as well as Convallaria. The results show promise for real-time FLIM and a viable route towards multi-megapixel fluorescence lifetime images, with a proof-of-principle mosaic image shown with 3.6 MP.
Publisher
Scientific Reports
Published On
Dec 02, 2020
Authors
Vytautas Zickus, Ming-Lo Wu, Kazuhiro Morimoto, Valentin Kapitany, Areeba Fatima, Alex Turpin, Robert Insall, Jamie Whitelaws, Laura Machesky, Claudio Bruschini, Daniele Faccio, Edoardo Charbon
Tags
FLIM
cellular imaging
time-gated SPAD camera
neural network
real-time applications
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