scholarly journals Simultaneous dual-color fluorescence lifetime imaging with novel red-shifted fluorescent proteins

2016 ◽  
Vol 13 (12) ◽  
pp. 989-992 ◽  
Author(s):  
Tal Laviv ◽  
Benjamin B Kim ◽  
Jun Chu ◽  
Amy J Lam ◽  
Michael Z Lin ◽  
...  
Keyword(s):  
Fluorescence Lifetime ◽  
Fluorescent Proteins ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Imaging ◽  
Dual Color

scholarly journals Wide-field Fluorescence Lifetime Imaging Microscopy with a High-Speed Mega-pixel SPAD Camera

2020 ◽  
Author(s):  
V. Zickus ◽  
M.-L. Wu ◽  
K. Morimoto ◽  
V. Kapitany ◽  
A. Fatima ◽  
...  
Keyword(s):  
Fluorescence Lifetime ◽  
High Speed ◽  
Single Photon ◽  
Fluorescent Proteins ◽  
Cell Metabolism ◽  
Fluorescence Lifetime Imaging Microscopy ◽  
Wide Field ◽  
Fluorescence Lifetime Imaging ◽  
Protein Activity ◽  
Lifetime Imaging

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 Megapixel 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 megapixels.

scholarly journals Fluorescence lifetime imaging of fluorescent proteins as an effective quantitative tool for noninvasive study of intracellular processes

2017 ◽  
Vol 11 (01) ◽  
pp. 1730009 ◽  
Author(s):  
Svitlana M. Levchenko ◽  
Artem Pliss ◽  
Junle Qu
Keyword(s):  
Fluorescence Lifetime ◽  
Cultured Cells ◽  
Fluorescent Proteins ◽  
Genetically Engineered ◽  
Fluorescence Lifetime Imaging ◽  
Molecular Processes ◽  
Lifetime Imaging ◽  
New Directions ◽  
Quantitative Tool

Fluorescence lifetime imaging (FLIM) is an effective noninvasive bioanalytical tool based on measuring fluorescent lifetime of fluorophores. A growing number of FLIM studies utilizes genetically engineered fluorescent proteins targeted to specific subcellular structures to probe local molecular environment, which opens new directions in cell science. This paper highlights the unconventional applications of FLIM for studies of molecular processes in diverse organelles of live cultured cells.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vytautas Zickus ◽  
Ming-Lo Wu ◽  
Kazuhiro Morimoto ◽  
Valentin Kapitany ◽  
Areeba Fatima ◽  
...  
Keyword(s):  
Neural Network ◽  
Fluorescence Lifetime ◽  
Single Photon ◽  
Fluorescent Proteins ◽  
Cell Metabolism ◽  
Wide Field ◽  
Fluorescence Lifetime Imaging ◽  
Lifetime Estimation ◽  
Protein Activity ◽  
Lifetime Imaging

AbstractFluorescence 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.

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