TY - JOUR
T1 - Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation
AU - Zickus, Vytautas
AU - Wu, Ming-Lo
AU - Morimoto, Kazuhiro
AU - Kapitany, Valentin
AU - Fatima, Areeba
AU - Turpin, Alex
AU - Insall, Robert
AU - Whitelaw, Jamie
AU - Machesky, Larua
AU - Bruschini, Claudio
AU - Faccio, Daniele
AU - Charbon, Edoardo
PY - 2020/12/2
Y1 - 2020/12/2
N2 - 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.
AB - 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.
UR - https://europepmc.org/articles/PMC7710711
UR - https://researchdata.gla.ac.uk/1012/
U2 - 10.1038/s41598-020-77737-0
DO - 10.1038/s41598-020-77737-0
M3 - Article
C2 - 33268900
SN - 2045-2322
VL - 10
JO - Scientific Reports
JF - Scientific Reports
M1 - 20986
ER -