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Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation
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 slowin...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710711/ https://www.ncbi.nlm.nih.gov/pubmed/33268900 http://dx.doi.org/10.1038/s41598-020-77737-0 |
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author | Zickus, Vytautas Wu, Ming-Lo Morimoto, Kazuhiro Kapitany, Valentin Fatima, Areeba Turpin, Alex Insall, Robert Whitelaw, Jamie Machesky, Laura Bruschini, Claudio Faccio, Daniele Charbon, Edoardo |
author_facet | Zickus, Vytautas Wu, Ming-Lo Morimoto, Kazuhiro Kapitany, Valentin Fatima, Areeba Turpin, Alex Insall, Robert Whitelaw, Jamie Machesky, Laura Bruschini, Claudio Faccio, Daniele Charbon, Edoardo |
author_sort | Zickus, Vytautas |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7710711 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77107112020-12-03 Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation Zickus, Vytautas Wu, Ming-Lo Morimoto, Kazuhiro Kapitany, Valentin Fatima, Areeba Turpin, Alex Insall, Robert Whitelaw, Jamie Machesky, Laura Bruschini, Claudio Faccio, Daniele Charbon, Edoardo Sci Rep Article 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. Nature Publishing Group UK 2020-12-02 /pmc/articles/PMC7710711/ /pubmed/33268900 http://dx.doi.org/10.1038/s41598-020-77737-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Zickus, Vytautas Wu, Ming-Lo Morimoto, Kazuhiro Kapitany, Valentin Fatima, Areeba Turpin, Alex Insall, Robert Whitelaw, Jamie Machesky, Laura Bruschini, Claudio Faccio, Daniele Charbon, Edoardo Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation |
title | Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation |
title_full | Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation |
title_fullStr | Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation |
title_full_unstemmed | Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation |
title_short | Fluorescence lifetime imaging with a megapixel SPAD camera and neural network lifetime estimation |
title_sort | fluorescence lifetime imaging with a megapixel spad camera and neural network lifetime estimation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710711/ https://www.ncbi.nlm.nih.gov/pubmed/33268900 http://dx.doi.org/10.1038/s41598-020-77737-0 |
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