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Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging
Fluorescence reconstruction microscopy (FRM) describes a class of techniques where transmitted light images are passed into a convolutional neural network that then outputs predicted epifluorescence images. This approach enables many benefits including reduced phototoxicity, freeing up of fluorescen...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7802935/ https://www.ncbi.nlm.nih.gov/pubmed/33362219 http://dx.doi.org/10.1371/journal.pcbi.1008443 |
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author | LaChance, Julienne Cohen, Daniel J. |
author_facet | LaChance, Julienne Cohen, Daniel J. |
author_sort | LaChance, Julienne |
collection | PubMed |
description | Fluorescence reconstruction microscopy (FRM) describes a class of techniques where transmitted light images are passed into a convolutional neural network that then outputs predicted epifluorescence images. This approach enables many benefits including reduced phototoxicity, freeing up of fluorescence channels, simplified sample preparation, and the ability to re-process legacy data for new insights. However, FRM can be complex to implement, and current FRM benchmarks are abstractions that are difficult to relate to how valuable or trustworthy a reconstruction is. Here, we relate the conventional benchmarks and demonstrations to practical and familiar cell biology analyses to demonstrate that FRM should be judged in context. We further demonstrate that it performs remarkably well even with lower-magnification microscopy data, as are often collected in screening and high content imaging. Specifically, we present promising results for nuclei, cell-cell junctions, and fine feature reconstruction; provide data-driven experimental design guidelines; and provide researcher-friendly code, complete sample data, and a researcher manual to enable more widespread adoption of FRM. |
format | Online Article Text |
id | pubmed-7802935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78029352021-01-22 Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging LaChance, Julienne Cohen, Daniel J. PLoS Comput Biol Research Article Fluorescence reconstruction microscopy (FRM) describes a class of techniques where transmitted light images are passed into a convolutional neural network that then outputs predicted epifluorescence images. This approach enables many benefits including reduced phototoxicity, freeing up of fluorescence channels, simplified sample preparation, and the ability to re-process legacy data for new insights. However, FRM can be complex to implement, and current FRM benchmarks are abstractions that are difficult to relate to how valuable or trustworthy a reconstruction is. Here, we relate the conventional benchmarks and demonstrations to practical and familiar cell biology analyses to demonstrate that FRM should be judged in context. We further demonstrate that it performs remarkably well even with lower-magnification microscopy data, as are often collected in screening and high content imaging. Specifically, we present promising results for nuclei, cell-cell junctions, and fine feature reconstruction; provide data-driven experimental design guidelines; and provide researcher-friendly code, complete sample data, and a researcher manual to enable more widespread adoption of FRM. Public Library of Science 2020-12-23 /pmc/articles/PMC7802935/ /pubmed/33362219 http://dx.doi.org/10.1371/journal.pcbi.1008443 Text en © 2020 LaChance, Cohen http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article LaChance, Julienne Cohen, Daniel J. Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging |
title | Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging |
title_full | Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging |
title_fullStr | Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging |
title_full_unstemmed | Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging |
title_short | Practical fluorescence reconstruction microscopy for large samples and low-magnification imaging |
title_sort | practical fluorescence reconstruction microscopy for large samples and low-magnification imaging |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7802935/ https://www.ncbi.nlm.nih.gov/pubmed/33362219 http://dx.doi.org/10.1371/journal.pcbi.1008443 |
work_keys_str_mv | AT lachancejulienne practicalfluorescencereconstructionmicroscopyforlargesamplesandlowmagnificationimaging AT cohendanielj practicalfluorescencereconstructionmicroscopyforlargesamplesandlowmagnificationimaging |