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High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration
Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield fluorescent imaging remains one of the most widel...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9119453/ https://www.ncbi.nlm.nih.gov/pubmed/35588399 http://dx.doi.org/10.1371/journal.pone.0264241 |
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author | Forsgren, Edvin Edlund, Christoffer Oliver, Miniver Barnes, Kalpana Sjögren, Rickard Jackson, Timothy R. |
author_facet | Forsgren, Edvin Edlund, Christoffer Oliver, Miniver Barnes, Kalpana Sjögren, Rickard Jackson, Timothy R. |
author_sort | Forsgren, Edvin |
collection | PubMed |
description | Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield fluorescent imaging remains one of the most widely used. To accomplish imaging of 3D samples, conventional widefield fluorescence imaging entails acquiring a sequence of 2D images spaced along the z-dimension, typically called a z-stack. Oftentimes, the first step in an analysis pipeline is to project that 3D volume into a single 2D image because 3D image data can be cumbersome to manage and challenging to analyze and interpret. Furthermore, z-stack acquisition is often time-consuming, which consequently may induce photodamage to the biological sample; these are major barriers for workflows that require high-throughput, such as drug screening. As an alternative to z-stacks, axial sweep acquisition schemes have been proposed to circumvent these drawbacks and offer potential of 100-fold faster image acquisition for 3D-samples compared to z-stack acquisition. Unfortunately, these acquisition techniques generate low-quality 2D z-projected images that require restoration with unwieldy, computationally heavy algorithms before the images can be interrogated. We propose a novel workflow to combine axial z-sweep acquisition with deep learning-based image restoration, ultimately enabling high-throughput and high-quality imaging of complex 3D-samples using 2D projection images. To demonstrate the capabilities of our proposed workflow, we apply it to live-cell imaging of large 3D tumor spheroid cultures and find we can produce high-fidelity images appropriate for quantitative analysis. Therefore, we conclude that combining axial z-sweep image acquisition with deep learning-based image restoration enables high-throughput and high-quality fluorescence imaging of complex 3D biological samples. |
format | Online Article Text |
id | pubmed-9119453 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91194532022-05-20 High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration Forsgren, Edvin Edlund, Christoffer Oliver, Miniver Barnes, Kalpana Sjögren, Rickard Jackson, Timothy R. PLoS One Research Article Fluorescence microscopy is a core method for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. While many fluorescent microscopy techniques exist, due to its cost-effectiveness and accessibility, widefield fluorescent imaging remains one of the most widely used. To accomplish imaging of 3D samples, conventional widefield fluorescence imaging entails acquiring a sequence of 2D images spaced along the z-dimension, typically called a z-stack. Oftentimes, the first step in an analysis pipeline is to project that 3D volume into a single 2D image because 3D image data can be cumbersome to manage and challenging to analyze and interpret. Furthermore, z-stack acquisition is often time-consuming, which consequently may induce photodamage to the biological sample; these are major barriers for workflows that require high-throughput, such as drug screening. As an alternative to z-stacks, axial sweep acquisition schemes have been proposed to circumvent these drawbacks and offer potential of 100-fold faster image acquisition for 3D-samples compared to z-stack acquisition. Unfortunately, these acquisition techniques generate low-quality 2D z-projected images that require restoration with unwieldy, computationally heavy algorithms before the images can be interrogated. We propose a novel workflow to combine axial z-sweep acquisition with deep learning-based image restoration, ultimately enabling high-throughput and high-quality imaging of complex 3D-samples using 2D projection images. To demonstrate the capabilities of our proposed workflow, we apply it to live-cell imaging of large 3D tumor spheroid cultures and find we can produce high-fidelity images appropriate for quantitative analysis. Therefore, we conclude that combining axial z-sweep image acquisition with deep learning-based image restoration enables high-throughput and high-quality fluorescence imaging of complex 3D biological samples. Public Library of Science 2022-05-19 /pmc/articles/PMC9119453/ /pubmed/35588399 http://dx.doi.org/10.1371/journal.pone.0264241 Text en © 2022 Forsgren et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Forsgren, Edvin Edlund, Christoffer Oliver, Miniver Barnes, Kalpana Sjögren, Rickard Jackson, Timothy R. High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration |
title | High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration |
title_full | High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration |
title_fullStr | High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration |
title_full_unstemmed | High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration |
title_short | High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration |
title_sort | high-throughput widefield fluorescence imaging of 3d samples using deep learning for 2d projection image restoration |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9119453/ https://www.ncbi.nlm.nih.gov/pubmed/35588399 http://dx.doi.org/10.1371/journal.pone.0264241 |
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