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High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.ORCID-id: 0000-0002-1898-4453
Sartorius Corporate Research, Sartorius Stedim Data Analytics AB, Umeå, Sweden.
Sartorius BioAnalytics, Essen BioScience, Ltd., Units 2 & 3 The Quadrant, Hertfordshire, Royston, United Kingdom.
Sartorius BioAnalytics, Essen BioScience, Ltd., Units 2 & 3 The Quadrant, Hertfordshire, Royston, United Kingdom.
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2022 (engelsk)Inngår i: PLOS ONE, E-ISSN 1932-6203, Vol. 17, nr 5 May, artikkel-id e0264241Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

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Public Library of Science , 2022. Vol. 17, nr 5 May, artikkel-id e0264241
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URN: urn:nbn:se:umu:diva-203153DOI: 10.1371/journal.pone.0264241ISI: 001016382300005PubMedID: 35588399Scopus ID: 2-s2.0-85130357196OAI: oai:DiVA.org:umu-203153DiVA, id: diva2:1727440
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eSSENCE - An eScience CollaborationTilgjengelig fra: 2023-01-16 Laget: 2023-01-16 Sist oppdatert: 2023-09-05bibliografisk kontrollert

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