| Title: |
High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration |
| Authors: |
Forsgren, Edvin; Edlund, Christoffer; Oliver, Miniver; Barnes, Kalpana; Sjögren, Rickard; Jackson, Timothy R. |
| Publisher Information: |
Umeå universitet, Kemiska institutionen; Sartorius Corporate Research, Sartorius Stedim Data Analytics AB, Umeå, Sweden; Sartorius BioAnalytics, Essen BioScience, Ltd., Units 2 & 3 The Quadrant, Hertfordshire, Royston, United Kingdom; Public Library of Science |
| Publication Year: |
2022 |
| Collection: |
Umeå University: Publications (DiVA) |
| Subject Terms: |
Computer graphics and computer vision; Datorgrafik och datorseende |
| 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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
PLOS ONE, 2022, 17:5 May; PMID 35588399; ISI:001016382300005 |
| DOI: |
10.1371/journal.pone.0264241 |
| Availability: |
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-203153; https://doi.org/10.1371/journal.pone.0264241 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.4FBC3693 |
| Database: |
BASE |