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Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning

John Wills, Jatin Verma, Benjamin Rees, Danielle Harte, Chelly Haxhiraj, Claire Barnes Orcid Logo, Rachel Barnes, Matthew A. Rodrigues, Minh Doan, Andrew Filby, Rachel E. Hewitt, Cathy Thornton Orcid Logo, James Cronin Orcid Logo, Julia D. Kenny, Ruby Buckley, Anthony Lynch, Anne E. Carpenter, Huw Summers Orcid Logo, George Johnson Orcid Logo, Paul Rees Orcid Logo

Archives of Toxicology, Volume: 95, Issue: 9, Pages: 3101 - 3115

Swansea University Authors: John Wills , Jatin Verma , Benjamin Rees , Danielle Harte , Chelly Haxhiraj , Claire Barnes Orcid Logo, Rachel Barnes , Cathy Thornton Orcid Logo, James Cronin Orcid Logo, Anthony Lynch , Huw Summers Orcid Logo, George Johnson Orcid Logo, Paul Rees Orcid Logo

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Abstract

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual...

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Published in: Archives of Toxicology
ISSN: 0340-5761 1432-0738
Published: Springer Science and Business Media LLC 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa57403
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Abstract: The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25–5.0 μg/mL) and/or carbendazim (0.8–1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the “DeepFlow” neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for ‘mononucleates’, ‘binucleates’, ‘mononucleates with MN’ and ‘binucleates with MN’, respectively. Successful classifications of ‘trinucleates’ (90%) and ‘tetranucleates’ (88%) in addition to ‘other or unscorable’ phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.
Keywords: Compound screening; Genetic toxicology; High throughput; Image analysis; Machine learning; Micronucleus test
College: Swansea University Medical School
Funders: The authors acknowledge the UK Engineering and Physical Sciences Research Council (EP/N013506/1) and UK Biotechnology and Biological Sciences Research Council (BB/P026818/1) for supporting this work. We also thank the Life Science Bridging Fund within the Life Science Research Network Wales (LSBF/R3-007), AgorIP (WEFO), and the National Institutes of Health (R35 GM122547) for providing funding in support of the project
Issue: 9
Start Page: 3101
End Page: 3115