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

John W. Wills, Jatin R. Verma, Benjamin J. Rees, Danielle S.G. Harte, Qiellor Haxhiraj, Claire M. Barnes, Rachel Barnes, Matthew A. Rodrigues, Minh Doan, Andrew Filby, Rachel E. Hewitt, Catherine A. Thornton, James G. Cronin, Julia D. Kenny, Ruby Buckley, Anthony M. Lynch, Anne E. Carpenter, Huw D. Summers, George E. Johnson, Paul Rees

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)3101-3115
Number of pages15
JournalArchives of Toxicology
Volume95
Issue number9
DOIs
StatePublished - Sep 2021

Keywords

  • Compound screening
  • Genetic toxicology
  • High throughput
  • Image analysis
  • Machine learning
  • Micronucleus test

ASJC Scopus subject areas

  • Toxicology
  • Health, Toxicology and Mutagenesis

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