Journal article 24 views 2 downloads
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, Rachel Barnes, Matthew A. Rodrigues, Minh Doan, Andrew Filby, Rachel E. Hewitt, Cathy Thornton, James Cronin, Julia D. Kenny, Ruby Buckley, Anthony Lynch, Anne E. Carpenter, Huw Summers, George Johnson, Paul Rees
Archives of Toxicology
Swansea University Authors: John Wills, Jatin Verma, Benjamin Rees, Danielle Harte, Chelly Haxhiraj, Claire Barnes, Rachel Barnes, Cathy Thornton, James Cronin, Anthony Lynch, Huw Summers, George Johnson, Paul Rees
PDF | Version of Record
© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International LicenseDownload (7.03MB)
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...
|Published in:||Archives of Toxicology|
Springer Science and Business Media LLC
Check full text
No Tags, Be the first to tag this record!
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.
Compound screening; Genetic toxicology; High throughput; Image analysis; Machine learning; Micronucleus test
Swansea University Medical School