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Arch Toxicol DOI:10.1007/s00204-021-03113-0

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

Publication TypeJournal Article
Year of Publication2021
AuthorsWills, JW, Verma, JR, Rees, BJ, Harte, DSG, Haxhiraj, Q, Barnes, CM, Barnes, R, Rodrigues, MA, Doan, M, Filby, A, Hewitt, RE, Thornton, CA, Cronin, JG, Kenny, JD, Buckley, R, Lynch, AM, Carpenter, AE, Summers, HD, Johnson, GE, Rees, P
JournalArch Toxicol
Date Published2021 Sep

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.


Alternate JournalArch Toxicol
PubMed ID34245348
Grant ListEP/N013506/1 / / Engineering and Physical Sciences Research Council /
LSBF/R3-007 / / Life Science Research Network Wales /
R35 GM122547 / NH / NIH HHS / United States
R35 GM122547 / GM / NIGMS NIH HHS / United States
BB/P026818/1 / BB_ / Biotechnology and Biological Sciences Research Council / United Kingdom
R35 GM122547 / NH / NIH HHS / United States