Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.
John W WillsJatin R VermaBenjamin J ReesDanielle S G HarteQiellor HaxhirajClaire M BarnesRachel BarnesMatthew A RodriguesMinh DoanAndrew FilbyRachel E HewittCatherine A ThorntonJames G CroninJulia D KennyRuby BuckleyAnthony M LynchAnne E CarpenterHuw D SummersGeorge E JohnsonPaul ReesPublished in: Archives of toxicology (2021)
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.
Keyphrases
- deep learning
- flow cytometry
- artificial intelligence
- high throughput
- convolutional neural network
- induced apoptosis
- big data
- dna damage
- machine learning
- endothelial cells
- neural network
- high resolution
- cell cycle arrest
- oxidative stress
- electronic health record
- induced pluripotent stem cells
- metabolic syndrome
- cardiovascular disease
- transcription factor
- air pollution
- physical activity
- risk assessment
- ms ms
- mass spectrometry
- cell proliferation
- climate change
- signaling pathway
- liquid chromatography tandem mass spectrometry
- single cell
- pi k akt
- high performance liquid chromatography
- type diabetes