Applying a deep neural network based approach to automating the Micronucleus (MN) assay

The Micronucleus (MN) Assay is a test mandated for use in genetic toxicology testing by regulatory bodies such as the Food and Drug administration (FDA). An increased quantity of MN is an indication of chromosomal damage which can be characterised into chromosomal breakage (caused by a clastogen) and chromosomal loss (caused by an aneugen). By comparing a dose response, estimates can be made into the potency of the chemical. Historically the cell scoring procedure takes place through the ‘gold standard’ of manual scoring by light microscopy following staining. However, despite being classed the gold standard, this method is laborious and subjective, with archiving of results not a possibility. This leads to the need to develop a new technique to streamline the process, whilst still maintaining accuracy. The result is the creation of a ground truth based deep learning algorithm. By using imaging flow cytometry to carry out the MN assay, a ground truth was created, consisting of different cellular types, including MN. By scoring these images manually by eye, a ground truth of images to teach the deep-learning algorithm is created. By applying a deep neural network, the algorithm uses multiple layers to differentiate information, mimicking the way neurons work in the brain. This approach allows for differentiation between different cellular types based on the ground truth images scored. By assessing more images, the accuracy is further increased. This is advantageous as a MN count is generated directly after processing the imaging flow cytometry file. This streamlines the process completely whilst maintaining accuracy. Also, by using three different laboratory datasets in the production of the ground truth, application was shown to be accurate for cross-laboratory use, a novelty in this research setting. This allows for the existing ground truth to be used for future MN scoring, allowing for the MN assay to be fully automated

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

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