Schistosomiasis is a neglected disease of poverty that is caused by infection with blood fluke species contained within the genus Schistosoma . For the last 40 years, control of schistosomiasis in endemic regions has predominantly been facilitated by administration of a single drug, praziquantel. Due to limitations in this mono-chemotherapeutic approach for sustaining schistosomiasis control into the future, alternative anti-schistosomal compounds are increasingly being sought by the drug discovery community. Herein, we describe a multi-pronged, integrated strategy that led to the identification and further exploration of the quinoxaline core as a promising anti-schistosomal scaffold. Firstly, phenotypic screening of commercially available small molecules resulted in the identification of a moderately active hit compound against Schistosoma mansoni (1, EC 50 = 4.59 μM on schistosomula). Secondary exploration of the chemical space around compound 1 led to the identification of a quinoxaline-core containing, non-genotoxic lead (compound 22). Compound 22 demonstrated substantially improved activities on both intra-mammalian (EC 50 = 0.44 μM, 0.20 μM and 84.7 nM, on schistosomula, juvenile and adult worms, respectively) and intra-molluscan (sporocyst) S. mansoni lifecycle stages. Further medicinal chemistry optimisation of compound 22, resulting in the generation of 20 additional analogues, improved our understanding of the structure-activity relationship and resulted in considerable improvements in both anti-schistosome potency and selectivity (e.g. compound 30; EC 50 = 2.59 nM on adult worms; selectivity index compared to the HepG2 cell line = 348). Some derivatives of compound 22 (e.g. 31 and 33) also demonstrated significant activity against the two other medically important species, Schistosoma haematobium and Schistosoma japonicum . Further optimisation of this class of anti-schistosomal is ongoing and could lead to the development of an urgently needed alternative to praziquantel for assisting in schistosomiasis elimination strategies.

Use of imaging flow cytometry to assess induced DNA damage via the cytokinesis block micronucleus (CBMN) assay has thus far been limited to radiation dosimetry in human lymphocytes using high end, 'ImageStream X' series imaging cytometers. Its potential to enumerate chemically induced DNA damage using in vitro cell lines remains unexplored. In the present manuscript, we investigate the more affordable FlowSight® imaging cytometry platform to assess in vitro micronucleus (MN) induction in the human lymphoblastoid TK6 and metabolically competent MCL-5 cells treated with Methyl Methane Sulfonate (MMS) (0-5 µg/ml), Carbendazim (0-1.6 µg/ml), and Benzo[a]Pyrene (B[a]P) (0-6.3 µg/ml) for a period of 1.5-2 cell-cycles. Cells were fixed, and nuclei and MN were stained using the fluorescent nuclear dye DRAQ5™. Image acquisition was carried out using a 20X objective on a FlowSight® imaging cytometer (Amnis, part of Merck Millipore) equipped with a 488 nm laser. Populations of ∼20000 brightfield cell images, alongside DRAQ5™ stained nuclei/MN were rapidly collected (≤10 min). Single, in-focus cells suitable for scoring were then isolated using the IDEAS® software. An overlay of the brightfield cell outlines and the DRAQ5 nuclear fluorescence was used to facilitate scoring of mono-, bi-, tri-, and tetra-nucleated cells with or without MN events and in context of the cytoplasmic boundary of the parent cell.To establish the potential of the FlowSight® platform, and to establish 'ground truth' cell classification for the supervised machine learning based scoring algorithm that represents the next stage of our project, the captured images were scored manually. Alongside, MN frequencies were also derived using the 'gold standard' light microscopy and manual scoring. A minimum of 3000 bi-nucleated cells were assessed using both approaches. Using the benchmark dose approach, the comparability of genotoxic potency estimations for the different compounds and cell lines was assessed across the two scoring platforms as highly similar. This study therefore provides essential proof-of-concept that FlowSight® imaging cytometry is capable of reproducing the results of 'gold standard' manual scoring by light microscopy. We conclude that, with the right automated scoring algorithm, imaging flow cytometry could revolutionise the reportability and scoring throughput of the CBMN assay.

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.

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 dose 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.

Genetic toxicity testing is the assessment of compounds, and their respective metabolites, potential to cause DNA damage either directly or indirectly. There are many genetic toxicology screening assays designed to assess the DNA damaging potential of chemicals in early drug development to help identify promising drugs that have a low risk potential of causing damage leading to cancer in humans. Despite this, in vitro tests generate a high number of miss-leading positives, the consequences of which lead to unnecessary animal testing and abandoning promising drug candidates. Understanding chemical Mode of Action (MoA) is vital in identifying true genotoxic potential of substances. Here I demonstrate a simple robust protocol for optimised staining of fixed human lymphoblast P53 proficient TK6 cells with the antibodies; Anti-ɣH2AX, Anti-P53 and Ant-pH3S28 along with DRAQ5™ DNA staining in a whole cell multiplex system that is suitable for analysis via microscopy or imaging flow cytometry. Use of the Amnis FlowSight® and ImageStream X Mark II® platform provided a high content high throughput acquisition platform with the sensitivity of flow cytometry and accuracy of image analysis. Using both optimal and suboptimal lasers for fluorophore excitation demonstrated that a multiplex system for DNA damage assessment including MN was possible in un-lysed cells. IDEAS 6.2 template generation allowed for batch processing of data samples extracting the following metrics: Cell Cycle, Micronucleus (MN), ɣH2AX, P53, PH3, G1 ɣH2AX, G1 P53, S ɣH2AX, S P53, G2 ɣH2AX, G2/M P53, Prophase, Metaphase, Anaphase and Abnormal mitosis. Furthermore, high content nature of the platform using imagery allows for identification of rare cellular event assessment particularly preliminary data on biomarker signal found within MN. The system found differences in the biomarker metric responses between the chemicals; MMS, Carbendazim, ARA-C, Vinblastine, Etoposide and Crizotinib suggesting potential for chemical MoA elucidation.