Investigating FlowSight® imaging flow cytometry as a platform to assess chemically induced micronuclei using human lymphoblastoid cells in vitro

A multi-biomarker micronucleus assay using imaging flow cytometry

Genetic toxicity testing assesses the potential of compounds to cause DNA damage. There are many genetic toxicology screening assays designed to assess the DNA damaging potential of chemicals in early drug development aiding the identification of promising drugs that have low-risk potential for causing genetic damage contributing to cancer risk in humans. Despite this, in vitro tests generate a high number of misleading positives, the consequences of which can lead to unnecessary animal testing and/or the abandonment of promising drug candidates. Understanding chemical Mode of Action (MoA) is vital to identifying the true genotoxic potential of substances and, therefore, the risk translation into the clinic. Here we demonstrate a simple, robust protocol for staining fixed, human-lymphoblast p53 proficient TK6 cells with antibodies against ɣH2AX, p53 and pH3S28 along with DRAQ5™ DNA staining that enables analysis of un-lysed cells via microscopy approaches such as imaging flow cytometry. Here, we used the Cytek® Amnis® ImageStream®X Mk II which provides a high-throughput acquisition platform with the sensitivity of flow cytometry and spatial morphological information associated with microscopy. Using the ImageStream manufacturer's software (IDEAS® 6.2), a masking strategy was developed to automatically detect and quantify micronucleus events (MN) and characterise biomarker populations. The gating strategy developed enables the generation of a template capable of automatically batch processing data files quantifying cell-cycle, MN, ɣH2AX, p53 and pH3 populations simultaneously. In this way, we demonstrate how a multiplex system enables DNA damage assessment alongside MN identification using un-lysed cells on the imaging flow cytometry platform. As a proof-of-concept, we use the tool chemicals carbendazim and methyl methanesulphonate (MMS) to demonstrate the assay's ability to correctly identify clastogenic or aneugenic MoAs using the biomarker profiles established.

The Imaging Flow Cytometry-Based Cytokinesis-Block MicroNucleus (CBMN) Assay

The dose of ionizing radiation received by an individual can be determined using biodosimetry methods which measure biomarkers of exposure in tissue samples from that individual. These markers can be expressed in many ways, including DNA damage and repair processes. Following a mass casualty event involving radiological or nuclear material, it is important to rapidly provide this information to medical responders to assist in the medical management of potentially exposed casualties. Traditional methods of biodosimetry rely on microscope analysis, making them time-consuming and labor-intensive. To increase sample throughput following a large-scale radiological mass casualty event, several biodosimetry assays have been adapted for analysis by imaging flow cytometry. This chapter briefly reviews these methods with a focus on the most current methodology to identify and quantify micronuclei in binucleated cells within the cytokinesis-block micronucleus assay using an imaging flow cytometer.

Imaging Flow Cytometry of Multi-Nuclearity

Multi-nuclearity is a common feature for cells in different cancers. Also, analysis of multi-nuclearity in cultured cells is widely used for evaluating the toxicity of different drugs. Multi-nuclear cells in cancer and under drug treatments form from aberrations in cell division and/or cytokinesis. These cells are a hallmark of cancer progression, and the abundance of multi-nucleated cells often correlates with poor prognosis.The use of standard bright field or fluorescent microscopy to analyze multi-nuclearity at the quantitative level is laborious and can suffer from user bias. Automated slide-scanning microscopy can eliminate scorer bias and improve data collection. However, this method has limitations, such as insufficient visibility of multiple nuclei in the cells attached to the substrate at low magnification.Since quantification of multi-nuclear cells using microscopic methods might be difficult, imaging flow cytometry (IFC) is a method of choice for this. We describe the experimental protocol for the preparation of the samples of multi-nucleated cells from the attached cultures and the algorithm for the analysis of these cells by IFC. Images of multi-nucleated cells obtained after mitotic arrest induced by taxol, as well as cells obtained after cytokinesis blockade by cytochalasin D treatment, can be acquired at a maximal resolution of IFC. We suggest two algorithms for the discrimination of single-nucleus and multi-nucleated cells. The advantages and disadvantages of IFC analysis of multi-nuclear cells in comparison with microscopy are discussed.

Anti-schistosomal activities of quinoxaline-containing compounds: From hit identification to lead optimisation

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, EC₅₀ = 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 (EC₅₀ = 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; EC₅₀ = 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.

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Lead compound 22 was identified with EC₅₀ of 0.44 µM and 84.7 nM for schistosomula and adult worms.

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20 analogues of the lead compound 22 were synthesised.

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Compounds 25, 30 and 32 showed the best selectivity profile.

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Compounds 31 and 33 are the most active on three medically important schistosome species.

RNA-Based Analysis Reveals High Diversity of Plant-Associated Active Fungi in the Atmosphere

Fungi are ubiquitous in nature; that is, they are present everywhere on the planet; understanding the active state and functional capacity of airborne microbes associated with health of human, animal, and plant is critical for biosafety management. Here, we firstly and directly proved that there were about 40% active fungi in the air via rRNA amplicon sequencing and imaging flow cytometry simultaneously. Amplicon sequencing analysis showed differences between structures of active and total fungal community; Ascomycota were dominant in the active community, while Basidiomycota have low transcriptional activity across all samples. Notably, plant pathogenic fungi were predominant in the air, and more than 50% were active, including not only several common plant pathogens but also biotrophic fungi (Erysiphe sp. and Microbotryum sp.) and host-specific pathogens, which were generally considered to be inactive after leaving the host. Putative plant pathogens of eight genera were found active across the sampling season, indicating their superior ability to obtain nutrients even in barren nutrient environments. Interestingly, we detected several potentially active unrecorded fungi in China (Diatrype prominens, Septofusidium herbarum, Pseudomicrostroma glucosiphilum, and Uromycladium tepperianum), which suggested that they spread over a long distance by air and may cause diseases under favorable conditions. Our results suggested that maintaining transmission in air is an essential feature of many fungi including plant pathogens regardless of being a biotrophic, hemibiotrophic, or necrotrophic group. Moreover, two potentially active human pathogens and one animal pathogen were captured, which indicated their potential risks. This study provided a new perspective for more comprehensive understanding of airborne fungi, including their multidimensional lifestyle, state, functioning, and potential pathogenic risk. It also laid the foundation for further prediction and management of airborne microbial communities, which would be of interest for public health and agriculture.

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.

Can a digital slide scanner and viewing technique assist the visual scoring for the cytokinesis-block micronucleus cytome assay?

The cytokinesis-block micronucleus cytome (CBMNcyt) assay is a comprehensive method to measure DNA damage, cytostasis and cytotoxicity caused by nutritional, radiation and chemical factors. A slide imaging technique has been identified as a new method to assist with the visual scoring of cells for the CBMNcyt assay. A NanoZoomer S60 Digital Pathology slide scanner was used to view WIL2-NS cells treated with hydrogen peroxide (H2O2) and measure CBMNcyt assay biomarkers using a high-definition desktop computer screen. The H2O2-treated WIL2-NS cells were also scored visually using a standard light microscope, and the two visual scoring methods were compared. Good agreement was found between the scoring methods for all DNA damage indices (micronuclei, nucleoplasmic bridges and nuclear buds) and nuclear division index with correlation R values ranging from 0.438 to 0.789, P < 0.05. Apoptotic and necrotic cell frequency was lower for the NanoZoomer scoring method, but necrotic frequency correlated well with the direct visual microscope method (R = 0.703, P < 0.0001). Considerable advantages of the NanoZoomer scoring method compared to direct visual microscopy includes reduced scoring time, improved ergonomics and a reduction in scorer fatigue. This study indicates that a digital slide scanning and viewing technique may assist with visual scoring for the CBMNcyt assay and provides similar results to conventional direct visual scoring.

The in vitro micronucleus assay using imaging flow cytometry and deep learning

The in vitro micronucleus (MN) assay is a well-established assay for quantification of DNA damage, and is required by regulatory bodies worldwide to screen chemicals for genetic toxicity. The MN assay is performed in two variations: scoring MN in cytokinesis-blocked binucleated cells or directly in unblocked mononucleated cells. Several methods have been developed to score the MN assay, including manual and automated microscopy, and conventional flow cytometry, each with advantages and limitations. Previously, we applied imaging flow cytometry (IFC) using the ImageStream® to develop a rapid and automated MN assay based on high throughput image capture and feature-based image analysis in the IDEAS® software. However, the analysis strategy required rigorous optimization across chemicals and cell lines. To overcome the complexity and rigidity of feature-based image analysis, in this study we used the Amnis® AI software to develop a deep-learning method based on convolutional neural networks to score IFC data in both the cytokinesis-blocked and unblocked versions of the MN assay. We show that the use of the Amnis AI software to score imagery acquired using the ImageStream® compares well to manual microscopy and outperforms IDEAS® feature-based analysis, facilitating full automation of the MN assay.

The 3D reconstructed skin micronucleus assay using imaging flow cytometry and deep learning: A proof-of-principle investigation

The reconstructed skin micronucleus (RSMN) assay was developed in 2006, as an in vitro alternative for genotoxicity evaluation of dermally applied chemicals or products. In the years since, significant progress has been made in the optimization of the assay, including publication of a standard protocol and extensive validation. However, the diverse morphology of skin cells makes cell preparation and scoring of micronuclei (MN) tedious and subjective, thus requiring a high level of technical expertise for evaluation. This ultimately has a negative impact on throughput and the assay would benefit by the development of an automated method which could reduce scoring subjectivity while also improving the robustness of the assay by increasing the number of cells that can be scored. Imaging flow cytometry (IFC) with the ImageStream^®X Mk II can capture high-resolution transmission and fluorescent imagery of cells in suspension. This proof-of-principle study describes protocol modifications that enable such automated measurement in 3D skin cells following exposure to mitomycin C and colchicine. IFC was then used for automated image capture and the Amnis® Artificial Intelligence (AAI) software permitted identification of binucleated (BN) cells with 91% precision. On average, three times as many BN cells from control samples were evaluated using IFC compared to the standard manual analysis. When IFC MNBN cells were visually scored from within the BN cell images, their frequency compared well with manual slide scoring, showing that IFC technology can be applied to the RSMN assay. This method enables faster time to result than microscope-based scoring and the initial studies presented here demonstrate its capability for the detection of statistically significant increases in MNBN frequencies. This work therefore demonstrates the feasibility of combining IFC and AAI to automate scoring for the RSMN assay and to improve its throughput and statistical robustness.