A Multi-Biomarker Micronucleus Assay using Imaging Flow Cytometry_ Example template and data files
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ORGANISM(S): Homo sapiens (human)
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PROVIDER: S-BSST1351 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description: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.
| S-EPMC8131758 | biostudies-literature
Project description:The cytokinesis-block micronucleus (CBMN) assay is a well-established technique that can be employed in triage radiation biodosimetry to estimate whole body doses of radiation to potentially exposed individuals through quantitation of the frequency of micronuclei (MN) in binucleated lymphocyte cells (BNCs). The assay has been partially automated using traditional microscope-based methods and most recently has been modified for application on the ImageStream(X) (IS(X) ) imaging flow cytometer. This modification has allowed for a similar number of BNCs to be automatically scored as compared to traditional microscopy in a much shorter time period. However, the MN frequency measured was much lower than both manual and automated slide-based methods of performing the assay. This work describes the optimized analysis template which implements newly developed functions in the IDEAS(®) data analysis software for the IS(X) that enhances specificity for BNCs and increases the frequency of scored MN. A new dose response calibration curve is presented in which the average rate of MN per BNC is of similar magnitude to those presented in the literature using automated CBMN slide scoring methods. In addition, dose estimates were generated for nine irradiated, blinded samples and were found to be within ±0.5 Gy of the delivered dose. Results demonstrate that the improved identification accuracy for MN and BNCs in the IS(X) -based version of the CBMN assay will translate to increased accuracy when estimating unknown radiation doses received by exposed individuals following large-scale radiological or nuclear emergencies. © 2016 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.
| S-EPMC5089661 | biostudies-literature
Project description:The in vitro micronucleus (MN) assay is a well-established test for evaluating genotoxicity and cytotoxicity. The use of manual microscopy to perform the assay can be laborious and often suffers from user subjectivity and interscorer variability. Automated methods including slide-scanning microscopy and conventional flow cytometry have been developed to eliminate scorer bias and improve throughput. However, these methods possess several limitations such as lack of cytoplasmic visualization using slide-scanning microscopy and the inability to visually confirm the legitimacy of MN or storage of image data for re-evaluation using flow cytometry. The ImageStreamX® MK II (ISX) imaging flow cytometer has been demonstrated to overcome all of these limitations. The ISX combines the speed, statistical robustness, and rare event capture capability of conventional flow cytometry with high resolution fluorescent imagery of microscopy and possesses the ability to store all collected image data. This paper details the methodology developed to perform the in vitro MN assay in human lymphoblastoid TK6 cells on the ISX. High resolution images of micronucleated mono- and bi-nucleated cells as well as polynucleated cells can be acquired at a high rate of capture. All images can then be automatically identified, categorized and enumerated in the data analysis software that accompanies the ImageStream, allowing for the scoring of both genotoxicity and cytotoxicity. The results demonstrate that statistically significant increases in MN frequency when compared with solvent controls can be detected at varying levels of cytotoxicity following exposure to well-known aneugens and clastogens. This work demonstrates a fully automated method for performing the in vitro micronucleus assay on the ISX imaging flow cytometry platform. © 2018 The Author. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.
| S-EPMC6174940 | biostudies-literature
Project description: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.
| S-EPMC8380241 | biostudies-literature
Project description:Background: Modern testing paradigms seek to apply human-relevant cell culture models and integrate data from multiple test systems to accurately inform potential hazards and modes of action for chemical toxicology. In genetic toxicology, the use of metabolically competent human hepatocyte cell culture models provides clear advantages over other more commonly used cell lines that require the use of external metabolic activation systems, such as rat liver S9. HepaRG™ cells are metabolically competent cells that express Phase I and II metabolic enzymes and differentiate into mature hepatocyte-like cells, making them ideal for toxicity testing. We assessed the performance of the flow cytometry in vitro micronucleus (MN) test and the TGx-DDI transcriptomic biomarker to detect DNA damage-inducing (DDI) chemicals in human HepaRG™ cells after a 3-day repeat exposure. The biomarker, developed for use in human TK6 cells, is a panel of 64 genes that accurately classifies chemicals as DDI or non-DDI. Herein, the TGx-DDI biomarker was analyzed by Ion AmpliSeq whole transcriptome sequencing to assess its classification accuracy using this more modern gene expression technology as a secondary objective. Methods: HepaRG™ cells were exposed to increasing concentrations of 10 test chemicals (six genotoxic chemicals, including one aneugen, and four non-genotoxic chemicals). Cytotoxicity and genotoxicity were measured using the In Vitro MicroFlow® kit, which was run in parallel with the TGx-DDI biomarker. Results: A concentration-related decrease in relative survival and a concomitant increase in MN frequency were observed for genotoxic chemicals in HepaRG™ cells. All five DDI and five non-DDI agents were correctly classified (as genotoxic/non-genotoxic and DDI/non-DDI) by pairing the test methods. The aneugenic agent (colchicine) yielded the expected positive result in the MN test and negative (non-DDI) result by TGx-DDI. Conclusions: This next generation genotoxicity testing strategy is aligned with the paradigm shift occurring in the field of genetic toxicology. It provides mechanistic insight in a human-relevant cell-model, paired with measurement of a conventional endpoint, to inform the potential for adverse health effects. This work provides support for combining these assays in an integrated test strategy for accurate, higher throughput genetic toxicology testing in this metabolically competent human progenitor cell line.
2020-02-18 | GSE136009 | GEO
Project description:Background:Modern testing paradigms seek to apply human-relevant cell culture models and integrate data from multiple test systems to accurately inform potential hazards and modes of action for chemical toxicology. In genetic toxicology, the use of metabolically competent human hepatocyte cell culture models provides clear advantages over other more commonly used cell lines that require the use of external metabolic activation systems, such as rat liver S9. HepaRG™ cells are metabolically competent cells that express Phase I and II metabolic enzymes and differentiate into mature hepatocyte-like cells, making them ideal for toxicity testing. We assessed the performance of the flow cytometry in vitro micronucleus (MN) test and the TGx-DDI transcriptomic biomarker to detect DNA damage-inducing (DDI) chemicals in human HepaRG™ cells after a 3-day repeat exposure. The biomarker, developed for use in human TK6 cells, is a panel of 64 genes that accurately classifies chemicals as DDI or non-DDI. Herein, the TGx-DDI biomarker was analyzed by Ion AmpliSeq whole transcriptome sequencing to assess its classification accuracy using this more modern gene expression technology as a secondary objective. Methods:HepaRG™ cells were exposed to increasing concentrations of 10 test chemicals (six genotoxic chemicals, including one aneugen, and four non-genotoxic chemicals). Cytotoxicity and genotoxicity were measured using the In Vitro MicroFlow® kit, which was run in parallel with the TGx-DDI biomarker. Results:A concentration-related decrease in relative survival and a concomitant increase in MN frequency were observed for genotoxic chemicals in HepaRG™ cells. All five DDI and five non-DDI agents were correctly classified (as genotoxic/non-genotoxic and DDI/non-DDI) by pairing the test methods. The aneugenic agent (colchicine) yielded the expected positive result in the MN test and negative (non-DDI) result by TGx-DDI. Conclusions:This next generation genotoxicity testing strategy is aligned with the paradigm shift occurring in the field of genetic toxicology. It provides mechanistic insight in a human-relevant cell-model, paired with measurement of a conventional endpoint, to inform the potential for adverse health effects. This work provides support for combining these assays in an integrated test strategy for accurate, higher throughput genetic toxicology testing in this metabolically competent human progenitor cell line.
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Project description:The widespread production and use of nanoparticles calls for faster and more reliable methods to assess their safety. The main aim of this study was to investigate the genotoxicity of three reference TiO2 nanomaterials (NM) within the frame of the FP7-NANoREG project, with a particular focus on testing the applicability of mini-gel comet assay and micronucleus (MN) scoring by flow cytometry. BEAS-2B cells cultured under serum-free conditions were exposed to NM100 (anatase, 50-150nm), NM101 (anatase, 5-8nm) and NM103 (rutile, 20-28nm) for 3, 24 or 48h mainly at concentrations 1-30 μg/ml. In the mini-gel comet assay (eight gels per slide), we included analysis of (i) DNA strand breaks, (ii) oxidised bases (Fpg-sensitive sites) and (iii) light-induced DNA damage due to photocatalytic activity. Furthermore, MN assays were used and we compared the results of more high-throughput MN scoring with flow cytometry to that of cytokinesis-block MN cytome assay scored manually using a microscope. Various methods were used to assess cytotoxic effects and the results showed in general no or low effects at the doses tested. A weak genotoxic effect of the tested TiO2 materials was observed with an induction of oxidised bases for all three materials of which NM100 was the most potent. When the comet slides were briefly exposed to lab light, a clear induction of DNA strand breaks was observed for the anatase materials, but not for the rutile. This highlights the risk of false positives when testing photocatalytically active materials if light is not properly avoided. A slight increase in MN formation for NM103 was observed in the different MN assays at the lower doses tested (1 and 5 μg/ml). We conclude that mini-gel comet assay and MN scoring using flow cytometry successfully can be used to efficiently study cytotoxic and genotoxic properties of nanoparticles.
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Project description:Measured variability of product within Cell and Gene Therapy (CGT) manufacturing arises from numerous sources across pre-analytical to post-analytical phases of testing. Operators are a function of the manufacturing process and are an important source of variability as a result of personal differences impacted by numerous factors. This research uses measurement uncertainty in comparison to Coefficient of Variation to quantify variation of participants when they complete Flow Cytometry data analysis through a 5-step gating sequence. Two study stages captured participants applying gates using their own judgement, and then following a diagrammatical protocol, respectively. Measurement uncertainty was quantified for each participant (and analysis phase) by following Guide to the Expression of Uncertainty in Measurement protocols, combining their standard deviations in quadrature from each gating step in the respective protocols. When participants followed a diagrammatical protocol, variation between participants reduced by 57%, increasing confidence in a more uniform reported cell count percentage. Measurement uncertainty provided greater resolution to the analysis processes, identifying that most variability contributed in the Flow Cytometry gating process is from the very first gate, where isolating target cells from dead or dying cells is required. This work has demonstrated the potential for greater usage of measurement uncertainty within CGT manufacturing scenarios, due to the resolution it provides for root cause analysis and continuous improvement.
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