Abstract
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.
Highlights
The in vitro micronucleus (MN) assay is used worldwide to assess the ability of various chemicals or other agents to induce DNA damage in the fields of population biomonitoring and radiation biodosimetry[1,2,3]
MN originate from whole chromosomes or chromosome fragments that fail to be incorporated into the main nucleus following nuclear division[5] and MN frequency can be used as an endpoint to quantify DNA damage
This paper presents the use of a deep learning software package to automate analysis of the MN assay
Summary
The in vitro micronucleus (MN) assay is used worldwide to assess the ability of various chemicals or other agents to induce DNA damage in the fields of population biomonitoring and radiation biodosimetry[1,2,3]. The workflow used to has been the requirement for computer scientists to perform optimization and validation, making them relatively inaccessible to researchers lacking expertise in those areas To address this limitation, Amnis® AI (AAI) software has been developed to allow researchers to directly develop, train, and validate CNN models for IFC data through the use of a convenient graphical user interface.
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