A rapid and fully automated in vitro micronucleus assay using imaging flow cytometry and convolutional neural network analysis
| Autor: | Maria Gracia Garcia Mendoza, Alex Sutton, Raymond Kong, Matthew Rodrigues, Haley Pugsley |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | The Journal of Immunology. 208:172.02-172.02 |
| ISSN: | 1550-6606 0022-1767 |
| DOI: | 10.4049/jimmunol.208.supp.172.02 |
| Popis: | Micronuclei (MN) originate from whole chromosomes or chromosome fragments that lag behind during cell division and fail to be incorporated into one of the two main nuclei. As a result, scoring MN using the well-established in vitro micronucleus assay evaluates the ability of chemicals or other agents to induce DNA damage. This technique is typically performed by manual microscopy, which can be time-consuming and prone to variability. Additionally, automated methods lack cytoplasmic visualization when using slide-scanning microscopy, and conventional flow cytometry doesn’t provide visual confirmation of MN. The ImageStream®X Mk II (ISX) imaging flow cytometer combines the high-resolution imagery of microscopy with conventional flow cytometry’s speed and statistical robustness in a single system. Previously, we developed a rapid and automated MN assay based on high-throughput image capture and feature-based image analysis using IDEAS® Software. However, the feature-based analysis was not readily applicable to multiple cell lines and chemicals, so we developed a deep learning method based on convolutional neural networks to score imaging flow cytometry data in both the cytokinesis-blocked and unblocked versions of the MN assay using Amnis® AI Software. Our current study validates our previously established assay and analyses using three different chemicals (Mitomycin C, Cyclophosphamide, and Eugenol) and three different cell lines (TK6, L5178Y, CHO-K1). Here, we demonstrate how using Amnis AI to score imagery acquired on the ISX provides a rapid and fully automated in vitro MN assay with improved accuracy, reproducibility, and time-to-results in toxicity and biodosimetry applications across multiple cell lines. |
| Databáze: | OpenAIRE |
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