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

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 dir...

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Autores principales: Matthew A. Rodrigues, Christine E. Probst, Artiom Zayats, Bryan Davidson, Michael Riedel, Yang Li, Vidya Venkatachalam
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Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/de6553bcb0b54e6eabcf48cd6ad699a3
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spelling oai:doaj.org-article:de6553bcb0b54e6eabcf48cd6ad699a32021-12-02T15:53:02ZThe in vitro micronucleus assay using imaging flow cytometry and deep learning10.1038/s41540-021-00179-52056-7189https://doaj.org/article/de6553bcb0b54e6eabcf48cd6ad699a32021-05-01T00:00:00Zhttps://doi.org/10.1038/s41540-021-00179-5https://doaj.org/toc/2056-7189Abstract 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.Matthew A. RodriguesChristine E. ProbstArtiom ZayatsBryan DavidsonMichael RiedelYang LiVidya VenkatachalamNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-12 (2021)
institution DOAJ
collection DOAJ
language EN
topic Biology (General)
QH301-705.5
spellingShingle Biology (General)
QH301-705.5
Matthew A. Rodrigues
Christine E. Probst
Artiom Zayats
Bryan Davidson
Michael Riedel
Yang Li
Vidya Venkatachalam
The in vitro micronucleus assay using imaging flow cytometry and deep learning
description 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.
format article
author Matthew A. Rodrigues
Christine E. Probst
Artiom Zayats
Bryan Davidson
Michael Riedel
Yang Li
Vidya Venkatachalam
author_facet Matthew A. Rodrigues
Christine E. Probst
Artiom Zayats
Bryan Davidson
Michael Riedel
Yang Li
Vidya Venkatachalam
author_sort Matthew A. Rodrigues
title The in vitro micronucleus assay using imaging flow cytometry and deep learning
title_short The in vitro micronucleus assay using imaging flow cytometry and deep learning
title_full The in vitro micronucleus assay using imaging flow cytometry and deep learning
title_fullStr The in vitro micronucleus assay using imaging flow cytometry and deep learning
title_full_unstemmed The in vitro micronucleus assay using imaging flow cytometry and deep learning
title_sort in vitro micronucleus assay using imaging flow cytometry and deep learning
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/de6553bcb0b54e6eabcf48cd6ad699a3
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