Name: automation of the micronucleus assay using imaging flow cytometry and artificial intelligence
Uploaded: 2023-01-27T13:00:00
Description: Watch this Scientific Journal Video about Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence at JoVE.com

1. Data acquisition using imaging flow cytometry
NOTE: Refer to Rodrigues et al.16 with the following modifications, noting that the acquisition regions using IFC may need to be modified for optimal image capture:
<ol>
	<li>For the non-Cyt-B method, perform a cell count using a commercially available cell counter following the manufacturer&#39;s instructions (see Table of Materials) on each culture immediately before culture and immed...

<ol>
	<li>Fenech, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=HUMN+project+initiative+and+review+of+validation,+quality+control+and+prospects+for+further+development+of+automated+micronucleus+assays+using+image+cytometry+systems.">HUMN project initiative and review of validation, quality control and prospects for further development of automated micronucleus assays using image cytometry systems.</a> International Journal of Hygiene and Environmental Health. 216 (5), 541-552 (2013).</li><li>OECD. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Test+No.+487:+In+Vitro+Mammalian+Cell+Micronucleus+Test.+Section+4.">Test No. 487: In Vitro Mammalian Cell Micronucleus Test. Section 4.</a> OECD Guidelines for the Testing of Chemicals. , (2016).</li><li>Fenech, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+in+vitro+micronucleus+technique.">The in vitro micronucleus technique.</a> Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis. 455 (1), 81-95 (2000).</li><li>Bonassi, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+increased+micronucleus+frequency+in+peripheral+blood+lymphocytes+predicts+the+risk+of+cancer+in+humans.">An increased micronucleus frequency in peripheral blood lymphocytes predicts the risk of cancer in humans.</a> Carcinogenesis. 28 (3), 625-631 (2007).</li><li>Fenech, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytokinesis-block+micronucleus+cytome+assay.">Cytokinesis-block micronucleus cytome assay.</a> Nature Protocols. 2 (5), 1084-1104 (2007).</li><li>Fenech, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Commentary+on+the+SFTG+international+collaborative+study+on+the+in+vitro+micronucleus+test:+To+Cyt-B+or+not+to+Cyt-B.">Commentary on the SFTG international collaborative study on the in vitro micronucleus test: To Cyt-B or not to Cyt-B.</a> Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis. 607 (1), 9-12 (2006).</li><li>Seager, A. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recommendations,+evaluation+and+validation+of+a+semi-automated,+fluorescent-based+scoring+protocol+for+micronucleus+testing+in+human+cells.">Recommendations, evaluation and validation of a semi-automated, fluorescent-based scoring protocol for micronucleus testing in human cells.</a> Mutagenesis. 29 (3), 155-164 (2014).</li><li>Rossnerova, A., Spatova, M., Schunck, C., Sram, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+scoring+of+lymphocyte+micronuclei+by+the+MetaSystems+Metafer+image+cytometry+system+and+its+application+in+studies+of+human+mutagen+sensitivity+and+biodosimetry+of+genotoxin+exposure.">Automated scoring of lymphocyte micronuclei by the MetaSystems Metafer image cytometry system and its application in studies of human mutagen sensitivity and biodosimetry of genotoxin exposure.</a> Mutagenesis. 26 (1), 169-175 (2011).</li><li>Bryce, S. M., Bemis, J. C., Avlasevich, S. L., Dertinger, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vitro+micronucleus+assay+scored+by+flow+cytometry+provides+a+comprehensive+evaluation+of+cytogenetic+damage+and+cytotoxicity.">In vitro micronucleus assay scored by flow cytometry provides a comprehensive evaluation of cytogenetic damage and cytotoxicity.</a> Mutation Research/Genetic Toxicology and Environmental Mutagenesis. 630 (1), 78-91 (2007).</li><li>Avlasevich, S. L., Bryce, S. M., Cairns, S. E., Dertinger, S. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vitro+micronucleus+scoring+by+flow+cytometry:+Differential+staining+of+micronuclei+versus+apoptotic+and+necrotic+chromatin+enhances+assay+reliability.">In vitro micronucleus scoring by flow cytometry: Differential staining of micronuclei versus apoptotic and necrotic chromatin enhances assay reliability.</a> Environmental and Molecular Mutagenesis. 47 (1), 56-66 (2006).</li><li>Basiji, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Principles+of+Amnis+imaging+flow+cytometry.">Principles of Amnis imaging flow cytometry.</a> Methods in Molecular Biology. 1389, 13-21 (2016).</li><li>Rodrigues, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automation+of+the+in+vitro+micronucleus+assay+using+the+Imagestream&#174;+imaging+flow+cytometer.">Automation of the in vitro micronucleus assay using the Imagestream&#174; imaging flow cytometer.</a> Cytometry Part A. 93 (7), 706-726 (2018).</li><li>Verma, J. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Investigating+FlowSight&#174;+imaging+flow+cytometry+as+a+platform+to+assess+chemically+induced+micronuclei+using+human+lymphoblastoid+cells+in+vitro.">Investigating FlowSight&#174; imaging flow cytometry as a platform to assess chemically induced micronuclei using human lymphoblastoid cells in vitro.</a> Mutagenesis. 33 (4), 283-289 (2018).</li><li>Wills, J. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inter-laboratory+automation+of+the+in+vitro+micronucleus+assay+using+imaging+flow+cytometry+and+deep+learning.">Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.</a> Archives of Toxicology. 95 (9), 3101-3115 (2021).</li><li>Rodrigues, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+in+vitro+micronucleus+assay+using+imaging+flow+cytometry+and+deep+learning.">The in vitro micronucleus assay using imaging flow cytometry and deep learning.</a> Npj Systems Biology and Applications. 7 (1), 20 (2021).</li><li>Rodrigues, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+automated+method+to+perform+the+in+vitro+micronucleus+assay+using+multispectral+imaging+flow+cytometry.">An automated method to perform the in vitro micronucleus assay using multispectral imaging flow cytometry.</a> Journal of Visualized Experiments. (147), e59324 (2019).</li><li>Lovell, D. 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The work presented here describes the use of deep learning algorithms to automate the scoring of the MN assay. Several recent publications have shown that intuitive, interactive tools allow the creation of deep learning models to analyze image data without the need for in-depth computational knowledge18,19. The protocol described in this work using a user interface-driven software package has been designed to work well with very large data files and permit the cr...

The micronucleus (MN) assay is fundamental in genetic toxicology to evaluate DNA damage in the development of cosmetics, pharmaceuticals, and chemicals for human use1,2,3,4. Micronuclei are formed from whole chromosomes or chromosome fragments that do not incorporate into the nucleus following division and condense into small, circular bodies separate from the nucleus. Thus, MN can be used as an endpoint to quantify DNA damage in genotoxicity testing1.
The preferred method for quantifying MN is within once-divided binucleated cells (BNCs) by blocking division using Cytochalasin-B (Cyt-B). In this version of the assay, cytotoxicity is also assessed by scoring mononucleated (MONO) and polynucleated (POLY) cells. The assay can also be performed by scoring MN in unblocked MONO cells, which is faster and easier to score, with cytotoxicity being assessed using pre- and post-exposure cell counts to assess proliferation5,6.
Physical scoring of the assay has historically been performed through manual microscopy, since this permits visual confirmation of all key events. However, manual microscopy is challenging and subjective1. Thus, automated techniques have been developed, including microscope slide scanning and flow cytometry, each with their own advantages and limitations. While slide-scanning methods allow key events to be visualized, slides must be created at optimal cell density, which can be difficult to achieve. Additionally, this technique often lacks cytoplasmic visualization, which can compromise the scoring of MONO and POLY cells7,8. While flow cytometry offers high-throughput data capture, the cells must be lysed, thus not permitting the use of the Cyt-B form of the assay. Additionally, as a non-imaging technique, conventional flow cytometry does not provide visual validation of key events9,10.
Therefore, imaging flow cytometry (IFC) has been investigated to perform the MN assay. The ImageStreamX Mk II combines the speed and statistical robustness of conventional flow cytometry with the high-resolution imaging capabilities of microscopy in a single system11. It has been shown that by using IFC, high-resolution imagery of all key events can be captured and automatically scored using feature-based12,13 or artificial intelligence (AI) techniques14,15. By using IFC to perform the MN assay, the automatic scoring of many more cells compared to microscopy in a shorter amount of time is achievable.
This work deviates from a previously described image analysis workflow16 and discusses all steps required to develop and train a Random Forest (RF) and/or&#160;convolutional neural network (CNN) model using the Amnis AI software (henceforth referred to as &#34;AI software&#34;). All necessary steps are described, including populating ground truth data using AI-assisted tagging tools, interpretation of model training results, and application of the model to classify additional data, permitting calculation of genotoxicity and cytotoxicity15.

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	<tbody>
		<tr>
			<td>15 mL centrifuge tube</td>
			<td>Falcon</td>
			<td>352096</td>
			<td></td>
		</tr>
		<tr>
			<td>Cleanser - Coulter Clenz&#160;</td>
			<td>Beckman Coulter</td>
			<td>8546931</td>
			<td>Fill container with 200 mL of Cleanser.&#160; https://www.beckmancoulter.com/wsrportal/page/itemDetails?itemNumber=8546931#2/10//0/25/ 
			1/0/asc/2/8546931///0/1//0/</td>
		</tr>
		<tr>
			<td>Colchicine</td>
			<td>MilliporeSigma</td>
			<td>64-86-8</td>
			<td></td>
		</tr>
		<tr>
			<td>Corning bottle-top vacuum filter&#160;</td>
			<td>MilliporeSigma</td>
			<td>CLS430769</td>
			<td>0.22 &#181;m filter, 500 mL bottle</td>
		</tr>
		<tr>
			<td>Cytochalasin B</td>
			<td>MilliporeSigma</td>
			<td>14930-96-2</td>
			<td>5 mg bottle</td>
		</tr>
		<tr>
			<td>Debubbler - 70% Isopropanol</td>
			<td>MilliporeSigma</td>
			<td>1.3704</td>
			<td>Fill container with 200 mL of Debubbler.&#160; http://www.emdmillipore.com/US/en/product/2-Propanol-70%25-%28V%2FV%29-0.1-%C2%B5m-filtred,MDA_CHEM-137040?ReferrerURL=https%3A%2F%2Fwww.google.com%2F</td>
		</tr>
		<tr>
			<td>Dimethyl Sulfoxide (DMSO)</td>
			<td>MilliporeSigma</td>
			<td>67-68-5</td>
			<td></td>
		</tr>
		<tr>
			<td>Dulbecco&#39;s Phosphate Buffered Saline 1X</td>
			<td>EMD Millipore</td>
			<td>BSS-1006-B</td>
			<td>PBS Ca++MG++ Free&#160;</td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum</td>
			<td>HyClone</td>
			<td>SH30071.03</td>
			<td></td>
		</tr>
		<tr>
			<td>Formaldehyde, 10%, methanol free, Ultra Pure</td>
			<td>Polysciences, Inc.</td>
			<td>04018</td>
			<td>This is what is used for the 4% and 1% Formalin. CAUTION: Formalin/Formaldehyde toxic by inhalation and if swallowed.&#160; Irritating to the eyes, respiratory systems and skin.&#160; May cause sensitization by inhalation or skin contact. Risk of serious damage to eyes.&#160; Potential cancer hazard.&#160; http://www.polysciences.com/default/catalog-products/life-sciences/histology-microscopy/fixatives/formaldehydes/formaldehyde-10-methanol-free-pure/</td>
		</tr>
		<tr>
			<td>Guava Muse Cell Analyzer</td>
			<td>Luminex</td>
			<td>0500-3115</td>
			<td>A standard configuration Guava Muse Cell Analyzer was used.</td>
		</tr>
		<tr>
			<td>Hoechst 33342</td>
			<td>Thermo Fisher</td>
			<td>H3570</td>
			<td>10 mg/mL solution</td>
		</tr>
		<tr>
			<td>Mannitol</td>
			<td>MilliporeSigma</td>
			<td>69-65-8</td>
			<td></td>
		</tr>
		<tr>
			<td>MEM Non-Essential Amino Acids 100X</td>
			<td>HyClone</td>
			<td>SH30238.01</td>
			<td></td>
		</tr>
		<tr>
			<td>MIFC - ImageStreamX Mark II</td>
			<td>Luminex, a DiaSorin company</td>
			<td>100220</td>
			<td>A 2 camera ImageStreamX Mark II eqiped with the 405 nm, 488 nm, and 642 nm lasers was used.</td>
		</tr>
		<tr>
			<td>MIFC analysis software - IDEAS</td>
			<td>Luminex, a DiaSorin company</td>
			<td>100220</td>
			<td>&#34;Image analysis sofware&#34; 
			The companion software to the MIFC (ImageStreamX MKII)</td>
		</tr>
		<tr>
			<td>MIFC software - INSPIRE</td>
			<td>Luminex, a DiaSorin company</td>
			<td>100220</td>
			<td>&#34;Image acquisition software&#34; 
			This is the software that runs the MIFC (ImageStreamX MKII)</td>
		</tr>
		<tr>
			<td>Amnis AI software</td>
			<td>Luminex, a DiaSorin company</td>
			<td>100221</td>
			<td>&#34;AI software&#34; 
			This is the software that permits the creation of artificial intelligence models to analyze data</td>
		</tr>
		<tr>
			<td>Mitomycin C</td>
			<td>MilliporeSigma</td>
			<td>50-07-7</td>
			<td></td>
		</tr>
		<tr>
			<td>NEAA Mixture 100x</td>
			<td>Lonza BioWhittaker</td>
			<td>13-114E</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicllin/Streptomycin/Glutamine solution 100X</td>
			<td>Gibco</td>
			<td>15070063</td>
			<td></td>
		</tr>
		<tr>
			<td>Potassium Chloride (KCl)</td>
			<td>MilliporeSigma</td>
			<td>P9541</td>
			<td></td>
		</tr>
		<tr>
			<td>Rinse - Ultrapure water or deionized water</td>
			<td>NA</td>
			<td>NA</td>
			<td>Use any ultrapure water or deionized water.&#160; Fill container with 900 mL of Rinse.</td>
		</tr>
		<tr>
			<td>RNase</td>
			<td>MilliporeSigma</td>
			<td>9001-99-4</td>
			<td></td>
		</tr>
		<tr>
			<td>RPMI-1640 Medium 1x</td>
			<td>HyClone</td>
			<td>SH30027.01</td>
			<td></td>
		</tr>
		<tr>
			<td>Sheath - PBS</td>
			<td>MilliporeSigma</td>
			<td>BSS-1006-B</td>
			<td>This is the same as Dulbecco&#39;s Phosphate Buffered Saline 1x&#160; Ca++MG++ free.&#160; Fill container with 900 mL of Sheath.</td>
		</tr>
		<tr>
			<td>Sterile water</td>
			<td>HyClone</td>
			<td>SH30529.01</td>
			<td></td>
		</tr>
		<tr>
			<td>Sterilizer - 0.4%&#8211;0.7% Hypochlorite</td>
			<td>VWR</td>
			<td>JT9416-1</td>
			<td>This is assentually 10% Clorox bleach that can be made by deluting Clorox bleach with water.&#160; Fill container with 200 mL of Sterilzer.</td>
		</tr>
		<tr>
			<td>T25 flask</td>
			<td>Falcon</td>
			<td>353109</td>
			<td></td>
		</tr>
		<tr>
			<td>T75 flask</td>
			<td>Falcon</td>
			<td>353136</td>
			<td></td>
		</tr>
		<tr>
			<td>TK6 cells</td>
			<td>MilliporeSigma</td>
			<td>95111735</td>
			<td></td>
		</tr>
	</tbody>
</table>

automation of the micronucleus assay using imaging flow cytometry and artificial intelligence

The micronucleus (MN) assay is used worldwide by regulatory bodies to evaluate chemicals for genetic toxicity. The assay can be performed in two ways: by scoring MN in once-divided, cytokinesis-blocked binucleated cells or fully divided mononucleated cells. Historically, light microscopy has been the gold standard method to score the assay, but it is laborious and subjective. Flow cytometry has been used in recent years to score the assay, but is limited by the inability to visually confirm key aspects of cellular imagery. Imaging flow cytometry (IFC) combines high-throughput image capture and automated image analysis, and has been successfully applied to rapidly acquire imagery of and score all key events in the MN assay. Recently, it has been demonstrated that artificial intelligence (AI) methods based on convolutional neural networks can be used to score MN assay data acquired by IFC. This paper describes all steps to use AI software to create a deep learning model to score all key events and to apply this model to automatically score additional data. Results from the AI deep learning model compare well to manual microscopy, therefore enabling fully automated scoring of the MN assay by combining IFC and AI.

Figure 1&#160;shows the workflow for using the AI software to create a model for the MN assay. The user loads the desired .daf files into the AI software, then assigns objects to the ground truth model classes using the AI-assisted cluster (Figure 2) and predict (Figure 3) tagging algorithms. Once all ground truth model classes have been populated with sufficient objects, the model can be trained using the RF or CNN algorithms. Foll...

Watch this Scientific Journal Video about Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence at JoVE.com

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

The micronucleus (MN) assay is a well-established test for quantifying DNA damage. However, scoring the assay using conventional techniques such as manual microscopy or feature-based image analysis is laborious and challenging. This paper describes the methodology to develop an artificial intelligence model to score the MN assay using imaging flow cytometry data.

The micronucleus (MN) assay is used worldwide by regulatory bodies to evaluate chemicals for genetic toxicity. The assay can be performed in two ways: by ...

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