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>
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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. P., 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=Analysis+of+negative+historical+control+group+data+from+the+in+vitro+micronucleus+assay+using+TK6+cells.">Analysis of negative historical control group data from the in vitro micronucleus assay using TK6 cells.</a> Mutation Research/Genetic Toxicology and Environmental Mutagenesis. 825, 40-50 (2018).</li><li>Berg, 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=ilastik:+interactive+machine+learning+for+(bio)image+analysis.">ilastik: interactive machine learning for (bio)image analysis.</a> Nature Methods. 16 (12), 1226-1232 (2019).</li><li>Hennig, H., 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+open-source+solution+for+advanced+imaging+flow+cytometry+data+analysis+using+machine+learning.">An open-source solution for advanced imaging flow cytometry data analysis using machine learning.</a> Methods. 112, 201-210 (2017).</li></ol>

The authors are employed by Luminex Corporation, a DiaSorin Company, the manufacturer of the ImageStream imaging flow cytometer and the Amnis AI software used in this work.

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.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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 ...

Performing the micronucleus assay using imaging flow cytometry overcomes many limitations of traditional methods, including low throughput, score variability, and lack of visual confirmation of events. The main advantage of this technique is that all key events can be acquired using imaging flow cytometry and analyzed using artificial intelligence. This method has a potential to be used in large scale screening of chemicals and other compounds to test for toxicity at higher throughput than is currently available.When building a new AI model, the main challenge is obtaining sufficient images of cells with micronuclei, so it is important to use the image tagging algorithms. To begin, launch the Artificial Intelligence or AI software. Under Experiment Type, click the radio button beside Train to start a training experiment for building the Convolutional Neural Network or CNN model, and click on Next.On the Class Names, click on Add. In the pop-up window, type Mononucleated, and click on OK to add the mononucleated class to the list of class names. Repeat this process to add the other class names, such as Mononucleated with MN, Binucleated, Binucleated with MN, Polynucleated, and Irregular morphology.Under Select Files, click on Add Files, and browse for the desired files to be added to the AI software, to build the ground truth data. Next on the Select Base Population screen, locate the Non-apoptotic population from the population hierarchy. Then right click on the Non-apoptotic population, choose Select All Matching Populations, and click on Next.To assign a tagged truth population of mononucleated cells with micronucleus, click on the Mononucleated with MN class under Model Classes on the left, and then click on the appropriate tagged truth population on the right. Once all appropriate truth populations are assigned, click on Next. On the Select Channels screen, ensure that the appropriate channels for the experiment are chosen.Here, choose Bright Field or BF as channel one, herds for DNA as channel seven, and then click Next. Finally, on the Confirmation screen, click on Create Experiment. Click on Tagging to launch the tagging tool interface.Then click on the zoom tools to crop the images for easier viewing. And click on the slider bar to adjust the image size and decide the number of images to be shown in the gallery. Click on the Display Setting option and choose min-max, which provides the best contrast image for identifying all key events.Next, click on Setup Gallery Display to change the color of the DNA image to yellow or white, which will improve the visualization of small objects. Click on Cluster to run the algorithm to group images with similar morphology together. Once clustering is complete, click on the individual clusters and begin assigning images to appropriate model classes.After a minimum of 25 objects are assigned to each model class, the predict algorithm becomes available. Click on Predict. Once the predict algorithm has completed running, add objects from the predicted classes to the appropriate model classes.Once a minimum of 100 objects are assigned to each model class, click on the Training tab at the top of the screen, and then click on the Train button. Once model training is complete, click on View Results to assess model accuracy. Once the model training is complete, click the menu button to define a new experiment.Under Experiment Type, click the radio button beside Classify to start a classification experiment, and click Next. Click on the model to be used for classification, then click on Next. On the Select Files screen, click on Add Files.Browse for the files to be classified by the CNN model, and then click on Next. Next, on the Select Base Population screen, click on the checkbox next to the Non-apoptotic population in one of the loaded files. Right click on the Non-apoptotic population, click on Select All Matching Populations to select this population from all loaded files, and then click Next.On the Select Channels screen, verify channel one is selected for the Bright Field, and channel seven is selected for the DNA stain, and click Next. Finally, on the confirmation screen, click on Create Experiment. The AI software loads the selected model and all images from the chosen data files.After loading, click on Finish. Next, click on Classify to launch the classification screen. And use the check boxes to choose to use Random Forest or RF, and CNN.Then click on the Classify button. This begins the process of using the RF and CNN models to classify additional data and identify all objects that belong in the specified model classes. Once the classification is complete, click on View Results.Click the Update DAFs button to bring up the update DAFs with classification results window, and then click on OK to update the DAF files. To generate the report, on the results screen, click on Generate Report. If an individual report for each input DAF is required, select the checkbox beside Create report for each input DAF.Otherwise, just click on OK to obtain the reports. The AI assisted cluster algorithm groups similar objects within a segment together, according to the morphology of both unclassified objects and the objects that have been assigned to the ground truth model classes. Clusters containing mononucleated cells fall on one side of the object map, while the multinucleated cells are on the opposite side.Binucleated cell clusters fall between mono and multinucleated cell clusters. Finally, clusters with irregular morphology, fall in different areas of the object map. The predict algorithm is more robust than the cluster algorithm in identifying subtle morphologies in images.For example, Mononucleated Cells with MN versus Mononucleated cells without MN.The performance of the model can be assessed using tools, including class distribution histograms, accuracy statistics, and an interactive confusion matrix. In class distribution histograms, the closer the percentage values between the truth and predicted populations, the more accurate the model. In the accuracy statistics, The closer these metrics are to 100%the more accurate the model is at identifying events in the model classes.Finally, the interactive confusion matrix indicates where the model missclassifies events. Genotoxicity was measured by the percentage of micronuclei by microscopy, indicated by clear bars, and AI indicated by dotted bars following a 3 hour exposure and 24 hours recovery, for Mannitol, Etoposide and Mitomycin C, using both the Cytochalasin B and Non-Cytochalasin B methods. When creating a training experiment, ensure the loaded data contains imagery from positive and negative control samples.Micronuclei are rare, and sufficient imagery is required to build an accurate AI model. This procedure permits the creation of AI models to analyze imaging flow micronucleus data in any field of study, such as radiation biodosimetry. A rapid and robust AI-based method to identify micronuclei, may extend to other applications, such as quantifying micronuclei that may be predictive for the risk of cancer development.

automation-micronucleus-assay-using-imaging-flow-cytometry-artificial

Research

JoVE Journal

Bioengineering

1.8K visualizações.  Luminex Corporation. A realização do ensaio de micronúcleos usando citometria de fluxo de imagem supera muitas limitações dos métodos tradicionais, incluindo baixo rendimento, variabilidade do escore e falta de confirmação visual dos eventos. A principal vantagem dessa técnica é que todos os eventos-chave podem ser adquiridos por citometria de fluxo por imagem e analisados por inteligência artificial. Este método tem um potencial para ser usado na triagem em larga escala de produtos químicos e outros...