Robust Quantification of Regional Patterns of Migration in Three-Dimensional Cell Culture Models

Robust Quantification of Regional Patterns of Migration in Three-Dimensional Cell Culture Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Robust Quantification of Regional Patterns of Migration in Three-Dimensional Cell Culture Models Chun Kiet Vong, Alan Wang, Mike Dragunow, Thomas I-H Park, Vickie Shim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-573824/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Feb, 2022 Read the published version in Journal of Medical and Biological Engineering → Version 1 posted You are reading this latest preprint version Abstract Wound healing assays is a common two-dimensional migration model, with the spheroid assay three-dimensional migration model recently emerging as being more representative of in vivo migration behaviours. These models provide insight to the overall migration of cells in response to various factors such as biological, chemotactic and molecular agents. However, currently available analysis techniques for these assays fall short on providing quantifiable means to measure regional migration patterns, , which is essential to allow more robust assessment of drug treatments on cell migration in a chemotactic fashion. Therefore, the aim of this study is to develop a finite element (FE) based pipeline that can objectively quantify regional migration patterns of cells. Here, we report that our FE based approach was able to accurately measure changes in overall migration areas compared to the standard ImageJ method. Furthermore, our regional migration analysis provided accurate and quantitative means to analyse the migration pattern seen in the phantom data and our experimental results, giving us confidence that it can be a robust tool for analysing cell migration patterns. Biomedical Engineering Glioblastoma Cell migration Finite element analysis Spheroid assay Figures Figure 1 Figure 2 Figure 3 Figure 4 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF. Cite Share Download PDF Status: Published Journal Publication published 01 Feb, 2022 Read the published version in Journal of Medical and Biological Engineering → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-573824","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":32097468,"identity":"0820d9b6-a76f-4823-a586-5f623149edf2","order_by":0,"name":"Chun Kiet Vong","email":"","orcid":"","institution":"The University of Auckland","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chun","middleName":"Kiet","lastName":"Vong","suffix":""},{"id":32097469,"identity":"488d88c0-c888-43e2-b0e2-9f348e9dd70c","order_by":1,"name":"Alan Wang","email":"","orcid":"","institution":"The University of Auckland","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alan","middleName":"","lastName":"Wang","suffix":""},{"id":32097470,"identity":"4bd7bc64-b426-4f22-a471-f9d14471bff3","order_by":2,"name":"Mike Dragunow","email":"","orcid":"","institution":"The University of Auckland","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mike","middleName":"","lastName":"Dragunow","suffix":""},{"id":32097471,"identity":"14c934f3-4414-4b41-97e8-a4576ebd8785","order_by":3,"name":"Thomas I-H Park","email":"","orcid":"","institution":"The University of Auckland","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"I-H","lastName":"Park","suffix":""},{"id":32097472,"identity":"3f12b00e-dbc9-4835-bfe4-492949d90325","order_by":4,"name":"Vickie Shim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3RrQ7CMBDA8VtIWlMyuwngFToDYny8Spclw/AQU0OB5y2mFuQtSzbTgF2CYWYOD4KEFkvSgEP0b079cpcWwGb7zwgImOvpIMDga5K85w8EoPqBcKT9tducJ7ypBT6OIbhbJN3dSNiMR8UlyGWC5U6uwZOCcs9A/JQRTxEnb2mKw0xd2ALxuJHQXpHTSpPyqchEE2EgLsBUEYzylmClt3BN0EQGTJM4PshEVKNszQIZZX5qIIRue/9RLBf7pg66WxaOx01V+6YX+/gIBuCYdthsNpvtm16OmUyFRETkuwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1680-4287","institution":"The University of Auckland","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Vickie","middleName":"","lastName":"Shim","suffix":""}],"badges":[],"createdAt":"2021-05-29 21:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-573824/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-573824/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40846-022-00680-0","type":"published","date":"2022-02-01T10:17:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":10277855,"identity":"c7d09129-c3eb-42ea-b7f7-f7a4f518cb97","added_by":"auto","created_at":"2021-06-11 21:31:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":274111,"visible":true,"origin":"","legend":"Processing of a representative tumoursphere sample for ImageJ and FE model images. Area measurement using ImageJ was achieved by identifying the boundaries using the magic wand tool on thresholded images. FE area analysis required a Free Hand boundary region before it was imported into our FE analysis software as a mesh and the area was measured. White scale bar 250 µm. The 2D view of the 3D FE model is shown.","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-573824/v1/cae99f8f4ee35f095dab838a.png"},{"id":10277655,"identity":"fa8d1a20-3044-4422-ab83-b2ea7314d1f3","added_by":"auto","created_at":"2021-06-11 21:25:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74905,"visible":true,"origin":"","legend":"Changes in area measured in ImageJ and FE analysis correlated very highly with each other. Overall area was measured using ImageJ and our FE based method over 24 hours in 6 hour increments on two migrating tumoursphere repeats (A1 and B1). The areas were correlated using the William Pearson Correlation analysis in Graphpad Prism 8 and the R2 was measured (A2 and B2).","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-573824/v1/1cd373f5afce708a706cf416.png"},{"id":10277739,"identity":"ab8ce788-50cb-480b-8dac-d696f10d605d","added_by":"auto","created_at":"2021-06-11 21:28:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":243636,"visible":true,"origin":"","legend":"Our FE-based approach accurately quantified regional migration of the phantom data. The phantom data mesh (A1) was utilised in our FE models to quantify the migration rate over time which was visualised in the migration map (Red indicates high migration rate while blue indicates minimal migration rate; A2). Data points in the migration map were segmented in 60˚ increments and averaged, before they were plotted and compared against each other (B). Every segment at each timepoint were compared against the 120˚ segment and statistical significance was assessed via Two-way ANOVA test with Dunnett’s post-test; ****p\u003c0.0001 all segments versus 120˚ segment (B). Migration rate was calculated by finding the difference in migration distances between the current timepoint and the previous timepoint, and the difference was divided by the time that has passed between the two timepoints (C).","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-573824/v1/a59f2d7cf8f46ca0c9a069f7.png"},{"id":10277737,"identity":"021aad2d-9ca7-40b9-8d84-5780c416a33a","added_by":"auto","created_at":"2021-06-11 21:28:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":331672,"visible":true,"origin":"","legend":"FE-based analysis was capable of detecting and quantifying the nuanced migration. The images of the the migrating tumoursphere were taken every 6 hours for 24 hours (A1) and were analysed by our FE models to produce the migration maps (Red indicates high migration rate while blue indicates minimal migration rate; A2). The data points from the migration maps were plotted and graphed in segments to assess the regional migration of the tumoursphere. Every segment at each timepoint were compared against the 120˚ segment and statistical significance was assessed via Two-way ANOVA test with Dunnett’s post-test; **p\u003c0.005, ***p\u003c0.0005, ****p\u003c0.0001 all segments versus 120˚ segment, and different *p from different segment comparisons are outlined in the graph (B). Migration rate was calculated by finding the difference in migration distances between the current timepoint and the previous timepoint, and the difference was divided by the time that has passed between the two timepoints (C). White scale bar represents 250 µm.","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-573824/v1/e18b3d6f58892b5a615d4612.png"},{"id":18678362,"identity":"b4cc664d-3029-415d-a399-77a9d9ec373b","added_by":"auto","created_at":"2022-02-28 10:17:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1313258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptjmberefupdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-573824/v1_covered.pdf"},{"id":13645793,"identity":"00bcb488-7667-4072-93ef-972e694fe829","added_by":"auto","created_at":"2021-09-17 09:21:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1308140,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptjmberefupdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-573824/v1_covered.pdf"},{"id":10277856,"identity":"41e84866-0bc0-441e-96e1-6037ce8ff9cc","added_by":"auto","created_at":"2021-06-11 21:31:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1304620,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptjmberefupdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-573824/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eRobust Quantification of Regional Patterns of Migration in Three-Dimensional Cell Culture Models\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"\u003cp\u003eDue to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glioblastoma, Cell migration, Finite element analysis, Spheroid assay","lastPublishedDoi":"10.21203/rs.3.rs-573824/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-573824/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWound healing assays is a common two-dimensional migration model, with the spheroid assay three-dimensional migration model recently emerging as being more representative of in vivo migration behaviours. These models provide insight to the overall migration of cells in response to various factors such as biological, chemotactic and molecular agents. However, currently available analysis techniques for these assays fall short on providing quantifiable means to measure regional migration patterns, , which is essential to allow more robust assessment of drug treatments on cell migration in a chemotactic fashion. Therefore, the aim of this study is to develop a finite element (FE) based pipeline that can objectively quantify regional migration patterns of cells. Here, we report that our FE based approach was able to accurately measure changes in overall migration areas compared to the standard ImageJ method. Furthermore, our regional migration analysis provided accurate and quantitative means to analyse the migration pattern seen in the phantom data and our experimental results, giving us confidence that it can be a robust tool for analysing cell migration patterns.\u003c/p\u003e","manuscriptTitle":"Robust Quantification of Regional Patterns of Migration in Three-Dimensional Cell Culture Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-06-11 21:25:33","doi":"10.21203/rs.3.rs-573824/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"57b784b8-d895-4f03-b0cd-816fd3a2c6d4","owner":[],"postedDate":"June 11th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":4966271,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2022-02-28T10:17:45+00:00","versionOfRecord":{"articleIdentity":"rs-573824","link":"https://doi.org/10.1007/s40846-022-00680-0","journal":{"identity":"journal-of-medical-and-biological-engineering","isVorOnly":false,"title":"Journal of Medical and Biological Engineering"},"publishedOn":"2022-02-01 10:17:45","publishedOnDateReadable":"February 1st, 2022"},"versionCreatedAt":"2021-06-11 21:25:33","video":"","vorDoi":"10.1007/s40846-022-00680-0","vorDoiUrl":"https://doi.org/10.1007/s40846-022-00680-0","workflowStages":[]},"version":"v1","identity":"rs-573824","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-573824","identity":"rs-573824","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}