Live-organismal Transcriptomics

Live-organismal Transcriptomics | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Biological Sciences - Article Live-organismal Transcriptomics Hirofumi Shintaku, Kotaro Torii, Keiko Watanabe, Alissa Gordon, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4555410/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Recent studies employing live-cell transcriptomics have demonstrated measurement of the temporal changes in gene expression in single cells cultured in vitro. However, time-series analyses and in vivo applications remain unexplored. Here, we show that nanoelectrokinetic sampling uniquely enables live-cell and organism transcriptomics at multiple time points to uncover the individuality of dynamic gene expression in single cells and organisms. Autoregression with a live-cell transcriptome offers an inference of single-cell gene regulation networks, which provide solid mechanisms for the stochastic behaviour of individual cells. The live-organism transcriptome captured embryo-specific mRNA kinetics during the embryogenesis of the nematode Caenorhabditis elegans, identifying the core determinant of stochastic bifurcation in organismal life-or-death fates. These results highlight the power of real-time-series transcriptomics for comprehending the dynamic individuality concealed within apparently stochastic process of living things. We anticipate that our study will provide a starting point for understanding the spontaneous individualisation of living matter from single cells to organisms, contributing to the elucidation of fundamental laws in the diversification of life. Biological sciences/Biological techniques/Sequencing/RNA sequencing Biological sciences/Computational biology and bioinformatics/Gene regulatory networks Biological sciences/Systems biology/Cellular noise Biological sciences/Biotechnology/Nanobiotechnology/Microfluidics Biological sciences/Computational biology and bioinformatics/Data integration Figures Figure 1 Figure 2 Figure 3 Figure 4 Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFigures.pdf Extended Data Fig SupplementaryMovie1.avi Supplementary Movie 1 SupplementaryMovie2.avi Supplementary Movie 2 SupplementaryMovie3.avi Supplementary Movie 3 Cite Share Download PDF Status: Under Review 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-4555410","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":323688231,"identity":"7c458f01-db85-4845-8f1e-15c0b7add7d6","order_by":0,"name":"Hirofumi Shintaku","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9972-4222","institution":"Kyoto University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hirofumi","middleName":"","lastName":"Shintaku","suffix":""},{"id":323688232,"identity":"1887ac20-ae16-4d56-a10d-463a7f8ab657","order_by":1,"name":"Kotaro Torii","email":"","orcid":"","institution":"RIKEN","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kotaro","middleName":"","lastName":"Torii","suffix":""},{"id":323688233,"identity":"e5243125-37ba-48e1-9866-0e6442b61ee1","order_by":2,"name":"Keiko Watanabe","email":"","orcid":"","institution":"RIKEN","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Keiko","middleName":"","lastName":"Watanabe","suffix":""},{"id":323688234,"identity":"800fda80-ed68-4408-8047-87288d424951","order_by":3,"name":"Alissa Gordon","email":"","orcid":"https://orcid.org/0009-0002-6473-0697","institution":"University of Tennessee, Knoxville","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alissa","middleName":"","lastName":"Gordon","suffix":""},{"id":323688235,"identity":"6df46709-16f2-4dc0-9958-97a67f4e9f8c","order_by":4,"name":"Masahiro Yo","email":"","orcid":"","institution":"RIKEN Center for Brain Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Masahiro","middleName":"","lastName":"Yo","suffix":""},{"id":323688236,"identity":"402550e3-1a78-4492-85b2-d0c171dbc18a","order_by":5,"name":"Asako Sakaue-Sawano","email":"","orcid":"","institution":"RIKEN","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Asako","middleName":"","lastName":"Sakaue-Sawano","suffix":""},{"id":323688237,"identity":"d69c0607-5ff1-4366-a723-45fd15658b62","order_by":6,"name":"Atsushi Miyawaki","email":"","orcid":"https://orcid.org/0000-0002-0671-4376","institution":"RIKEN Center for Brain Science","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Miyawaki","suffix":""},{"id":323688238,"identity":"ae4e7358-216c-4f7b-a05e-23952b31d455","order_by":7,"name":"Kaori Nishikawa","email":"","orcid":"","institution":"RIKEN","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kaori","middleName":"","lastName":"Nishikawa","suffix":""},{"id":323688239,"identity":"c21180f0-a60f-45a1-a3e4-519e6b682723","order_by":8,"name":"Asuka Takeishi","email":"","orcid":"","institution":"RIKEN","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Asuka","middleName":"","lastName":"Takeishi","suffix":""}],"badges":[],"createdAt":"2024-06-10 02:20:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4555410/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4555410/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59953128,"identity":"16b5565f-54d9-469c-b8ed-f28fcae923bd","added_by":"auto","created_at":"2024-07-09 18:19:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":340641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLive-cell time-series transcriptome data revealed polymorphic scGRNs underlying phenotypic behaviors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Hierarchically clustered sequential Fucci signals of each cell in images (top) and trajectories of Fucci intensity in each cluster (bottom). \u003cstrong\u003eb\u003c/strong\u003e Analytical workflow. Comparation of scGRNs and phenotypic behaviors in cell cycle progression obtained from the same cells. Prediction of scGRNs is based on a time difference in real time-series of transcriptome data. \u003cstrong\u003ec\u003c/strong\u003e Two type of consensus scGRNs showing the variance (left) and average (right) of edge weight in 15 scGRNs. \u003cstrong\u003ed\u003c/strong\u003e Heatmap of a matrix of trajectory difference among each Fucci trajectory shown in (c\u003cstrong\u003e)\u003c/strong\u003e. \u003cstrong\u003ee\u003c/strong\u003e Heatmap of a correlation matrix of scGRNs. \u003cstrong\u003ef\u003c/strong\u003e Scatter plot showing the negative correlation between the two matrices of trajectory difference (d) and graphical correlations (e). \u0026nbsp;\u003cem\u003eP\u003c/em\u003e value was estimated with a two-tailed \u003cem\u003et\u003c/em\u003e test. \u003cstrong\u003eg\u003c/strong\u003e Histogram of the Spearman’s ρ between the two matrices of trajectory difference and graphical correlations derived from permutated time-series data. A red arrowhead shows original correlation coefficient in (f). \u003cstrong\u003eh\u003c/strong\u003e Comparison of regression accuracy among four types of scGRNs predicted from different feature types. (Upper left) Variables in time-series transcriptome data. (Lower left) Four regression models for time-series regression. (Upper right) Cross-validation with multi-objective variable regression using scGRNs as the explanatory variable and Fucci trajectory as the objective variable. (Lower right) Histogram showing regression accuracy of test data based on each regression model.\u003c/p\u003e","description":"","filename":"MainFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4555410/v1/59c9ab2aec59f0880f89c642.png"},{"id":59952574,"identity":"bfd9cbaf-9dc3-4798-9ef5-8942332f1e49","added_by":"auto","created_at":"2024-07-09 18:11:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":327222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSMAD2/EP300-CDKN1A pathway is a modulator of phenotypic behaviors in cell cycle progression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eA\u003cstrong\u003e \u003c/strong\u003esubnetwork of consensus scGRN based on Gini index evaluated in regression with scGRNs and Fucci trajectories. \u003cstrong\u003eb\u003c/strong\u003eA subnetwork of consensus scGRN showing total weight of directly pointing edges for each targeted nodes (Left) and each source nodes (Right). \u003cstrong\u003ec\u003c/strong\u003ePrediction of Fucci trajectories in absence of a regulation in \u003cem\u003eSMAD2-CDKN1A \u003c/em\u003eand\u003cem\u003eEP300-CDKN1A \u003c/em\u003e\u0026nbsp;pathways. (Upper) The learner trained on the original dataset was provided with scGRNs, where the both edge weights were overwritten to 0, to predict the Fucci trajectory. (Bottom) Predicted Fucci trajectory in each cluster. \u003cstrong\u003ed\u003c/strong\u003e Patterns of cell cycle progression in original HeLa/Fucci cells and mutated cells with deletion of two nucleotides in SMAD2 binding site on \u003cem\u003eCDKN1A\u003c/em\u003e promoter in the first 2.5 hours. Upper hierarchical clustering showed the major two clusters with the distinct cell-cycle patterns of original cells indicated by light gray and mutated cells indicated by purple. Bottom panel showed temporal changes of Fucci signals by each cluster and cell type. Chi squared test revealed a significant p-value of 0.0072.\u003c/p\u003e","description":"","filename":"MainFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4555410/v1/28d3dace0bcc2f99d63199cb.png"},{"id":59952572,"identity":"dc998d7e-65c0-4981-9df8-3bedebed28a9","added_by":"auto","created_at":"2024-07-09 18:11:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":306891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLive-organismal transcriptome captured the embryo-specific orchestration of mRNA kinetics along cell lineages.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Sequential images of time-series sampling and subsequent embryogenesis in a hatched embryo (Left) and a dead embryo (Right). Scale bars: 25 μm. \u003cstrong\u003eb\u003c/strong\u003e Three types of features in single-embryo transcriptome data. Velocity is time difference in the cell lineage standardized by the expression level in zygotes. Divergence is the difference of the velocities between cell lineages. \u003cstrong\u003ec\u003c/strong\u003eZoom-in space for each feature type in Supplementary Fig. 10b. \u003cstrong\u003ed,e\u003c/strong\u003e Measurement of the degree of separation between fates in the UMAPspace. (d) The increasing trend of prediction accuracy depending on the complexity of the decision-tree model for each feature. (e) Quantification of the degree of separation as area under curve of each line in (d). \u003cstrong\u003ef\u003c/strong\u003eComparison of fate prediction accuracy among original and shuffled single-embryo data. In partially shuffled data, transcriptome data in each cell is randomly shuffled within embryos with the same fate. In shuffled data, transcriptome data in each cell is shuffled within whole embryos without distinguishing fates. Velocity and divergence are re-evaluated from the shuffled data of gene expression. For the comparation of prediction accuracy among feature types, feature values are vertically stacked for each embryo. \u003cstrong\u003eg\u003c/strong\u003e Prediction accuracy of each feature type in original and shuffled dataset (upper pannel: partially shuffled; lower pannel: shuffled).\u003c/p\u003e","description":"","filename":"MainFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4555410/v1/ae4fd8ee2c878be4a6e67a0d.png"},{"id":59952575,"identity":"810978e0-a3b5-4708-a2bc-b01aa09fc205","added_by":"auto","created_at":"2024-07-09 18:11:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":966217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eZygotic expression level of\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e fzy-1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eis a core determinant of the fate in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eoma-1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003ezu405\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Identification of determinants based on classification analysis with a random forest. (Left) Classification analysis with vertically stacked single-embryo data. (Upper right) UMAP embedding with whole features. (Lower right) UMAP embedding with identified 573 determinants. \u003cstrong\u003eb\u003c/strong\u003e Search for zygotic determinants with high explainability to the variations of determinants in two-cell stage divergence. The explainability is based on feature importance in regression and on Sperman’s correlation coefficient between the two types of determinants. \u003cstrong\u003ec\u003c/strong\u003e (Left) Histograms of \u003cem\u003efzy-1\u003c/em\u003e and \u003cem\u003enoca-1\u003c/em\u003e expression levels in zygotes. (Right) The zygotic expression levels of \u003cem\u003efzy-1\u003c/em\u003e and \u003cem\u003enoca-1\u003c/em\u003e in hatched embryos and dead embryos. \u003cstrong\u003ed\u003c/strong\u003e Correlation network for the entire set of determinants. The edges show significant correlation between the inter-embryonic variations of each feature. \u003cem\u003eP\u003c/em\u003e\u0026nbsp;value was estimated with a two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test. \u003cstrong\u003ee\u003c/strong\u003e Closeness centrality of correlation network. The circle size indicates Gini indexe obtained by the classification analysis in (a). Pink and white arrowheads indicate\u0026nbsp; \u003cem\u003efzy-1 \u003c/em\u003eand\u003cem\u003e noca-1\u003c/em\u003e in zygotes, respectively. \u003cstrong\u003ef\u003c/strong\u003e Comparison of correlation between two types of importance scores, derived from PCA and classification analysis, of each gene in individual cell types. FDR is based on \u003cem\u003eP\u003c/em\u003e\u0026nbsp;value estimated with a two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test. \u003cstrong\u003eg\u003c/strong\u003e Cell-type specific correlation networks for whole variable genes. Gray and coloured nodes are non- and determinants in each cell types, respectively. The edges indicate high correlation (Sperman’s correlation, \u003cem\u003er\u003c/em\u003e \u0026gt; 0.5) between the inter-embryonic expression variations of individual genes.\u003c/p\u003e","description":"","filename":"MainFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4555410/v1/bd1321cced658c55ced3e98c.png"},{"id":61837062,"identity":"375da89b-85fd-419b-91a5-5c3598a0e374","added_by":"auto","created_at":"2024-08-06 05:58:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1615879,"visible":true,"origin":"","legend":"","description":"","filename":"Livever6.1submit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4555410/v1_covered_a55e879b-18e1-4f64-9c27-a9ca7984d247.pdf"},{"id":59952577,"identity":"8ca79497-07f7-4030-8ab1-37606bfad5ae","added_by":"auto","created_at":"2024-07-09 18:11:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6363046,"visible":true,"origin":"","legend":"Extended Data Fig","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4555410/v1/1599e2794f2cd7c65cf4c952.pdf"},{"id":59953129,"identity":"1511e88d-e872-4b01-8f5d-ba8e4dfd0a96","added_by":"auto","created_at":"2024-07-09 18:19:25","extension":"avi","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":147590,"visible":true,"origin":"","legend":"Supplementary Movie 1","description":"","filename":"SupplementaryMovie1.avi","url":"https://assets-eu.researchsquare.com/files/rs-4555410/v1/af720f8df71b125567804008.avi"},{"id":59952595,"identity":"b0eea115-8d0a-46dc-80d9-c9d116b0d539","added_by":"auto","created_at":"2024-07-09 18:11:30","extension":"avi","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":320654254,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Movie 2\u003c/p\u003e","description":"","filename":"SupplementaryMovie2.avi","url":"https://assets-eu.researchsquare.com/files/rs-4555410/v1/c474d24aee4eb4a5f1493f12.avi"},{"id":59952593,"identity":"4c79ee4b-9f7d-4591-a573-07b0968962da","added_by":"auto","created_at":"2024-07-09 18:11:26","extension":"avi","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":51927392,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Movie 3\u003c/p\u003e","description":"","filename":"SupplementaryMovie3.avi","url":"https://assets-eu.researchsquare.com/files/rs-4555410/v1/466b7d6c89d91d9e3f423e94.avi"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Live-organismal Transcriptomics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4555410/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4555410/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Recent studies employing live-cell transcriptomics have demonstrated measurement of the temporal changes in gene expression in single cells cultured in vitro. However, time-series analyses and in vivo applications remain unexplored. Here, we show that nanoelectrokinetic sampling uniquely enables live-cell and organism transcriptomics at multiple time points to uncover the individuality of dynamic gene expression in single cells and organisms. Autoregression with a live-cell transcriptome offers an inference of single-cell gene regulation networks, which provide solid mechanisms for the stochastic behaviour of individual cells. The live-organism transcriptome captured embryo-specific mRNA kinetics during the embryogenesis of the nematode Caenorhabditis elegans, identifying the core determinant of stochastic bifurcation in organismal life-or-death fates. These results highlight the power of real-time-series transcriptomics for comprehending the dynamic individuality concealed within apparently stochastic process of living things. We anticipate that our study will provide a starting point for understanding the spontaneous individualisation of living matter from single cells to organisms, contributing to the elucidation of fundamental laws in the diversification of life.","manuscriptTitle":"Live-organismal Transcriptomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-09 18:11:20","doi":"10.21203/rs.3.rs-4555410/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4ab72e1a-3b1b-4a37-b79f-0456fba24e1c","owner":[],"postedDate":"July 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":34229672,"name":"Biological sciences/Biological techniques/Sequencing/RNA sequencing"},{"id":34229673,"name":"Biological sciences/Computational biology and bioinformatics/Gene regulatory networks"},{"id":34229674,"name":"Biological sciences/Systems biology/Cellular noise"},{"id":34229675,"name":"Biological sciences/Biotechnology/Nanobiotechnology/Microfluidics"},{"id":34229676,"name":"Biological sciences/Computational biology and bioinformatics/Data integration"}],"tags":[],"updatedAt":"2025-07-11T01:55:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-09 18:11:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4555410","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4555410","identity":"rs-4555410","version":["v1"]},"buildId":"CiT4i_kKBbxQbnFL0ufpk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}