Semi-Independent Transcriptomic, Morphological, Connectivity Dimensions of the Mouse Brain Revealed by CoT-Mining 258 Million Multimodal Associations

Semi-Independent Transcriptomic, Morphological, Connectivity Dimensions of the Mouse Brain Revealed by CoT-Mining 258 Million Multimodal Associations | 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 Biological Sciences - Article Semi-Independent Transcriptomic, Morphological, Connectivity Dimensions of the Mouse Brain Revealed by CoT-Mining 258 Million Multimodal Associations Hanchuan Peng, Lijun Wang, Fuhui Long This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8864665/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 We address a fundamental question in brain cell typing: to what extent do transcriptomic, morphological, and connectivity features of neurons correspond to one another, and are they redundant or largely independent? To this end, we constructed NeuroXiv2, a large unified multimodal mouse-brain neuron database, integrating neuronal morphology, connectivity, and transcriptomic profiles across 1,385 hierarchical brain regions, 182,483 reconstructed neurons, and 5.24 million cells with expression data for 1,122 genes. We next built the NeuroXiv2 knowledge graph, encoding 258.4 million cross-modal associations among nodes spanning brain regions, neurons, microenvironmental subregions, and transcriptomic cell types. We then developed AI-Powered Open Mining with Chain-of-Thought (AIPOM-CoT) reasoning, an agent that integrates multimodal information and outperforms single-modality analyses in cell-type separation. Using this approach, we achieved a brain-wide, single-neuron resolution quantification demonstrating that neuronal morphology, brain connectivity, and transcriptomic profiles are neither completely redundant or fully independent. Indeed they form three semi-independent dimensions. Further, we showed that combining any two substantially improves predictive power, however none is fully deterministic. We also applied AIPOM-CoT to identify homogeneous Car3-positive neuronal subclasses jointly defined by anatomy, connectivity, and molecular signatures. Overall, the conceptual advances and methodological innovations presented here demonstrate the value of the NeuroXiv2 database, the power of AI-driven analysis, and the broader need for scalable, integrative cross-modal discovery in neuroscience. Biological sciences/Neuroscience Biological sciences/Computational biology and bioinformatics Full Text Additional Declarations There is NO Competing Interest. 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 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-8864665","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":607772687,"identity":"e63b5d18-acd3-4363-b0d6-8b521cfb54b0","order_by":0,"name":"Hanchuan Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACA/YeEAnE7MwNRGrhOQPWIsHAzEisFokcME2CFnPJt8c+/Ci4U8cP0vJxTy1hLZaz85Jn9hg8k5BsZmxgnPHsOBEOu51jzMBjcFjC4DBjAzPPgWNEaLl5xpjxD1CLPfFabvAYM4NtYQZrqSFCy5m8ZGYZg8OSM4C2HJxx4AARWo6fPcz45s9hfv725oMPPhyoI6wFBQCtOEyiFiAg1ZZRMApGwSgYCQAAjw05maOBBQ0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-3478-3942","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Hanchuan","middleName":"","lastName":"Peng","suffix":""},{"id":607772688,"identity":"7b3e4835-d7d4-4495-a722-683e0def4b96","order_by":1,"name":"Lijun Wang","email":"","orcid":"","institution":"Institute for Brain and Intelligence, Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Wang","suffix":""},{"id":607772689,"identity":"93180cbc-8319-4924-be89-36e5d4e3f405","order_by":2,"name":"Fuhui Long","email":"","orcid":"","institution":"Adadia","correspondingAuthor":false,"prefix":"","firstName":"Fuhui","middleName":"","lastName":"Long","suffix":""}],"badges":[],"createdAt":"2026-02-12 17:35:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8864665/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8864665/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093052,"identity":"045196fc-d1fc-4b9c-8c56-bb59c9a9df9c","added_by":"auto","created_at":"2026-04-03 11:33:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2826401,"visible":true,"origin":"","legend":"Article File","description":"","filename":"NeuroXivMS260212submission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8864665/v1_covered_79c47552-1606-4265-ae0d-03f59e209f69.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Semi-Independent Transcriptomic, Morphological, Connectivity Dimensions of the Mouse Brain Revealed by CoT-Mining 258 Million Multimodal Associations","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-8864665/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8864665/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We address a fundamental question in brain cell typing: to what extent do transcriptomic, morphological, and connectivity features of neurons correspond to one another, and are they redundant or largely independent? To this end, we constructed NeuroXiv2, a large unified multimodal mouse-brain neuron database, integrating neuronal morphology, connectivity, and transcriptomic profiles across 1,385 hierarchical brain regions, 182,483 reconstructed neurons, and 5.24 million cells with expression data for 1,122 genes. We next built the NeuroXiv2 knowledge graph, encoding 258.4 million cross-modal associations among nodes spanning brain regions, neurons, microenvironmental subregions, and transcriptomic cell types. We then developed AI-Powered Open Mining with Chain-of-Thought (AIPOM-CoT) reasoning, an agent that integrates multimodal information and outperforms single-modality analyses in cell-type separation. Using this approach, we achieved a brain-wide, single-neuron resolution quantification demonstrating that neuronal morphology, brain connectivity, and transcriptomic profiles are neither completely redundant or fully independent. Indeed they form three semi-independent dimensions. Further, we showed that combining any two substantially improves predictive power, however none is fully deterministic. We also applied AIPOM-CoT to identify homogeneous Car3-positive neuronal subclasses jointly defined by anatomy, connectivity, and molecular signatures. Overall, the conceptual advances and methodological innovations presented here demonstrate the value of the NeuroXiv2 database, the power of AI-driven analysis, and the broader need for scalable, integrative cross-modal discovery in neuroscience.","manuscriptTitle":"Semi-Independent Transcriptomic, Morphological, Connectivity Dimensions of the Mouse Brain Revealed by CoT-Mining 258 Million Multimodal Associations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-31 06:02:26","doi":"10.21203/rs.3.rs-8864665/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-neuroscience","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"neuro","sideBox":"Learn more about [Nature Neuroscience](http://www.nature.com/neuro/)","snPcode":"","submissionUrl":"","title":"Nature Neuroscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"42a1e8b4-df71-4981-8299-099f51f172d1","owner":[],"postedDate":"March 31st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64672949,"name":"Biological sciences/Neuroscience"},{"id":64672950,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2026-03-31T06:02:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-31 06:02:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8864665","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8864665","identity":"rs-8864665","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}