Cryo-EM Structure Reconstruction by Gaussian Splatting: Pushing the Resolution to Extreme

Cryo-EM Structure Reconstruction by Gaussian Splatting: Pushing the Resolution to Extreme | 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 Cryo-EM Structure Reconstruction by Gaussian Splatting: Pushing the Resolution to Extreme Bing Zeng, Shuaicheng Liu, Shen Cheng, Guikun Xu, Haoqiang Fan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6178664/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the field of structural biology, Cryo-EM based high-resolution 3-D structure reconstruction of complex macromolecules is a vital step. Although multiple attempts have been tried within this framework to consider quality-degrading factors such as imaging noise, non-uniform distribution of particle orientations, and sample heterogeneity in order to achieve high resolution, there is still a substantial gap between the best reconstruction resolution achieved by the existing methods and the hard resolution provided by the imaging device. Here, we introduce CryoGS, a novel 3-D reconstruction method for Cryo-EM structures using Gaussian splatting. Through the integration of 3-D Gaussian representations into neural network learning, CryoGS employs a spatial domain approach to optimize learnable 3-D Gaussians and project them into 2-D images using the splatting technique. Compared with the existing methods, CryoGS achieves significant improvements in resolution, isotropy, and computational efficiency. For example, CryoGS achieves a resolution of 2.217Å on EMPIAR-10492 dataset, approaching its theoretical limit of 2.2Å, while the best resolution achieved by the existing methods is 3.805Å. Furthermore, CryoGS exhibits remarkable robustness in reconstructing heterogeneous structures and high-resolution models under extreme conditions such as pose inaccuracy, limited particle data, and high noise. Based on these results, we believe that CryoGS has great potential to be a powerful tool for Cryo-EM applications to ensure enhanced resolution, robustness, and efficiency. Biological sciences/Structural biology/Electron microscopy/Cryoelectron microscopy Physical sciences/Mathematics and computing/Computational science Cryo-EM 3-D structure reconstruction Gaussian splatting CryoGS Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted 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-6178664","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":430434972,"identity":"732e86d4-9186-42ec-a980-93c29c7208a2","order_by":0,"name":"Bing Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYPACGxiDmWgtaTDVxGs5TIIWfvb2h48Lfp1P7Oc/f/ADQ4V1YgP72QN4tUj2HEg2ntl3O3HmjGRmCYYz6YkNPHkJeLUY3Eg4Js3bcztxww1mNgbGtsOJDRI8Bni12N9/2P6bt+dc4v7zh4Fa/hGhxUCCmY2Z58eBxA0MyUAtDURokTiTxizN25BsPONGsrFEwrF04zaeHPxa+NuPP/zM88dOtr//4MMPH2qsZfvZz+DXAgaMbVBGAhCzEVYPAn+IUzYKRsEoGAUjFAAA6LJDmxgK8QsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4491-7967","institution":"University of Electronic Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"Zeng","suffix":""},{"id":430434973,"identity":"908e66ea-ef59-4d50-bc63-d7c2583ad305","order_by":1,"name":"Shuaicheng Liu","email":"","orcid":"https://orcid.org/0000-0002-8815-5335","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Shuaicheng","middleName":"","lastName":"Liu","suffix":""},{"id":430434974,"identity":"38eb263b-3d02-48a0-abdc-206128624a29","order_by":2,"name":"Shen Cheng","email":"","orcid":"","institution":"Megvii Research, Megvii","correspondingAuthor":false,"prefix":"","firstName":"Shen","middleName":"","lastName":"Cheng","suffix":""},{"id":430434975,"identity":"a3f32ee7-1bf0-4d11-8476-452867226951","order_by":3,"name":"Guikun Xu","email":"","orcid":"https://orcid.org/0009-0000-8508-2786","institution":"Megvii Research, Megvii","correspondingAuthor":false,"prefix":"","firstName":"Guikun","middleName":"","lastName":"Xu","suffix":""},{"id":430434976,"identity":"e69a3035-a163-40da-8bfe-d560dca2ca46","order_by":4,"name":"Haoqiang Fan","email":"","orcid":"","institution":"Megvii Research, Megvii","correspondingAuthor":false,"prefix":"","firstName":"Haoqiang","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2025-03-07 13:30:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6178664/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6178664/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89936685,"identity":"dc171ecb-576c-4669-85fe-b95879fdd273","added_by":"auto","created_at":"2025-08-26 15:19:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13934815,"visible":true,"origin":"","legend":"Article File","description":"","filename":"CryoGS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6178664/v1_covered_f077c4f2-b88d-4352-81a5-bf580000d48f.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Cryo-EM Structure Reconstruction by Gaussian Splatting: Pushing the Resolution to Extreme","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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":" Cryo-EM, 3-D structure reconstruction, Gaussian splatting, CryoGS","lastPublishedDoi":"10.21203/rs.3.rs-6178664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6178664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the field of structural biology, Cryo-EM based high-resolution 3-D structure reconstruction of complex macromolecules is a vital step. Although multiple attempts have been tried within this framework to consider quality-degrading factors such as imaging noise, non-uniform distribution of particle orientations, and sample heterogeneity in order to achieve high resolution, there is still a substantial gap between the best reconstruction resolution achieved by the existing methods and the hard resolution provided by the imaging device. Here, we introduce CryoGS, a novel 3-D reconstruction method for Cryo-EM structures using Gaussian splatting. Through the integration of 3-D Gaussian representations into neural network learning, CryoGS employs a spatial domain approach to optimize learnable 3-D Gaussians and project them into 2-D images using the splatting technique. Compared with the existing methods, CryoGS achieves significant improvements in resolution, isotropy, and computational efficiency. 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