TomoScore: A Neural Network Approach for Quality Assessment of Cellular cryo-ET

TomoScore: A Neural Network Approach for Quality Assessment of Cellular cryo-ET | 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 Article TomoScore: A Neural Network Approach for Quality Assessment of Cellular cryo-ET Zhao Wang, Xuqian Tan, Ethan Boniuk, Anisha Abraham, Xueting Zhou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5405930/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 Electron cryo-tomography (cryo-ET) is a powerful imaging tool that allows three-dimensional visualization of subcellular and molecular architecture without chemical fixation. Tomogram quality varies widely, particularly during large high-throughput data collections, and the most common strategy for initial quality assessment is empirical judgment by an expert. Tomograms may be collected for two distinct purposes: annotation of subcellular features and cellular morphology, typically performed at lower magnifications and higher defocus, and subtomogram averaging, at high magnifications, closer to focus. For the first purpose, contrast and the ability to distinguish cellular features of interest are key, whereas for subtomogram averaging, recoverable signal at high resolution is the key factor. We have developed “TomoScore” a deep-learning based tomogram screening tool targeting cellular annotation. This tool provides a single quantitative measure of the suitability of a tomogram for annotation of subcellular features, in terms of the scale of features that can be readily distinguished. We further explore the relationship between accumulated electron dose and resulting quality, suggesting an optimum dose range for cryo-ET data collection. Overall, our study streamlines data processing and reduces the need for human involvement during pre-selection for tomogram segmentation. Biological sciences/Structural biology/Electron microscopy/Cryoelectron tomography Biological sciences/Computational biology and bioinformatics/Computational models Full Text Additional Declarations There is NO Competing Interest. Ethics Institutional Review Board for Baylor College of Medicine and Affiliated Hospitals approved this study. 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. 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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-5405930","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":448066736,"identity":"6aaf3c95-fa0b-4bd3-ac39-0d69a6b01739","order_by":0,"name":"Zhao 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