Benchmarking and Automating the Biotinylation Proteomics Workflow

Benchmarking and Automating the Biotinylation Proteomics Workflow | 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 Benchmarking and Automating the Biotinylation Proteomics Workflow Ling Hao, Haorong Li, Noah Smeriglio, Jiawei Ni, Yan Wang, Shiori Sekine This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4590410/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 Protein biotinylation has been widely used in biotechnology with various labeling and enrichment strategies. However, different enrichment strategies have not been systematically evaluated due to the lack of a benchmarking model for fair comparison. Most biotinylation proteomics workflows suffer from lengthy experimental steps, non-specific bindings, limited throughput, and experimental variability. To address these challenges, we designed a two-proteome model, where biotinylated yeast proteins were spiked in unlabeled human proteins, allowing us to distinguish true enrichment from non-specific bindings. Using this benchmarking model, we compared common biotinylation proteomics methods and provided practical selection guidelines. We significantly optimized and shortened sample preparation from 3 days to 9 hours, enabling fully-automated 96-well plate sample processing. Next, we applied this optimized and automated workflow for proximity labeling to investigate the intricate interplay between mitochondria and lysosomes in living cells under both healthy state and mitochondrial damage. Our results suggested a time-dependent proteome remodeling and dynamic translocation within mitochondria and between mitochondria and lysosomes upon mitochondrial damage. This newly established benchmarking model and the fully-automated 9-hour workflow can be readily applied to the broad fields of protein biotinylation to study protein interaction and organelle dynamics. Biological sciences/Biological techniques/Proteomic analysis Physical sciences/Chemistry/Analytical chemistry/Mass spectrometry Biological sciences/Biochemistry/Proteomics Full Text Additional Declarations There is NO Competing Interest. Supplementary Files 20240613SupplementaryInformation.pdf Tables.zip Supplementary Data and Source Data 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. 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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-4590410","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":321604517,"identity":"fe6be873-286f-43d6-8640-8edfd4d00962","order_by":0,"name":"Ling 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However, different enrichment strategies have not been systematically evaluated due to the lack of a benchmarking model for fair comparison. Most biotinylation proteomics workflows suffer from lengthy experimental steps, non-specific bindings, limited throughput, and experimental variability. To address these challenges, we designed a two-proteome model, where biotinylated yeast proteins were spiked in unlabeled human proteins, allowing us to distinguish true enrichment from non-specific bindings. Using this benchmarking model, we compared common biotinylation proteomics methods and provided practical selection guidelines. We significantly optimized and shortened sample preparation from 3 days to 9 hours, enabling fully-automated 96-well plate sample processing. Next, we applied this optimized and automated workflow for proximity labeling to investigate the intricate interplay between mitochondria and lysosomes in living cells under both healthy state and mitochondrial damage. 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