Large-scale proteome inference from unpaired single-cell transcriptomic and proteomic data by msInfer

Large-scale proteome inference from unpaired single-cell transcriptomic and proteomic data by msInfer | 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 Large-scale proteome inference from unpaired single-cell transcriptomic and proteomic data by msInfer Yadong Wang, Tianyi Zhao, Yuzhi Sun, Renjie Liu, Liyuan Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9068677/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 Comprehensive characterization of cellular states requires simultaneous measurements of transcriptomes and proteomes at single-cell resolution. However, current technologies either measure only limited protein panels or quantify thousands of proteins at extremely low throughput. As a result, obtaining large-scale paired transcriptomic–proteomic measurements at single-cell resolution remains challenging. Here we present msInfer, a computational framework that integrates unpaired scRNA-seq and single-cell mass spectrometry (scMS) proteomics data to enable large-scale proteome inference for individual transcriptomic cells. To address the weak correlation between mRNA and protein abundance, msInfer replaces traditional anchor-based integration with a cell type–guided contrastive learning strategy for cross-omics alignment and employs an unsupervised weight generation module to infer protein abundances. Across extensive computational benchmarking and experimental validation, msInfer shows strong concordance between inferred and experimentally measured protein expression. msInfer facilitates the exploration of drug-induced molecular changes, supports the construction of single-cell multi-omics atlas and improves cell subtype annotation. Overall, msInfer provides a scalable and robust framework for bridging transcriptomic and proteomic measurements and enables comprehensive multi-omics characterization of cellular states. Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Computational biology and bioinformatics/Proteome informatics Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary20260309.pdf Supplementary 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-9068677","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615492921,"identity":"a1b305d2-a0f0-4c82-a3b9-8fee235b325b","order_by":0,"name":"Yadong 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