Large-scale proteomic inference at single-cell resolution by scInfer

Large-scale proteomic inference at single-cell resolution by scInfer | 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 proteomic inference at single-cell resolution by scInfer Yadong Wang, Tianyi Zhao, Yuzhi Sun, Renjie Liu, Liyuan Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal.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-6548225/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 Obtaining high-throughput, large-scale, and paired transcriptomic and proteomic data at the single-cell level is crucial for understanding the complex functions and phenotypic characteristics of multicellular organisms. However, current biomolecular measurement technologies are limited by their ability to detect only a small subset of functional proteins or by low cellular throughput, which hinders comprehensive analysis of cell function. Therefore, there is an urgent need for computational approaches that can bridge the gap between the high-throughput nature of single-cell RNA sequencing (scRNA-seq) and the large-scale protein profiling offered by single-cell proteomics. To address this challenge, we propose scInfer, a novel method that leverages single-cell proteomic data as a reference to infer large-scale protein expression profiles for each cell in scRNA-seq data. scInfer consists of two key modules: a self-supervised contrastive learning module that aligns unpaired transcriptomic and proteomic data, and an unsupervised weight generation module that performs the inference. We systematically evaluate the performance of scInfer on multiple datasets and demonstrate that the inferred protein expression closely matches experimentally measured values. scInfer enables effective downstream tasks such as differential protein identification and cell clustering. Moreover, it outperforms existing methods in multi-omics integration, significantly enhancing capabilities in cell subtype annotation, drug mechanism exploration, and the construction of single-cell multi-omics atlas. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Proteome informatics Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary20250419.pdf Supplementary File Editorialpolicychecklist.pdf editorial checklist nrreportingsummary.pdf reporting summary 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-6548225","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449125527,"identity":"f572c922-e0c3-47ba-84f8-3351b3aa594b","order_by":0,"name":"Yadong 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