A Machine Learning-Driven Electrophysiological Platform for Real-Time Tumor-Neural Interaction Analysis and Modulation

A Machine Learning-Driven Electrophysiological Platform for Real-Time Tumor-Neural Interaction Analysis and Modulation | 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 A Machine Learning-Driven Electrophysiological Platform for Real-Time Tumor-Neural Interaction Analysis and Modulation Bingzhe Xu, Ting Xu, Xinyue Zhang, Kai Sheng, Jie Li, Jinliang Ren, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6437576/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Neural-tumor electrophysiology—marked by pathological membrane potentials and ion channel dysregulation—emerges as actionable targets to curb tumor aggression. Yet, how neural-driven bioelectrical crosstalk dynamically regulates tumors within functional circuits remains elusive, demanding tools for real-time interaction decoding. Here, we present a machine learning-driven electrophysiological platform that integrates custom microfluidics with real-time decoding of complex neural-tumor signal dynamics. This innovative approach reveals how glioma cells selectively hijack specific neural electrical patterns, synchronizing neural and tumor firing to drive hyper-invasive behavior. Critically, we demonstrate that glioma cells do not respond indiscriminately to spontaneous neural activity; instead, they selectively hijack specific subsets of neural signals, reshaping waveform properties and synchronizing neural and tumor firing events. This entrainment significantly enhances glioma invasiveness, establishing an interactive dynamic wherein glioma cells not only respond to but actively manipulate neural signals. Strikingly, targeted stimulation of glioma cells with these hijacked signal patterns—without direct neural involvement—was sufficient to induce hyper-invasive behavior, emphasizing the role of these electrical cues as drivers of tumor aggression. Our platform pioneers a novel methodology for real-time analysis of tumor-neural interactions, offering a translational toolkit that bridges mechanistic insights into glioma biology and therapeutic innovation targeting neural-tumor communication. Biological sciences/Biological techniques/Electrophysiology/Extracellular recording Biological sciences/Cancer/CNS cancer Biological sciences/Biological techniques/Lab-on-a-chip Electrophysiological platform Glioma invasion Neural-tumor interaction Signal decoding Microfluidics Machine learning Full Text Additional Declarations There is NO Competing Interest. The MOV S1 file is not available with this version. Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Nature Communications → 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-6437576","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443877885,"identity":"15b8d835-6699-4b7e-99b4-8c2b25149e15","order_by":0,"name":"Bingzhe 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