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

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.