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A Machine Learning-Driven Electrophysiological Platform for Real-Time Tumor-Neural Interaction Analysis and
Ting Xu1,2, Xinyue Zhang2, Youheng Jiang3
1School of Biomedical Engineering, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou, 510275, P.R. China.
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. Our findings show that glioma cells selectively hijack specific subsets of neural signals, reshaping waveform properties and synchronizing their firing events with neural activity. This dynamic interaction plays a critical role in boosting glioma invasiveness, as tumor cells harness neural activity to promote their progression. Notably, targeted stimulation of glioma cells with these hijacked signal patterns-without direct neural involvement-is sufficient to induce hyper-invasive behavior, emphasizing the role of these electrical cues as drivers of tumor aggression.
Insights
Glioma cells hijack neural signals, altering their electrical properties to drive tumor invasion. Manipulating these hijacked signals alone can increase glioma aggressiveness, revealing new therapeutic targets.
Area of Science:
- Neuroscience
- Oncology
- Bioengineering
Background:
- Neural and tumor cells communicate via bioelectrical signals, influencing tumor progression.
- Understanding this neural-tumor crosstalk is crucial for developing effective cancer therapies.
- Current tools lack the capability to decode these complex, real-time interactions.
Purpose of the Study:
- To develop a platform for real-time decoding of neural-tumor electrophysiological dynamics.
- To investigate how neural activity influences glioma cell behavior and invasiveness.
- To identify electrical cues that drive tumor aggression.
Main Methods:
- Utilized a machine learning-driven electrophysiological platform.
- Integrated custom microfluidics for precise signal control and measurement.
- Analyzed complex neural-tumor signal dynamics in real-time.
Main Results:
- Glioma cells selectively "hijack" specific neural signals, modifying their characteristics.
- Tumor cells synchronize their activity with neural firing patterns.
- Targeted electrical stimulation of glioma cells with hijacked patterns induced hyper-invasive behavior.
Conclusions:
- Neural-tumor bioelectrical crosstalk is a key driver of glioma progression and invasiveness.
- Glioma cells actively manipulate neural signals to promote their own growth.
- Electrical cues, independent of direct neural involvement, can significantly impact tumor aggression, offering novel therapeutic avenues.
