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

Domain: neuroscience
Published: 2026-01-07
Editorial outcome: minor_revision

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.

Introduction
Understanding tumor-neural interactions is critical to uncovering the mechanisms driving glioma progression and invasiveness. Recent studies reveal that electrophysiological properties of tumor cells—such as membrane potential and ion channel activity—are central regulators of proliferation, migration, and invasion1–8 These properties, integral to the tumor microenvironment9, influence cancer behavior by modulating ion permeability and signal transduction, leading to abnormal membrane polarization3,10,11 Ion channels thus represent promising therapeutic targets12–14, as they control proliferation via membrane potential and ionic homeostasis at cell cycle checkpoints (e.g., voltage-gated sodium channels maintaining glioblastoma stemness in G0 phase)15,16, and calcium-activated potassium channels (BK) correlate with glioma grade and growth17. For migration and invasion, coordinated K⁺/Cl⁻ efflux through BK channels enables volume regulation for tissue navigation18,19, while calcium transients drive collective invasion20. Glioblastomas exhibit pathological ion channel reprogramming, including Kir4.1 loss and BK upregulation21, with ~90% of ion channel mutations linked to aggressive phenotypes through disrupted differentiation and enhanced plasticity22. Mutations in ion channels and pumps are common in GBM23, promoting high proliferation, migration, and invasiveness24.

One particularly compelling aspect of tumor electrophysiology is the bidirectional interaction between neurons and glioma cells5,25. Neuronal activity promotes glioma progression via synaptic integration and neurotrophic factor release, as glioma cells can structurally and electrically incorporate into neural circuits26. Neurons modulate glioma malignancy through neural excitation, paracrine signaling, and direct synaptic connections27–30. Paracrine factors such as neuroligin-3 and BDNF31, along with neuron-to-glioma AMPA receptor–mediated synapses32, induce excitatory postsynaptic currents (EPSCs) that depolarize glioma membranes and drive proliferation33,34. Functional glutamatergic “neurogliomal synapses” identified by Venkataramani et al32. demonstrate that presynaptic glutamate activates AMPA receptors on glioma cells, triggering EPSCs, depolarization, and calcium influx35, thereby enhancing glioma stem cell (GSC) proliferation and invasion. Additionally, glioma networks generate rhythmic electrical waves propagated through tumor microtubes, reinforcing malignancy3. These neuron–glioma interactions remodel both cellular and network dynamics36–39, amplifying glioma excitability and growth while increasing neuronal hyperexcitability through glutamate release and synaptogenic signaling, and reducing inhibitory interneurons35,40–42 Therapeutically, targeting these pathways shows promise: AMPA receptor antagonists such as perampanel block neuron-induced EPSCs and suppress tumor growth43; anti-inflammatory agents like meloxicam reduce invasiveness by disrupting gap junction–mediated Ca²⁺ signaling; and gabapentin inhibits glioma-driven synaptogenesis by blocking TSP1 signaling44.

Despite these advances45–50 current research tools lack the resolution and versatility to systematically study complex interactions between neurons and tumors. In this work, we present a machine learning–driven electrophysiological platform that enables real-time analysis and modulation of tumor–neural interactions. Integrating microfluidic precision with computational modeling, this system decodes and manipulates complex electrophysiological signals to reveal high-resolution dynamics of glioma–neuron communication. Our approach identifies specific neural circuits hijacked by glioma cells to promote hyper-invasive behavior and uncovers distinct electrophysiological signatures associated with this invasive state. Moreover, we demonstrate that reproducing these signatures in vitro recapitulates tumor phenotypes, supporting the hypothesis that bidirectional neuron–glioma signaling exerts a stronger influence on invasiveness than neural activity alone.

Results
Platform design and characterization
Here, we present a machine learning-driven electrophysiological platform that integrates custom microfluidics with real-time decoding of complex neural-tumor signal dynamics (Fig. S1). The platform architecture is composed of five distinct layers with specific functionalities: (1) Signal Acquisition Hardware Layer, featuring microelectrode arrays (MEAs) for capturing neural and tumor electrophysiological activities, paired with a stimulation device for precise electrical modulation; (2) Data Collection and Processing, which filters, denoises, and normalizes raw signals (processing 127,060 signals with spectral analysis and waveform characterization) to extract spatiotemporal tumor-neural correlations; (3) Machine Learning (LSTM Architecture), employing dual LSTM layers (128 units each) with a 0.3 dropout rate to decode local/global temporal dependencies, enabling pattern recognition via signal prediction (ReLU, MSE) and classification (Softmax, CE); (4) Regulation and Intervention, identifying neural signal hijacking events and implementing real-time feedback control; (5) Visualization and Monitoring, providing dynamic tracking of tumor invasion and neural activity through integrated imaging and fluorescence staining. By synergizing microfluidics, MEAs, real-time analytics, and closed-loop intervention, this platform offers all-in-one solution for dissecting tumor-nerve crosstalk.

The microfluidic MEA component (Fig. 1a) integrates several critical components for precise tumor-neural interaction analysis. First, dual-channel perfusion inlets and outlets enable dynamic nutrient delivery and waste removal, mimicking physiological microenvironments while maintaining long-term co-culture stability. Then a high-density MEA layout with spatially optimized contact points ensures real-time electrophysiological recording from tumor-neural interface regions (Fig. 1a), capturing local field potentials (LFPs) and single-cell spiking activities. Tumor and neural systems are initially confined to separated regions (Fig. 1b), with micro-channels allowing real-time monitoring tumor invasion into neural networks over 7 days. The electrophysiological recordings obtained during this period showed distinct patterns that corresponded to different stages of the interaction between the tumor and nerve cells. Leveraging deep learning technology, the platform achieves feature-specific weighting for signal discrimination, and analyzes spatiotemporal correlations between tumor invasion and neural activity through LSTM-based dynamic interaction mapping (Fig. 1c).

A customized detection platform (Fig. S2), comprising a collection module, microscope port, signal amplification system, processing platform, and shielding enclosure, was designed to be compatible with sensor and imaging technologies (Fig. 2a). This integration enables the combination of neural imaging and fluorescence staining techniques, facilitating the acquisition of high-dimensional data on tumor and neural activities. The staggered electrode layout (Fig. 2b, c) allows real-time discrimination between newly invading tumor signals (detected at right electrodes with <10 ms latency) and embedded hybrid signals (recorded from central electrodes with time-delayed correlation to neural activity). By assigning unique electrode clusters to tumor-front regions, the system generates spatially encoded signal fingerprints, enabling machine learning algorithms to classify invasion stages based on signal propagation patterns (e.g., radial spread vs. neural co-option). As demonstrated in Fig. 2d, the platform’s signal processing pipeline leverages these spatiotemporal tags to dynamically separate signals into different categories (signal 1, 2, 3…, indicating early invasion signals, mixed region signals, and established invasion signals). Figure 2e displays the fabricated device picture. This microfluidic-electrode array chip connects to external circuits via a specialized vertical quick-clamp-based connector composed of a base, circuit platform, and quick clamp (Fig. 2f, g). The acquisition board features four pre-stage probe interfaces, each capable of connecting two RHD-2000 pre-stage probes, thus supporting up to eight 64-channel pre-stage probes and acquiring signals from 512 channels simultaneously. Driven by an FPGA module, the board synchronizes input data from all pre-stage probes, serializes the parallel inputs, and transmits the data to the upper computer.

The electrode array demonstrated consistent resistance values across both manufacturing and assembly (Fig. 2h), ensuring reliable and uniform signal capture. Gaussian-distributed baseline noise (pH 7.2–7.4) confirmed predominantly internal system-derived interference, validating a high-sensitivity electrophysiological platform optimized for intracranial microenvironment simulation. Baseline noise (4.91 to −4.65 μV, Fig. S3) remained within neural signal detection thresholds, demonstrating a low-noise design essential for precise signal acquisition in microfluidic/co-culture systems where neural amplitudes are inherently small. Recorded neural electrical signals and processed data (Fig. 2i, j) demonstrate the system’s effectiveness. We implemented a modified threshold filter to mitigate signal peaks that could distort noise distribution estimates (Fig. 2k), achieving a 17% reduction in mean absolute error (MAE) post-filtering. The filtering criteria were as follows:

threshold=AVG+factor\cdot STD (1)

AVG 0 = 1/n \sum_{i=1}^{n} x0_i (2)

STD0= \sqrt{\frac{\sum_{i=1}^{n} (x0_i - AVG 0)^2}{n}} (3)

modified threshold=AVG 0 +factor\cdot STD0 (4)

x0_i \in signal and |x0_i|<threshold (5)

In summary, these results validate the platform’s capability to capture fine-scale electrophysiological events during glioma invasion within a neural microenvironment. The minimized baseline noise level enhances data precision, essential for accurately detecting and interpreting dynamic signal changes in co-culture conditions.

Hyper-invasive behavior in brain tumors driven by specific neural networks
We developed a pneumatic-based microfluidic system (Fig. 3a) designed to reliably initiate glioma cell invasion in a controlled neural environment. The system comprises a main chamber, microchannel, air pump interface, and pump. The open main chamber allows for direct loading of cells and culture medium, while the microchannel connects the main chamber to the air pump interface, which controls air pressure within the microchannel to regulate the initial positioning of cells. The fluid control unit precisely managed liquid flow within the microchannel, improving the reliability and reproducibility of the experimental setup (Fig. 3a, MOVIES 1). To track cell locations reliably, the glioma cells were transfected with GFP for expression in the cytoplasm (Fig. 3b). This setup allows for precise tracking of cell movements and interactions with the environment, thereby facilitating a detailed analysis of electrophysiological changes during tumor invasion. In our 24-h continuous observation of glioma cells within the microchannel, we observed obvious cellular movement (Fig. 3c), demonstrating the system’s effectiveness in recording glioma cell migration.

Distinct populations of green-fluorescent glioma cells and neuron-marker-positive neural cells within the microfluidic channel (Fig. 3d) were arranged to facilitate direct interactions, which is critical for simulating the in vivo environment where glioma cells engage with neural networks. Over a 7-day observation period of temporal changes in glioma cell distribution across electrodes, we observed hyper-invasive behavior in glioma cells within specific microfluidic chips featuring distinctive neural signals, as evidenced by pronounced changes in cell distribution (Fig. 3e). Daily imaging revealed accelerated tumor movement in these hyper-invasive tumors, with cells traversing most electrodes by days 3-5. A histogram of daily cell positions (Fig. 3f) indicates that hyper-state invasive glioma migrates faster and more extensively over the same timeframe, underscoring enhanced motility and invasiveness. The temporal dynamics heatmap (Fig. 3g, h) further shows that tumor cells in the hyper-invasive state respond more rapidly, exhibiting faster and more widespread migration, whereas the normal-state tumor displays slower, less intense activity.

10,000 cells were seeded into the device (ratio of tumor cells to nerve cells is generally 1:1), and almost all seeded cells were retained within the device. Viability assessment demonstrated that cell survival rate remained above 80% following 7 days of culture (Fig. S4). To quantitatively assess the extent of tumor invasion, we introduced the Cell Migration Center (CMC) as a metric to define the overall migration of tumor populations within the microchannel. The CMC is calculated based on the weighted positions of cells, with cells closer to the end of the channel given greater weight. This method provides a comprehensive quantification of cell migration by integrating the spatial distribution of all migrating cells. The CMC allows for a more precise measurement of tumor invasion dynamics compared to individual cell tracking, as it reflects the collective behavior of the entire cell population.

CgMC= \frac{\sum_{i=1}^{n} CN_i \cdot d_i}{\sum_{i=1}^{n} CN_i}

Where the variable “i” is defined as the electrode serial number, while “d” denotes the center-to-center distance between adjacent electrodes, “n” is the total number of electrodes, “CN_i” is the cell count near electrode “I”. Compared to traditional single-cell tracking, this population-level analysis largely reduces data variability due to high intercellular differences in migration speed within the tumor population (Fig. S5). The quantitative CMC analysis (Fig. 3i) further corroborates our previous observations, showing a marked enhancement of glioma cell migration with neurons. The results indicate that glioma cells exhibit a baseline invasive behavior in the absence of neurons (defined as “Normal state”, Fig. 3i). Notably, after the addition of neurons, the invasion rate of some tumor cells was observed to increase significantly (defined as “Hyper-Invasive”, Fig. 3i). Hyper-invasive tumor cells exhibited rapid migration, with a sharp increase in CMC during the first 4 days, reaching the maximum migration distance by day 4, as some of them traversed the entire length of the microchannel.

Unhijacked neural activity fails to induce tumor hyper-invasion
To systematically resolve the distinct bioelectrical signatures of neurons and glioma cells in tumor microenvironments, we constructed a standardized electrophysiological signal database under controlled conditions on the platform. We employed a microelectrode array (MEA) system to synchronously record transmembrane potential changes in both cell types (sampling rate: 30 kHz), with environmental noise suppressed using an adaptive Kalman filter. The raw signals after wavelet denoising revealed distinct characteristics: neuronal action potentials exhibited typical biphasic spike waveforms (duration: 1.2±0.3 ms; peak-to-peak interval: 8.5±1.2 ms), while tumor cells displayed prolonged multiphasic potential profiles (duration: 1~15.6 ms) with high variability in peak intervals (coefficient of variation: 32%). These time-domain differences provide a critical basis for subsequent machine learning classification. In addition to conventional time-domain features (amplitude, slope, interval integral), we extracted energy distribution characteristics in the 0.1–5 kHz frequency band via Morlet wavelet transforms and derived nonlinear dynamic parameters of intrinsic mode functions (IMFs) using Hilbert-Huang transforms. After optimization with a recursive feature elimination (RFE) algorithm, 12 highly discriminative feature vectors were selected as inputs for a modified residual neural network (ResNet-18). This network achieved a classification accuracy of ~98% in cross-validation (Fig. S6, bottom right corner), demonstrating robust performance in distinguishing neuronal and glioma electrophysiological signatures.

To further explore the mechanisms underlying hyper-invasive behavior in gliomas within certain neural environments, we try to extracted electrophysiological signals from neurons and applied them directly to stimulate tumor cells. Our hypothesis proposed that certain type of neural spontaneous electrophysiological signals promotes glioma invasion by influencing intracellular biological signaling (Fig. 4a). Neuronal activity, essential for brain function, has been reported to modulate glioma cell migration via electrical interactions and calcium channel-dependent depolarization, which triggers calcium influx into adjacent tumor cells to regulate intracellular calcium levels and drive migratory behavior. Therefore, when glioma cells are exposed to these neural signals, they may exhibit changes in intracellular calcium levels that influence their migratory responses. Our calcium flux monitoring confirmed that spontaneous neural activity can indeed propagate calcium transients to adjacent glioma cells (Fig. 4b), establishing a pathway for electrophysiological crosstalk in the tumor microenvironment. Initially, Cell 1 exhibits initial firing, which propagates to Cell 2, subsequently propagating to cells 3 and 5. Upon simultaneous stimulation from Cells 1 and 3, Cell 4 (which seems with a higher activation threshold) is activated and engages in co-firing with Cells 1 and 3. It can be observed that Cell 4 itself exhibits reduced firing events, further supporting it may have a higher activation threshold and consequently lower excitability. Consequently, despite Cell 4’s closer proximity to Cell 1, the signal preferentially activates Cell 3, and only when convergent stimulation from Cell 1 and Cell 3 occurs does Cell 4 become activated and functionally incorporated into the form co-firing network. Additionally, signals from cells 2 and 5 showed no obvious co-firing with cell 1 (Fig. S7), indicating that these firing effects are locally transient and do not propagate further. Notably, we observed synchronized firing events between neurons and glioma cells (Fig. 4c), suggesting that glioma cells do not passively respond to neural signals but engage actively with this activity3,5,51. This coordination of calcium signals between neurons and tumor cells underscores the importance of neural-tumor electrophysiological interactions in influencing tumor invasion dynamics.

The extraction and application of neuronal stimulation signals enabled direct assessment of the influence of neural electrophysiological activity on glioma invasion. We captured and filtered real-time electrophysiological activity of neuronal networks who can promote tumor migration via our customized microelectrodes array, preserving key temporal and frequency-dependent information. The temporal domain analysis of the neural electrical signals (Fig. 4d) revealed two primary components: low-amplitude baseline fluctuations within ±10 μV and brief, high-amplitude peaks, which we define as “noise” and “code” respectively. This “neural codes” data retains the biological encoding necessary for evaluating its role in modulating tumor behavior, with the time sequence and shape of the signals being critical factors that likely contribute to decode biological information.

We extracted stimulatory signals with specific patterns of neural activity that strongly promoted tumor invasion from 46519 neural electrophysiology signals. A detailed examination of these waveforms allowed us to classify and quantify their occurrence across all extracted signals, providing insights into the distribution of each category and identify certain types of electrical stimuli being more effective in promoting tumor invasion. Through detailed waveform analysis, we identified and categorized a total of >1000 “neural codes” waveform types, and screened 295 frequently used codes (Fig. 4e–h, showcasing representative examples). Characterizing these “neural codes” is essential for understanding the electrophysiological mechanisms involved, particularly in identifying firing patterns associated with glioma invasion or other pathological processes, making them integral to bioelectrical signal interpretation.

To generate electrophysiologically valid stimulation signals, we trained a dual-branch LSTM model on 46,519 spatiotemporal neural event sequences encompassing peak types, inter-peak intervals, and sequential dependencies. At each time step, the model processed three inputs: (1) one-hot encoded peak type, (2) continuous interval duration, and (3) historical signal context. The architecture employed two task-specific output branches: a softmax classifier for peak-type prediction and a ReLU-based regressor for interval estimation. Multi-task learning was implemented using a combined loss function (categorical cross-entropy + MSE), optimized via Adam (learning rate: 0.001) over 50 epochs with early stopping to prevent overfitting. The similarity between the simulated signal stimulation and nerve stimulation is shown in Fig. 4i (the relative occurrence frequency of code types with the most significant changes, the top 70 code types were selected with most of total variation). The application of neural spontaneous electrophysiological signals to glioma cells elicited a moderate enhancement of invasive behavior, confirming that the temporal dynamics and frequency modulation inherent in this neural activity has a stimulatory influence on tumor cell migration (Fig. 4j, k). However, this observed increase in invasion was less striking than the accelerated invasion patterns documented under hyper-invasive conditions (Fig. 4l), indicating that spontaneous neural network patterns alone may not fully explain the rapid invasion previously documented in hyper-invasive glioma. Building on prior research, we suspect that glioma cells could actively influence the electrophysiological patterns of the surrounding neural network, thereby promoting their own migratory behavior. This interaction may lead to the emergence of altered neural firing patterns.

Hijacked neural signals drive hyper-invasive tumor behavior without direct neural engagement
While spontaneous neural network activity has been shown to promote glioma invasion, it alone does not fully account for the rapid invasion patterns observed in hyper-invasive gliomas. Meanwhile, another key observation from our research is that glioma cells not only respond to neural activity but also alter the spontaneous electrophysiological signals of surrounding neurons (Fig. 5a3,5,51,). As illustrated in Fig. 5a, hijacked neural networks exhibited substantial alterations in signal frequency, duration, rise time, and decay time. These changes indicate that tumor cells are capable of modifying the waveform characteristics of neural signals. Furthermore, glioma cells were found to enhance the intensity of gamma (30-100 Hz) and theta (4−8 Hz) oscillations within the neural signals (Fig. 5b). Gamma waves are known to facilitate high-frequency neural oscillations essential for cognitive functions and inter-neuronal communication52. The observed increase in gamma wave activity suggests that glioma cells may enhance electrophysiological synchronization and promote high-frequency firing within neural networks. This hijacking of gamma wave synchronization is indicative of more coordinated and efficient signaling, which could, in turn, facilitate the invasive and metastatic capabilities of glioma cells. The elevated gamma wave activity correlated with increased glioma invasiveness, as high-frequency activity in this band is typically associated with elevated metabolic rates and enhanced cellular proliferation. In addition, the enhanced theta wave activity, generally linked to low-frequency neural processes such as spatial navigation and neuroplasticity, indicates a more profound glioma-neuron interaction. The increased theta wave activity likely supports the establishment of communication pathways that allow glioma cells to navigate more effectively through the neural microenvironment, further promoting their invasive potential. To further dissect the biological basis of hyper-invasive behavior, we conducted molecular profiling and uncovered an enhanced invasion-associated transition marked by E-cadherin downregulation, Vimentin/Snail/Zeb1 upregulation, and concurrent dysregulation of EMT-related mRNAs (CDH1, VIM, SNAI1, ZEB1) (Fig. S8). These invasive molecular hallmarks validate the shift from a non-aggressive to a highly invasive phenotype.

Based on this observation, we propose a hypothesis: glioma cells may hijack neural network signals to accelerate their migration (Fig. S93,5,51). To verify this hypothesis, we extracted the hijacked neural electrophysiological signals and applied them directly to glioma cells to determine whether they could induce the hyper-invasive state of tumor cells. These hijacked neural signals were captured and simulated as described previously for the neural signal extraction and simulation process. As shown in Fig. 5c, d, the neural signal encoding patterns in neurons also underwent significant changes under the influence of glioma cells. New waveform types emerged, while the proportion of certain pre-existing waveforms diminished. To investigate whether these hijacked signals could independently promote glioma invasion, we applied these altered electrophysiological signals to glioma cells in isolation, without the influence of an intact neural network. Remarkably, we observed a significant increase in tumor invasion, comparable to that seen in hyper-invasive conditions (Fig. 5e–g). This finding confirms that glioma cells can indeed hijack neural signals to form new patterns that substantially enhance their invasive capacity. Notably, these effects were induced solely through electrical stimulation, without the involvement of any neural network-derived chemical mediators, suggesting that electrophysiological interactions may play a more fundamental role in promoting glioma invasion than previously understood. These results highlight the critical role of electrical activity in driving tumor progression, emphasizing that the electrophysiological properties of the tumor microenvironment are key facilitators of glioma invasive behavior.

Methods
All procedures involving animals were reviewed and approved by the Animal Ethics Committee of Sun Yat-Sen University (SYSU-IACUC-2021-B0910).

Fabrication of microfluidic electrode array chip
The microfluidic electrode array chip consists of a PDMS (polydimethylsiloxane)-based microchannel system combined with a gold-plated electrode array featuring 32 microelectrodes and integrated micro-gates. The PDMS microchannel was fabricated by casting PDMS pre-polymer (Sylogard 184 Silicone Elastomer Base, Dow Corning Corporation) at a 10:1 base-to-curing-agent ratio onto a micro-fabricated mold with the desired microstructure. The setup was then cured at 60°C for 4 h. After curing, the PDMS was carefully peeled from the mold, preserving the channel features. The resulting microchannel structure serves as both an invasion pathway for glioma cells and a conduit connecting the main cell loading chamber with the microfluidic air pressure control chamber. This chip allows precise regulation of air pressure within the channel, controlling fluid flow to suit experimental requirements. The main cell loading chamber, measuring 5 mm in diameter and 8 mm in height, is open to the atmosphere, enabling direct cell and culture medium loading. Inlet and outlet ports (5 mm and 2 mm in diameter, respectively) were punched at predefined positions to allow fluid control within the microchannel. An optical window was also integrated at the base of the platform to enable continuous microscopic observation of cell migration.

The measurement was performed in the customized device with electrode array on a micro-channel for tumor passing. The electrode array was designed in AutoCAD 2020 (Autodesk, Inc.) and fabricated via a standard photolithography-based lift-off process. Au electrodes (100 μm width × 3 mm length, 200 μm center-to-center spacing) were patterned by depositing a 100 nm-thick gold layer via magnetron sputtering (ISC-150, SuPro Instruments), with the reference electrode placed at the distal end of the channel, 4.6 mm from the last active electrode. Electrophysiological signals were amplified using an RHD-2000 pre-amplifier with a 1000x gain and sampled at 30 kHz to capture both slow local field potentials and rapid spiking neural activity. Calibration experiments include electrode resistance calibration, noise characterization, and inter-device consistency calibration. For each recording channel, baseline noise was measured in a cell-free “blank” culture chamber (identical to experimental chambers but without cells) over a 24-h period. To eliminate device-specific artifacts, three replicate MEA systems (Devices A, B, and C) were cross-validated using a standardized electrical signal source (sinusoidal wave: 100 μV amplitude, 10–500 Hz frequency range). For each device, two key metrics were assessed: signal amplitude deviation (<2% variation across devices after gain correction) and temporal synchronization, where time delays between input and recorded signals were adjusted via FPGA-based timestamp alignment (resulting in residual delays <1 ms). Devices failing to meet these criteria (e.g., >5% amplitude deviation) underwent hardware recalibration (e.g., amplifier gain adjustment) before experimental use. The filtering factor (3–5× SD) is primarily grounded in classical neuroscience experience, with statistical properties of signal noise serving as a supplementary validation. The data acquisition system accommodates up to 512 channels, with synchronized data processing and transmission managed by an FPGA-based module. Noise reduction techniques, including adaptive filtering, were applied to improve signal clarity and reduce mean absolute error. The chip was interfaced with external circuits via a quick-clamp vertical connector to facilitate rapid assembly. For cell culture applications, the assembled device was sterilized, and its surface was treated to promote cell adhesion through oxygen plasma cleaning (PT-BT10 Plus, Sanhoptt Instruments Co., Ltd) for 300 s, followed by coatings of poly-l-lysine (A3890401, Thermo Fisher Scientific) and laminin (L2020, Sigma-Aldrich). The platform’s design allows for long-term experiments, maintaining functionality for up to 2 weeks. Calibration of the system was performed using a standard signal generator (DG1002, Rigol Technologies), and validation experiments with cultured hippocampal neurons confirmed the platform’s capacity to detect both spontaneous action potentials and local field potentials with high fidelity. This design, prioritizing sensitivity and low noise, enables precise, real-time monitoring of neural-glioma interactions during co-culture experiments.

Characterization of microfluidic electrode array chip performance
Sealing integrity of the PDMS culture chamber and micro-grating electrode chip was validated through two sequential tests: (1) a 12 h leak test by injecting 200 μL deionized water into the microfluidic channel, confirming no fluid level changes, and (2) pneumatic pump-driven fluid manipulation monitored in real-time under a microscope (BDS400, Optec Instrument Co., Ltd). Electrode uniformity was verified by measuring resistance across all 32 electrodes (34465A multimeter, Keysight). Signal transmission was tested using sinusoidal inputs (1 Hz/10 Hz, 2 mV; DG1002 generator, Rigol) with three replicates per frequency on three devices. Baseline noise was assessed by recording 10 second DMEM potential fluctuations (200 μL/channel) across three devices under identical conditions. All materials were validated for reproducibility through certified suppliers.

Cell culture and immunofluorescence characterization
Glioblastoma cells (U87MG, U251) were sourced from the American Type Culture Collection (ATCC, Manassas, VA, USA, HTB-14) and SSRCC (China, JY823) and cultured in DMEM/F12 (C11330500BT, Gibco), supplemented with 10% fetal bovine serum (FBS; 10270-106, Gibco), 2.5 mmol/L L-glutamine (25030081, Thermo Fisher Scientific), and 100 U/mL penicillin-streptomycin (15140122, Thermo Fisher Scientific). For real-time tracking, glioblastoma cells were transfected with a GFP expression plasmid using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s protocol. E18 embryos collected from one pregnant female Sprague-Dawley rat (Crl:CD[SD] from Charles River, 8-10 weeks old) were euthanized via CO₂ asphyxiation, embryos decapitated, and brains extracted into ice-cold HBSS (without Ca²⁺/Mg²⁺) with 10 mM HEPES (pH 7.4); hippocampi were microdissected under a stereomicroscope and dissociated via 15 min 37°C incubation in papin, followed by gentle trituration; the cell suspension was centrifuged, resuspended in Neurobasal-A medium (supplemented with 2% B27, 0.5 mM L-glutamine, and 100 U/mL penicillin/streptomycin) for use. Notably, even though GBM does not originate in the hippocampus, evidences have shown that hippocampal neurons do engage in functional interactions with GBM cells1,35, particularly through conserved synaptic and excitatory signaling pathways. Furthermore, hippocampal neural networks represent a uniquely well-characterized model in neurobiology: they encompass both glutamatergic (excitatory) and GABAergic (inhibitory) neuronal phenotypes, and their robust synaptic functionality and high experimental reproducibility have been consistently validated across decades of studies.

Cells were plated at ~70% confluency and transfected with a Lipofectamine 2000-DNA complex. Following transfection, cells were incubated for 24 h to achieve sufficient GFP expression, facilitating visualization during migration studies. Hippocampal neurons were isolated from E18 rat embryos and cultured in Neurobasal medium (21103049, Thermo Fisher Scientific) supplemented with B27 (17504044, Thermo Fisher Scientific) and L-glutamine (25030081, Thermo Fisher Scientific). All cultures were maintained at 37°C in a humidified environment with 5% CO₂ for optimal growth. Prior to experiments, the microfluidic electrode array chip was coated with an adhesion-promoting layer of 1 mg/mL Poly-l-lysine (A3890401, Thermo Fisher Scientific) and 0.5 mg/mL Laminin (L2020, Sigma-Aldrich) to enhance cell attachment to the electrode array. Cells (20,000) were then introduced into the microfluidic channels, and glioblastoma cell migration was tracked under both brightfield and GFP fluorescence modes using a microscope, facilitated by the platform’s optical access. The culture medium within the microfluidic chip was regularly replaced to sustain cell activity, and cell viability was monitored by periodic observation of cellular morphology and electrophysiological signals to ensure normal cellular function.

To visualize neural markers in the co-culture, immunofluorescence staining was performed. Cells were washed twice with phosphate-buffered saline (PBS, BL601A, Biosharp) and fixed in 4% paraformaldehyde (158127, Sigma-Aldrich) for 15 min at 4°C. After fixation, cells were rinsed three times with PBS, and non-specific binding was blocked with 5% bovine serum albumin (BSA, A3294, Sigma-Aldrich). Cells were incubated overnight at 4°C with the primary antibody NeuN (1:500; 12943T, Cell Signaling) in 5% BSA. Following primary antibody incubation, cells were washed five times with PBS, then incubated with a Cy3-conjugated secondary antibody (goat anti-rabbit, 1:500; GB21303, Servicebio) in 5% BSA for 4 h at room temperature in the dark. After a final series of five PBS washes, cells were counterstained with DAPI (C1002, Beyotime) for 10 min to visualize nuclei, followed by five additional PBS rinses. Immunofluorescence imaging was performed using a Nikon AX confocal fluorescence microscope (Nikon Corporation), with excitation wavelengths of 488 nm (for GFP), 550 nm (for Cy3), and 350 nm (for DAPI). This imaging approach enabled high-resolution brightfield and fluorescence visualization of glioblastoma and neural cells in co-culture.

Calcium imaging
Calcium transients in co-cultured glioma and neuronal cells were visualized using calcium-sensitive dyes, Fluo-8 AM (ab142773, Abcam). Cells were incubated with 10 μM Fluo-8 AM dye for 30 min at 37°C in the dark. After incubation, cells were washed three times with phosphate-buffered saline (PBS, BL601A, Biosharp) to remove excess dye, followed by the addition of fresh phenol red-free culture medium (DMEM/F-12, no phenol red, 21041025, Gibco) to facilitate imaging. Fluorescence imaging was performed using a Nikon AX confocal fluorescence microscope (Nikon Corporation) to capture intracellular calcium level changes. Fluctuations were recorded as variations in fluorescence intensity within glioma cells adjacent to the neuron co-culture chamber, enabling visualization of calcium transient responses to neuronal electrophysiological activity. For quantitative analysis, calcium oscillations in both cell types were recorded over a 5 min period. Fluorescence intensity changes were analyzed in ImageJ software (NIH, Bethesda, MD, USA) to assess the temporal dynamics of calcium transients triggered by neuronal signals. Results were plotted to compare intracellular calcium level variations between glioma and neuronal cells under co-culture conditions.

Multi-channel electrode array recording and waveform classification
The electrophysiological activity of neurons was recorded using a custom-designed multi-channel microelectrode array (MEA) system after 10–14 days of culture to ensure mature synaptic connectivity. For each recording session, a sterile micro cover was positioned over the cell culture chamber to maintain a sterile environment throughout the experiment. After connecting the MEA chip to the preamplifier, the system was allowed a 5 min stabilization period before recording. Once the environment reached stability, electrophysiological signals were captured for a duration of 610 s across 32 channels. Signal detection in the MEA system relied on the cell-electrode coupling model, as represented in Fig. S13. Neurons adhered to the electrode surface, separated by a nanoscale gap of culture medium (typically 40–100 nm). Cells adhered to the sensing electrode, separated by a nanoscale culture medium gap (typically 40–100 nm), which created an equivalent circuit of the cell-electrode interface. This interface was modeled with the cell membrane as a parallel combination of a capacitor and a resistor, representing the phospholipid bilayer capacitance and ion channel impedance, respectively. The impedance between the cell membrane and electrode surface, termed R_gap, represented the seal resistance. Signal detection was optimized by adjusting the coupling ratio (V_e/V_m), where V_e denotes the input voltage to the amplifier and V_m the voltage generated by ionic fluctuations at the cell membrane. Neural signals were captured in real time by the MEA system, analyzed for both temporal and frequency characteristics, including low-amplitude fluctuations and high-amplitude spike events. Data were digitized and processed via an FPGA-based acquisition module. Peak waveforms were extracted, initially categorized by amplitude, frequency, and temporal features, and stored in binary format. Raw signals were filtered using a second-order Butterworth low-pass filter with a 3 kHz cutoff to mitigate noise, and non-causal zero-phase filtering was applied to prevent phase distortion. A Savitzky-Golay FIR smoothing filter (polynomial order 5, frame length 501) was subsequently employed to improve signal clarity. A threshold adjustment algorithm was employed to estimate distribution parameters (mean and standard deviation) via maximum likelihood estimation, eliminating artifacts and outliers. This approach ensured accurate noise distribution assessment, thereby improving the reliability of recorded signal analyses.

Neural signal decoding was conducted using an unsupervised waveform classification process, constructing a comprehensive waveform database without predefined categories. Labeled training pool was constructed manually. Initial waveforms were aggregated into a waveform library, where the first waveform served as the standard template for categorization. Common-mode noise was firstly eliminated by subtracting overall signal fluctuations from each recording site. Next, bidirectional application of zero-phase Butterworth filtering (1~3000 Hz) was applied to mitigate phase distortion. The active signals were then extracted and peak-aligned to time 0, calibrating their peak amplitudes to the 0-time reference point, before proceeding to waveform similarity analysis. Waveform similarity comparison and classification were performed by digit-by-digit comparison from the 0-timepoint outward, with label consistency evaluated against a predefined threshold (30%). Waveforms with ≥90% label consistency were grouped into a fixed and unique neural code type. Each new waveform was cross-checked against existing template classes in the neural code library, and unmatched waveforms were designated as new neural code types.

Glioma invasion assay under neural stimulation
The glioma invasion assay in response to neural stimulation was conducted using a custom-designed microfluidic co-culture chip. Neural spontaneous electrophysiological signals were collected from a dedicated device containing pure neural networks (without tumor cells), and required signals were then applied to a separate device with pure tumor cells via the microelectrodes at the bottom of the microfluidic chamber. The electrical stimulation was delivered via integrated microelectrodes within the microfluidic device (recording function of the stimulated microelectrodes was paused during stimulation). The stimulation duration was set to last throughout the entire experimental period, during which the stimulation was continuously applied to the tumor cells to simulate neuron-tumor interactions in co-culture. Prior to cell seeding, the chip surface was coated with 1 mg/mL Poly-l-lysine (A3890401, Thermo Fisher Scientific) and 0.5 mg/mL Laminin (L2020, Sigma-Aldrich) to enhance cell adhesion. Glioma cell migration was monitored over a 7 day period using a microscope (BDS400, Optec Instrument Co., Ltd) with 488 nm excitation to detect GFP-expressing cells. Daily imaging was performed to capture migration progress. Quantitative migration metrics, including invasion distance and the Cell Migration Center (CMC), were analyzed using ImageJ software (NIH, Bethesda, MD, USA). The CMC was introduced as a comprehensive measure of tumor invasion, accounting for the spatial distribution of migrating cells within the microchannel. CMC was calculated using the following formula:

CgMC= \frac{\sum_{i=1}^{n} CN_i \cdot d_i}{\sum_{i=1}^{n} CN_i}

where i represents the electrode serial number, n is the total number of electrodes, CN_i denotes the cell count near electrode i, and d_i is the center-to-center distance between electrodes. Waveform events were extracted, and both temporal and frequency data from neural signals were examined to explore potential interactions between glioma cell invasion and neural activity.

To generate stimulation signals containing neural encoding with valid electrophysiological information, we extracted spatiotemporal sequences of encoded neural events (the types of encoded peaks, their inter-peak time intervals, and the types of subsequent peaks), and then processed these data using a deep learning model (Long Short-Term Memory, LSTM). This model was trained on 46519 extracted signals, capturing both the temporal dependencies and the distribution of waveform types. The LSTM network consisted of two layers, each containing 128 hidden units, enabling the model to learn both short-term and long-term dependencies in the signal data. At each time step, the model received three key features: a one-hot encoded vector representing the neural peak type, the time interval between consecutive peaks as a continuous feature, also historical signal sequences for contextual learning. After passing through the LSTM layers, two fully connected layers were added to process the temporal features further. The output layer was split into two branches: one branch predicted the peak type using a softmax activation function, and the other predicted the time interval between peaks using a ReLU activation function. The model was trained using a multi-task learning approach, with a combined loss function that incorporated categorical cross-entropy for peak type prediction and mean square error (MSE) for time interval prediction. The model was optimized using the Adam optimizer with a learning rate of 0.001, and training was carried out for 50 epochs, employing early stopping to avoid overfitting.

Once trained, the model was used to generate simulated electrophysiological signals. This process began with a randomly selected seed from the dataset, and the model sequentially predicted the peak types and time intervals for each successive signal point. To ensure that the generated signals matched the statistical distribution of the original data, a statistical control mechanism was applied. This mechanism adjusted the predicted peak category distributions and timing based on the observed characteristics of the original dataset. The generation process continued until the simulated signal contained at least 70 occurrences of common code types, ensuring that the distribution of each code type within the signal matched the overall proportions observed in the original dataset. The generated signals were then evaluated using statistical tests, such as the Chi-square test or Kolmogorov-Smirnov (K-S) test, to compare the temporal structure, waveform distribution, and timing patterns between the generated and real signals. Additionally, visual comparisons were made to assess how closely the simulated data resembled the original experimental data. This approach provided a robust framework for simulating neural signals that influence glioma invasion, helping to elucidate the role of neural-tumor crosstalk in cancer progression.

Extraction and application of hijacked neural electrophysiological signals
To extract hijacked neural electrophysiological signals, chips displaying a hyper-invasive state were screened and peak sorting was employed to isolate hijacked neural activity within neuron-glioma co-cultures, focusing on the temporal waveform characteristics of these signals. A total of 127,706 electrophysiological signals were initially extracted, followed by spectral analysis to identify frequency-domain alterations and synchronization patterns indicative of signal hijacking by glioma cells. Further analysis focused on characterizing waveform properties specific to the hijacked signals. To help the model capture the temporal patterns specific to each waveform type, each waveform category is embedded as a low-dimensional feature vector using an embedding layer, which provides a learnable representation for each category and allows the model to better understand inter-category relationships and temporal dependencies. The model architecture consists of a two-layer LSTM network, each layer containing 128 hidden units. This structure is designed to capture both local and global temporal dependencies within the signal. A dropout rate of 0.3 is applied between layers to reduce overfitting. The model is trained using the Adam optimizer with a learning rate of 0.001, and a multi-task output layer is employed. One output branch predicts the next time step’s signal value, applying a ReLU activation to ensure non-negativity, while the other branch estimates the distribution of waveform categories using a Softmax activation. This design allows the model to learn both the dynamic sequence patterns and the probabilistic distribution of waveform categories over time. For training, the model uses a combined loss function: mean squared error (MSE) for signal prediction and categorical cross-entropy for waveform category distribution. This multi-task approach enables the model to simultaneously learn temporal dependencies and categorical distributions, making it capable of generating realistic waveform sequences.

During the generation phase, the model starts from an initial randomly selected seed from the dataset and generates successive waveform points. To ensure the generated data matches the original data’s category proportions and temporal structure, a statistical control mechanism adjusts the predicted category distributions and timing based on the original dataset’s distributional characteristics. Finally, the generated signals are evaluated using statistical tests (e.g., chi-square or K-S tests) and visual comparisons to confirm that the simulated data closely resembles real signals in terms of temporal structure, waveform distribution, and timing patterns. For application, glioma cells were exposed to electrical stimulation patterns that mimicked the hijacked signals, including adjusted waveform proportions and preserved gamma (30–100 Hz) and theta (4–8 Hz) frequency activities. A custom stimulation protocol, informed by the GRU model, was implemented to deliver pulse trains at the identified hijacked signal frequencies and durations observed in co-culture conditions. Following stimulation, invasion assays quantified glioma cell migration and invasion to evaluate the cells’ response to the applied hijacked signals.

Immunoblotting
Cells were lysed in RIPA buffer (PC101, Epizyme) supplemented with protease inhibitors (GRF101, Epizyme). Lysates were centrifuged at 12,000 rpm for 30 min, and the supernatants were collected. Equal amounts of protein were separated by SDS–PAGE and transferred onto polyvinylidene difluoride (PVDF) membranes (E802-01, Vazyme). Membranes were blocked with 5% non-fat milk (3191345, BD) in TBST for 1 h at room temperature, followed by incubation with the indicated primary antibodies overnight at 4°C with gentle shaking. After washing, membranes were incubated with HRP-conjugated secondary antibodies (1:10,000; Abbkine, A21020, A21010) for 1 h at room temperature. Protein bands were visualized using an enhanced chemiluminescence (ECL) detection system (Yeasen) and imaged using a BIO-RAD imaging system. Primary antibodies used in this study included: E-Cadherin (1:1,000, CST, 3195), Vimentin (1:1,000, CST, 5741), Snail (1:1,000, CST, 3879), ZEB1 (1:1,000, CST, 70512), and GAPDH (1:10,000, Abbkine, A01020).

Quantitative RT-PCR
Total RNA from cells was extracted using the FastPure Cell/Tissue Total RNA Isolation Kit V2 (RC112-01, Vazyme). According to the manufacturer’s instructions, cDNA was synthesized using the PrimeScript RT reagent kit with gDNA Eraser (AG11706, Accurate Biology). Real-time quantitative PCR was performed using the SYBR Green Premix Pro Taq HS qPCR Kit (AG11701, Accurate Biology) according to the manufacturer’s instructions. Relative gene expression levels were normalized to GAPDH expression and calculated using the 2-ΔΔCT method. The primers used in this study were synthesized by Tsingke Biotechnology (China) and are listed in Table S1.

Statistics and reproducibility
All quantitative data are presented as mean ± standard error of the mean (SEM), unless otherwise stated. Cellular calcium signal processing was performed using Fiji (ImageJ; National Institutes of Health, Bethesda, MD, USA). Figures were generated using microsoft Office 365 (Microsoft Corporation, Redmond, WA, USA) and OriginLab 2023 (OriginLab Corporation, Northampton, MA, USA). In general, data were tested for normal distribution by Kolmogorov-Smirnov normality test and analyzed accordingly by unpaired two-tailed t-test were used to perform pairwise comparisons between two groups. Each experiment (e.g., migration assay, calcium imaging, or electrophysiological recording) was independently repeated at least three times with consistent results. Details of the data values, sample sizes and statistical measurements are provided in the figure legends.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript the authors develop a novel platform for measuring neural and glioma cell electrical signal patterns while utilizing machine learning algorithms to be able to reliably distinguish between neural-specific and glioma-specific bioelectrical signal patterns. The authors study glioma invasion in vitro with this platform and correlate tumor invasiveness to the specific electrical signal patterns being recorded by their platform. They find that specific neural networks can promote hyper-invasive behavior of brain tumor cells. They first applied neural-specific bioelectrical signals to glioma cells and found a modest increase in invasiveness although not the hyper-invasive phenotype. Next, they stimulated glioma cells with bioelectrical signal pattern of hijacked neural networks where glioma cells had modified the waveform characteristics of the bioelectrical signal patterns of neural cells. They found this glioma-altered neural network signal significantly accelerated tumor cell migration. Interestingly, the electrophysiological signal alone without any neurons was enough to increase tumor invasion.\nThis work is of significance to the field of glioblastoma pathophysiology and the interplay of neuron-glioma circuits. The authors do a good job of describing the current state of the literature in this field in their introduction and their work in this study appropriately expands upon our current knowledge. The platform utilized by the authors in this study is novel and provides key insights into how glioma cells alter the bioelectrical signal pattern of neurons and how these electrical circuits promote glioma invasion. The results and conclusions presented by the authors in this manuscript logically make sense within the context of the current state of the literature and our understanding of how neuron-glioma circuits function. The authors do a good job supporting their claims and conclusions with the experiments and figures provided in this manuscript. The methodology appears sound and definitely the correlation of the authors’ in vitro experiments and immunofluorescence data to the bioelectrical signals recorded is convincing. A reviewer with a stronger background on computational biology should comment on the validity of the authors’ machine learning algorithm calculations. This work meets the expected standards in this field.\nIn general, there is sufficient details provided in the methods for the work to be reproduced, please see suggestions below for some points for improvement. Overall, this is a well-written manuscript that is clear to understand. The author’s present some very interesting results that are convincing based on the experiments completed and also make sense within the larger context of the established literature in this field.\nSuggestions:\n• In the introduction, the authors could clearly state their hypothesis towards the end of this section. The authors talk about on lines 113-114 that mimicking electrophysiological signals can replicate tumor phenotype in vitro. However, they could state their hypothesis here which they actually talk about in their results and discussion section. Something along the lines of “Given our understanding that neuron-glioma interactions are bi-directional, we hypothesize that the electrophysiological signals produced by the interaction between these two cells types will have a greater effect on tumor invasiveness than the electrophysiological signals produced by neural cells alone” or something to that effect which seems to be essentially the central hypothesis of this paper. Presenting that in the introduction could help the reader better frame the results/discussion sections.\n• One experiment to potentially consider is testing different cell lines of glioma cells to determine if there are any differences between cell lines or if the same bioelectrical signaling patterns induce a similar degree of invasiveness across multiple cell lines.\n• Additionally, it may not be too challenging to also look at glioma proliferation especially if all of the glioma cells are tagged with GFP. Measuring change in GFP intensity over time could be used to determine whether the tumors are growing faster as well as being more invasive as I suspect they may be.\n• Authors should provide the glioblastoma cell line or cell lines utilized in the co-culture experiments as it is not immediately clear which cell lines were used and whether these cell lines are derived from mouse, rat, or human.\n• For the experiment described in figure 4-5, the authors state glioma cells were exposed to electrical stimulation patterns but it is unclear how this electrical stimulation is applied to the tumor cells. More details on how the electrical stimulation was applied to the glioma cells could help clarify the methodology. Also, any information regarding duration of stimulation application would be helpful as well and provide further context for this experiment. For instance, was the stimulus provided for a total time of 30minutes or a couple of hours or did it vary?", "reviewer_2": "The authors developed an MEA + machine learning platform to study the interaction between GBM cells and neurons. This new platform can answer important questions in cancer neuroscience. The interesting findings from this novel system are that as long as the authors mimic delivering the neuronal electrical signal to the sole glioma culture, they observed a similar glioma migration-promoting effect as seen in the neuron-glioma co-culture, suggesting that neuronal activity-dependent factors may not be necessary in this phenomenon; however, the biological model system needs to be further developed and validated to support the conclusion.\n- It is unclear which GBM cell line the author used. Detailed information is not included in the methods. Regardless, using only one GBM cell line does not reflect the high degree of heterogeneity present in GBM. Therefore, multiple cell lines, particularly human cell lines, should be tested in this in vitro system.\n- It is unclear whether \"normal state\" and \"hyper-invasive state\" describe the same GBM cell line, just different behaviors in the MEA chambers. Additionally, what criteria are used to define these two states?\n- The authors claim that their system tests GBM cell invasion, which is debatable. The system forces glioma cells to enter the neuron-rich area. Therefore, GBM cell mechanical sensing and their attachment to the bottom both contribute to their migration toward the neurons. The authors should utilize molecular assays to distinguish between cells in the \"normal state\" and the \"hyper-invasive state,\" and further evaluate whether the \"hyper-invasive state\" actually exhibits enriched molecular signatures in cell migration and invasion.\n- Several sentences (lines 262-266, 310-312, 330-331) appeared as if they were novel findings by the authors; however, they had already been shown previously by multiple groups in vivo (Monje, Winkler, Deneen labs). The claims need to be softened, and prior studies need to be cited and discussed.\n- In the methods, it's unclear how hippocampal neurons were isolated from E18 rat embryos. The system is somewhat unusual because GBM rarely forms in the hippocampus. What neuron subtypes are primarily present in this culture?\nMinor comments:\n- In many places, there is no space before the first parenthesis of the reference.\n- Lines 316-318 need citation.\n- line 300, not sure why \"pronounced\" is used.\n- Line 303, not sure what \"hat glioma cells\" means.\n- Fig. 3d should specify which neuron marker is used in the image and/or legend", "reviewer_3": "The manuscript deals with a proposed machine learning-based electrophysiological platform that combines custom microfluidics with real-time decoding of neural-tumor signal interactions. The manuscript claims that glioma cells do not passively respond to neural activity but instead selectively hijack specific neural electrical patterns. The authors claim that this selective entrainment leads to synchronized firing between neurons and tumor cells, which in turn drives hyper-invasive tumor behavior. Furthermore, the authors demonstrate that direct stimulation of glioma cells with these hijacked patterns is sufficient to induce aggressive invasion. The manuscript presents this platform as a novel tool for dissecting dynamic tumor-neural communication and suggests its potential for translational applications targeting bioelectrical signaling in glioma progression.\nThe manuscript is well written and presents a wide variety of experiments and data analytics endeavors. However, it does not typically provide full contextualization and details about the experiments being performed. The developed platform is quite complete, and it is shown that it is capable to precisely acquire signals from glioma and neurons. Despite this, I believe there are some experiments missing regarding the assessment of cell viability, cell-cell interaction and even the claimed effect on tumor cells, as, for example, it was not show if glioma cells would move across the device even if neurons were not present. Moreover, certain concepts like what constitutes “hijacking” or why it certain waveforms might be more relevant than others are not properly explained. Because of this, I believe that further experiments and results must be added in order to reach publishable status, as well as editing on the text to make many of the results presented more clearly understandable.\nPlease, find below a list of comments to be addressed in the next version of the manuscript.\nComments:\n- Page 3, Line 7-11: During Introduction, more context should be provided on the relationship between cancer cell exploitations of ion channels and their “high proliferative rate, migratory behavior, and invasiveness(14).”. Without further contextualization, it does not become clear why, for example, mutations in ion channels of 90% of gliobastoma should be related to aggressiveness.\n- Page 3, Line 21-22: When stating that “excitatory postsynaptic currents (EPSCs), (lead) to glioma cell membrane depolarization and promoting tumor proliferation”, it would help readers if more details about glioma as a disease, its onset, how does it typically expand and what are some of its key proliferative mechanisms, for example.\n- Page 5, Line 22-25 (and Method section): Can the authors provide further information regarding the culturing conditions, specifically, viability assessment along experimental time and typical numbers of cultured cells? Also, did the authors assess the level of adhesion of cells to the coated electrodes, i.e., was it assessed if all cells or only a small fraction of the seeded cells stayed within the device?\n- Page 7 – Can the authors provide more information regarding how measurements were performed and how the electrode array was designed? Also, further details on calibration experiments or efforts to understand how the signals might vary under different conditions would be valuable. Moreover, on the filtering criteria provided, how is the factor applied determined?\n- Page 8 – Can the authors report on control experiments where the neurons where not initially present on the chamber before glioma cells are added? It would be critical to confirm if the presence of the neurons within the chamber has an effect on the movement observed.\n- Page 8 – It is not clear to me what constitutes the “hyper-invasive behavior”. How do the authors identify this behavior? What metrics, signals or other, are used to classify them as such?\n- Page 8 – Can the authors comment on the ratio of glioma cells that started movement versus the population that stayed within the input sample? Figure 3 does not make it clear if all the cells or only a small fraction actually initiated movement across the device.\n- Page 8/9 – Can the authors comment on the observed movement of glioma cells, i.e., if their movement followed any specific trend, sequence, morphological change or any other behavior that could also be important to highlight?\n- Page 8/9 – During the 7 day experiment, how was viability of cells ensured if the liquid within the chamber was stopped in order to ensure no further movement induced into the channel?\n- Page 9 – It is confusing to use “i” as a variable for both the electrode serial number and the center-to-center distance of electrodes. Also, it is not clear the difference between “i” and “d” on that regard? Moreover, the authors should provide, even if in supplementary and for a small set of experiments, a comparison between the classical approach highlighted of individual cell tracking and their proposed approach, in order to showcase the differences and potential advantages of the proposed method.\n- Page 10 – Figure 4B – It is not very clear what is a neuron versus glioma cell in the images provided. For example, it seems that the Calcium trigger goes from Neuron (1) to Glioma (2) around the Glioma (3), while at the same time there are other cells (?)/locations with high signal as well that are not annotated or receive any comments. I would ask the authors to better annotate the Figure, as well as providing a better discussion of the phenomena being observed in it.\n- Page 11, Line 2 – Can the authors comment why the synchronized firing might have went from Neuron (1) to Glioma (2) to (3), instead of (1) to (3), as Glioma (3) is much closer to Neuron (1)? Also, why does Glioma (3) have less firing events compared with the other cells? And can the authors analyze and provide the data measured at other cells that were found surrounding these 3 cells? Is this firing effect short and local, or does it propagate further into other cells?\n- Page 11 – Line 22 – The authors must properly explain how the “neural codes” were identified and distinguished into more than 1000 types. It is not clear what metrics were used to compare them or how they were evaluated. Also, how was comparison across different experiments and observed glioma behaviors for ex?\n- Pag 12 – Figure 4i – I am unfamiliar with the type of plot utilized to compare the simulated signal with the nerve stimulation signal. Can the authors comment on what is being evaluated and how is the comparison done? Also, the authors do not provide an explanation on why the top 70 code types specifically were chosen for comparison.\n- Page 12 – Line 12 onwards – It is not made clear how the neural spontaneous electrophysiological signals were applied to glioma cells, as there is not previous explanation on how these signals could be generated and applied to the cells.\n- Page 12/13 – It is not very clear to me what the term “hijacking” constitutes experimentally, i.e., the authors claim that the neural networks are hijacked by glioma cells, however it is not made clear at what point in time does this effectively occur or what are the conditions to be achieved in order to consider that a certain network has been hijacked. Could that metric be a ratio in number of glioma cells versus neurons, or a specific number of physical connections between the 2 types of cells?\n- Page 13 – Figure 5B – Why are the gamma and theta oscillation results presented as equal or very similar normal distributions to then present a percentage value? Can the authors report on an actual mean and std, as well as statistical analysis of this difference?\nMinor:\n- The resolution and quality of Figures is not appropriate. There are also many cases where the graphs presented have no information on axis, units and further details about the data and statistics. Examples: Figure 3i, Figure 4d, e, f, g, h, l\n- Page 10, Line 20 – Typo “calcium hannel-“, instead of “calcium channel”."}, "author_response": "We sincerely appreciate your time and the positive comments of our work. Our point-by-point responses are detailed below.\nSuggestions:\n• In the introduction, the authors could clearly state their hypothesis towards the end of this section. The authors talk about on lines 113-114 that mimicking electrophysiological signals can replicate tumor phenotype in vitro. However, they could state their hypothesis here which they actually talk about in their results and discussion section. Something along the lines of “Given our understanding that neuron-glioma interactions are bi-directional, we hypothesize that the electrophysiological signals produced by the interaction between these two cells types will have a greater effect on tumor invasiveness than the electrophysiological signals produced by neural cells alone” or something to that effect which seems to be essentially the central hypothesis of this paper. Presenting that in the introduction could help the reader better frame the results/discussion sections.\nOur deepest gratitude for your careful reviewing and thoughtful suggestions. In the revised manuscript, we have added a state of the hypothesis at the end of the introduction to help the reader better frame the results/discussion sections. (page 6, lines 144-148)\n• One experiment to potentially consider is testing different cell lines of glioma cells to determine if there are any differences between cell lines or if the same bioelectrical signaling patterns induce a similar degree of invasiveness across multiple cell lines.\nWe sincerely appreciate your suggestion. In the revised manuscript, we introduced a new human glioma cell line (U251) to investigate whether bioelectrical signaling patterns consistently promote tumor invasiveness across different cell lines. Our finding indicated that while U87-associated bioelectrical signals still moderately promoted tumor invasion, however, recapitulating the invasion intensity observed in co-culture systems necessitated re-extracting co-culture-derived electrical signals from U251, highlighting variability in bioelectrical signaling patterns among different cell lines (Fig. S11). Related discussion has been added on pages 18-19, lines 458-465.\nFigure S11. Invasive behavior of human glioma U251 cells under four distinct bioelectrical conditions (Control, U87-hijacked signals, co-culture neural signals, or U251-hijacked signals).\n• Additionally, it may not be too challenging to also look at glioma proliferation especially if all of the glioma cells are tagged with GFP. Measuring change in GFP intensity over time could be used to determine whether the tumors are growing faster as well as being more invasive as I suspect they may be.\nWe sincerely thank the reviewer for this insightful suggestion regarding the assessment of glioma proliferation dynamics using GFP-tagged cells. Following this valuable input, we performed longitudinal quantification of GFP intensity to monitor glioma cell proliferation dynamics, which further validated that tumors exhibit not only increased invasiveness but also accelerated growth rates (Fig S10). Relevant discussion has been added on page 18, lines 456-458.\nFigure S10. Cell proliferation comparison between control and hijacked cells.\n• Authors should provide the glioblastoma cell line or cell lines utilized in the co-culture experiments as it is not immediately clear which cell lines were used and whether these cell lines are derived from mouse, rat, or human.\nWe sincerely appreciate the reviewer’s careful attention to this detail. In the revised manuscript, we have provided information on page 22, line 544. The glioblastoma cell line utilized in our experiments is U87 MG, a well-characterized human-derived glioblastoma cell line.\n• For the experiment described in figure 4-5, the authors state glioma cells were exposed to electrical stimulation patterns but it is unclear how this electrical stimulation is applied to the tumor cells. More details on how the electrical stimulation was applied to the glioma cells could help clarify the methodology. Also, any information regarding duration of stimulation application would be helpful as well and provide further context for this experiment. For instance, was the stimulus provided for a total time of 30minutes or a couple of hours or did it vary?\nThanks for the comment. In the revised manuscript we have added more details on how electrical stimulation was applied to the glioma cells on page 27, lines 665-670.\nThe electrical stimulation was delivered via integrated microelectrodes within the microfluidic device (recording function of the stimulated microelectrodes was paused during stimulation). The stimulation duration was set to last throughout the entire experimental period, during which the stimulation was continuously applied to the tumor cells to simulate neuron-tumor interactions in co-culture.\nReviewer: #2\nThe authors developed an MEA + machine learning platform to study the interaction between GBM cells and neurons. This new platform can answer important questions in cancer neuroscience. The interesting findings from this novel system are that as long as the authors mimic delivering the neuronal electrical signal to the sole glioma culture, they observed a similar glioma migration-promoting effect as seen in the neuron-glioma co-culture, suggesting that neuronal activity-dependent factors may not be necessary in this phenomenon; however, the biological model system needs to be further developed and validated to support the conclusion.\nWe sincerely thank you for the time and careful consideration in reviewing our manuscript. We have revised the manuscript accordingly. Our point-by-point responses are detailed below.\n- It is unclear which GBM cell line the author used. Detailed information is not included in the methods. Regardless, using only one GBM cell line does not reflect the high degree of heterogeneity present in GBM. Therefore, multiple cell lines, particularly human cell lines, should be tested in this in vitro system.\nThanks for the helpful suggestions. In the revised manuscript, we have supplemented the cell source information in the methods section (page 22, lines 544). The glioblastoma cell line utilized in our experiments is U87 MG, a well-characterized human-derived glioblastoma cell line.\nFurthermore, we incorporated an additional human glioma cell line (U251) into our experiments to validate the robustness of our primary findings. These data confirmed that neuron-glioma electrical signaling indeed enhances glioma migration (Fig. S11), consistent with our original observations. The updated results and corresponding analyses are presented in the revised manuscript at page 18, lines 458-465.\nFigure S11. Invasive behavior of human glioma U251 cells under four distinct bioelectrical conditions (Control, U87-hijacked signals, co-culture neural signals, or U251-hijacked signals).\n- It is unclear whether \"normal state\" and \"hyper-invasive state\" describe the same GBM cell line, just different behaviors in the MEA chambers. Additionally, what criteria are used to define these two states?- The authors claim that their system tests GBM cell invasion, which is debatable. The system forces glioma cells to enter the neuron-rich area. Therefore, GBM cell mechanical sensing and their attachment to the bottom both contribute to their migration toward the neurons. The authors should utilize molecular assays to distinguish between cells in the \"normal state\" and the \"hyper-invasive state,\" and further evaluate whether the \"hyper-invasive state\" actually exhibits enriched molecular signatures in cell migration and invasion.\nWe appreciate your comments. We agree that further molecular analyses are needed to discriminate between these two functional states. In the revised manuscript, we have supplemented molecular characterization of the \"normal state\" and \"hyper-invasive state,\" including detailed profiling of hyper-invasive behavior through differential expression in invasion-related proteins and RNAs. Molecular analyses (Fig. S8) revealed an invasion-associated transition characterized by downregulated E-cadherin, upregulated Vimentin/Snail/Zeb1, and corresponding dysregulated EMT-related mRNAs (CDH1, VIM, SNAI1, ZEB1). These molecular hallmarks of invasiveness confirm the transition from a non-aggressive to a highly invasive state. Relevant discussion has been added on page 16, lines 402-407.\nFigure S8. Invasion-related molecular signatures in hyper-invasive vs. control cells.\n- Several sentences (lines 262-266, 310-312, 330-331) appeared as if they were novel findings by the authors; however, they had already been shown previously by multiple groups in vivo (Monje, Winkler, Deneen labs). The claims need to be softened, and prior studies need to be cited and discussed.\nWe sincerely appreciate your careful review of these specific sentences. In the revised manuscript, we have softened the claims by citing the prior studies from Monje, Winkler, and Deneen labs. (lines 328, 383, 409).\n- In the methods, it's unclear how hippocampal neurons were isolated from E18 rat embryos. The system is somewhat unusual because GBM rarely forms in the hippocampus. What neuron subtypes are primarily present in this culture?\nThank you for your careful review of the methods section. We have revised the \"Neuron Isolation and Culture\" subsection (Page 22, Lines 551-557) to provide detailed protocols for hippocampal neuron isolation. Timed-pregnant E18 Sprague-Dawley rats were euthanized via CO₂ asphyxiation, embryos decapitated, and brains extracted into ice-cold HBSS (without Ca²⁺/Mg²⁺) with 10 mM HEPES (pH 7.4); hippocampi were microdissected under a stereomicroscope and dissociated via 15-minute 37°C incubation in papin, followed by gentle trituration; the cell suspension was centrifuged, resuspended in Neurobasal-A medium (supplemented with 2% B27, 0.5 mM L-glutamine, and 100 U/mL penicillin/streptomycin) for use.\nNotably, even though GBM does not originate in the hippocampus, evidences have shown that hippocampal neurons do engage in functional interactions with GBM cells, and many relevant [1,2] studies typically involve implanting tumors into the hippocampus. Furthermore, hippocampal neural networks represent a uniquely well-characterized model in neurobiology: they encompass both glutamatergic (excitatory) and GABAergic (inhibitory) neuronal phenotypes, and their robust synaptic functionality and high experimental reproducibility have been consistently validated across decades of studies. We have expanded the Discussion section to explicitly note this context on page 22, lines 557-564. Thank you again for prompting this clarification.\nReferences:\n[1] Venkatesh, H.S., Morishita, W., Geraghty, A.C. et al. Electrical and synaptic integration of glioma into neural circuits. Nature 573, 539–545 (2019).\n[2] Krishna, S., Choudhury, A., Keough, M.B. et al. Glioblastoma remodelling of human neural circuits decreases survival. Nature 617, 599–607 (2023).\nMinor comments:\n- In many places, there is no space before the first parenthesis of the reference.\n- Lines 316-318 need citation.\n- line 300, not sure why \"pronounced\" is used.\n- Line 303, not sure what \"hat glioma cells\" means.\n- Fig. 3d should specify which neuron marker is used in the image and/or legend\nThank you for your valuable comments. We have systematically checked the manuscript and added spaces before all reference parentheses to ensure formatting consistency.\nLines 316–318 citation: A relevant citation has been added to support this statement (line 390).\nLine 300 \"pronounced\": We have changed “pronounced” to “less striking”. So the text will be \"However, this observed increase in invasion was less striking than...\" .\nLine 303 \"hat glioma cells\": This was a typo and has been corrected to \"that glioma cells\".\nFig. 3d neuron marker: The legend for Fig. 3d has been updated to specify that the neuron marker (NeuN) used.\nWe appreciate your attention to these details, which have helped improve the clarity and rigor of our manuscript.\nReviewer: #3\nThe manuscript deals with a proposed machine learning-based electrophysiological platform that combines custom microfluidics with real-time decoding of neural-tumor signal interactions. The manuscript claims that glioma cells do not passively respond to neural activity but instead selectively hijack specific neural electrical patterns. The authors claim that this selective entrainment leads to synchronized firing between neurons and tumor cells, which in turn drives hyper-invasive tumor behavior. Furthermore, the authors demonstrate that direct stimulation of glioma cells with these hijacked patterns is sufficient to induce aggressive invasion. The manuscript presents this platform as a novel tool for dissecting dynamic tumor-neural communication and suggests its potential for translational applications targeting bioelectrical signaling in glioma progression.\nThe manuscript is well written and presents a wide variety of experiments and data analytics endeavors. However, it does not typically provide full contextualization and details about the experiments being performed. The developed platform is quite complete, and it is shown that it is capable to precisely acquire signals from glioma and neurons. Despite this, I believe there are some experiments missing regarding the assessment of cell viability, cell-cell interaction and even the claimed effect on tumor cells, as, for example, it was not show if glioma cells would move across the device even if neurons were not present. Moreover, certain concepts like what constitutes “hijacking” or why it certain waveforms might be more relevant than others are not properly explained. Because of this, I believe that further experiments and results must be added in order to reach publishable status, as well as editing on the text to make many of the results presented more clearly understandable. Please, find below a list of comments to be addressed in the next version of the manuscript.\nWe sincerely appreciate your time and the valuable comments of our work. We have revised the manuscript based on your suggestions, and our point-by-point responses to your comments are detailed in the following sections.\nComments:\n- Page 3, Line 7-11: During Introduction, more context should be provided on the relationship between cancer cell exploitations of ion channels and their “high proliferative rate, migratory behavior, and invasiveness(14).”. Without further contextualization, it does not become clear why, for example, mutations in ion channels of 90% of gliobastoma should be related to aggressiveness.\nWe thank the reviewer for this valuable comment. We have expanded the Introduction to clarify how ion channel exploitation drives cancer hallmarks on page 3, lines 74-88.\nMechanistically, ion channels regulate proliferation via membrane potential/ionic homeostasis control of cell cycle checkpoints (e.g., voltage-gated sodium channels maintain glioblastoma stemness in G0 phase) [1,2], while calcium-activated potassium channel (BK) correlate with glioma grade and directly regulate growth [3]. For migration/invasion, coordinated K⁺/Cl⁻ efflux via BK channels enables volume shrinkage critical for navigating brain tissue [4,5], with peripheral glioblastoma cells showing calcium transient-synchronized collective invasion [6]. Notably, glioblastomas exhibit pathological ion channel reprogramming (e.g., near-complete Kir4.1 loss and BK upregulation) [7], linking 90% ion channel mutations to aggressive phenotypes through disrupted differentiation and enhanced plasticity [8].\nReferences:\n1. Prevarskaya, N., R. Skryma, and Y. Shuba, Ion Channels in Cancer: Are Cancer Hallmarks Oncochannelopathies? Physiol Rev, 2018. 98(2): p. 559-621.\n2. Giammello, F., et al., Modulating voltage-gated sodium channels to enhance differentiation and sensitize glioblastoma cells to chemotherapy. Cell Commun Signal, 2024. 22(1): p. 434.\n3. Michelucci, A., et al., Hypoxia, Ion Channels and Glioblastoma Malignancy. Biomolecules, 2023. 13(12).\n4. Soroceanu, L., T.J. Manning, Jr., and H. Sontheimer, Modulation of glioma cell migration and invasion using Cl(-) and K(+) ion channel blockers. J Neurosci, 1999. 19(14): p. 5942-54.\n5. McFerrin, M.B. and H. Sontheimer, A role for ion channels in glioma cell invasion. Neuron Glia Biol, 2006. 2(1): p. 39-49.\n6. Xu, M., et al., A temporal examination of calcium signaling in cancer- from tumorigenesis, to immune evasion, and metastasis. Cell Biosci, 2018. 8: p. 25.\n7. Liu, J., et al., Potassium channels and their role in glioma: A mini review. Mol Membr Biol, 2019. 35(1): p. 76-85.\n8. Hanahan, D., Hallmarks of Cancer: New Dimensions. Cancer Discov, 2022. 12(1): p. 31-46.\n- Page 3, Line 21-22: When stating that “excitatory postsynaptic currents (EPSCs), (lead) to glioma cell membrane depolarization and promoting tumor proliferation”, it would help readers if more details about glioma as a disease, its onset, how does it typically expand and what are some of its key proliferative mechanisms, for example.\nThank you for your valuable suggestion. We have expanded the description of glioma as a disease and its key proliferative mechanisms in the revised manuscript (Page 4, Lines 101-112).\nGliomas are primary malignant CNS tumors with high invasiveness, recurrence, and poor prognosis. Onset involves genetic/epigenetic dysregulation, and key proliferative mechanisms include dysregulated signaling cascades (e.g., PI3K/Akt/mTOR, MAPK/ERK), ion channel dysfunction, and tumor microenvironment (TME) remodeling. Critically, Venkataramani et al. [1] identified functional glutamatergic synapses (\"neurogliomal synapses\") between neurons and glioma cells: presynaptic glutamate release activates postsynaptic AMPA receptors on glioma cells, triggering excitatory postsynaptic currents (EPSCs), membrane depolarization, and calcium influx [2]. This neuronal-glioma signaling directly promotes GSC proliferation and invasion, a mechanism validated by preclinical studies showing AMPA receptor antagonists inhibit tumor growth.\nReferences\n1. Venkataramani, V., Tanev, D.I., Strahle, C. et al. Glutamatergic synaptic input to glioma cells drives brain tumour progression. Nature 573, 532–538 (2019).\n2. Venkatesh, H.S., Morishita, W., Geraghty, A.C. et al. Electrical and synaptic integration of glioma into neural circuits. Nature 573, 539–545 (2019).\n- Page 5, Line 22-25 (and Method section): Can the authors provide further information regarding the culturing conditions, specifically, viability assessment along experimental time and typical numbers of cultured cells? Also, did the authors assess the level of adhesion of cells to the coated electrodes, i.e., was it assessed if all cells or only a small fraction of the seeded cells stayed within the device?\nWe sincerely appreciate your constructive comments. Further information has been provided in the revised manuscript. Specifically, the typical number of cultured cells was 10,000 and viability assessment has been supplemented in Fig. S4 (which showed that the cell survival rate remained above 80% after 7 days of culture). Our observations also confirmed that almost all seeded cells were retained within the device. Related discussion has been provided on page 10, lines 257-258.\nFigure S4. Cell viability after 7 days of culture. Green: Active cells expressing GFP; Blue: DAPI staining. Scale bar: 200 μm.\n- Page 7 – Can the authors provide more information regarding how measurements were performed and how the electrode array was designed? Also, further details on calibration experiments or efforts to understand how the signals might vary under different conditions would be valuable. Moreover, on the filtering criteria provided, how is the factor applied determined?\nWe thank the reviewer for these questions. The measurement was performed in the customized device fitted with an array of electrodes on microchannels. The electrode array (Fig. 2b) was designed in AutoCAD 2020 (Autodesk, Inc.) and fabricated via a standard photolithography-based lift-off process. Au electrodes (100 μm width × 3 mm length, 200 μm center-to-center spacing) were patterned by depositing a 100 nm-thick gold layer via magnetron sputtering (ISC-150, SuPro Instruments), with the reference electrode placed at the distal end of the channel, 4.6 mm from the last active electrode. Electrophysiological signals were amplified using a customized capture system with a 1000x gain and sampled at 30 kHz to capture both slow local field potentials and rapid spiking neural activity.\nCalibration experiments included electrode resistance calibration, noise characterization, and inter-device consistency calibration. For each recording channel, baseline noise was measured in a cell-free \"blank\" culture chamber (identical to experimental chambers but without cells) over a 24-hour period. To eliminate device-specific artifacts, three replicate MEA systems (Devices A, B, and C) were cross-validated using a standardized electrical signal source (sinusoidal wave: 100 µV amplitude, 10–500 Hz frequency range). For each device, two key metrics were assessed: signal amplitude deviation (<2% variation across devices after gain correction) and temporal synchronization, where time delays between input and recorded signals were adjusted via FPGA-based timestamp alignment (resulting in residual delays <1 ms). Devices failing to meet these criteria (e.g., >5% amplitude deviation) underwent hardware recalibration (e.g., amplifier gain adjustment) before experimental use. The filtering factor (3–5×SD) is primarily grounded in classical neuroscience experience, with supplementary validation from the statistical properties of signal noise. All related information has been provided on pages 20-21, lines 493-515.\n- Page 8 – Can the authors report on control experiments where the neurons where not initially present on the chamber before glioma cells are added? It would be critical to confirm if the presence of the neurons within the chamber has an effect on the movement observed.\nWe thank the reviewer for the question. In the revised manuscript, we have further discussed the control experiment data for the scenario where neurons were not initially present in the chamber. The results indicate that glioma cells exhibit a baseline invasive behavior in the absence of neurons (defined as \"Normal state\", Fig. 3i). Notably, after the addition of neurons, the invasion rate of tumor cells was observed to increase significantly (defined as “Hyper-Invasive”, Fig. 3i). This control experiment validates the effect of neuron presence on tumor movement.\nFigure 3i. Migration behavior (cell migration center) of glioma cells in neuron-free (Normal state) vs. neuron-seeded (Hyper-Invasive state).\n- Page 8 – It is not clear to me what constitutes the “hyper-invasive behavior”. How do the authors identify this behavior? What metrics, signals or other, are used to classify them as such?\nThank you for your question. To define \"hyper-invasive behavior,\" we first established baseline collective invasion velocity of tumor cells under microenvironmental conditions devoid of extrinsic stimuli, quantified by tracking cell migration dynamics over time. This baseline was operationally defined as \"normal invasive behavior.\" Upon exposure to pro-invasive cues (e.g., neuron-derived signaling or electrical stimulation), we observed a statistically significant increase in collective invasion velocity compared to baseline; this phenotype was designated \"hyper-invasive behavior.\" Furthermore, hyper-invasive behavior was characterized by molecular signatures (page 16, lines 402-407), including enhanced epithelial-mesenchymal transition (EMT) (downregulated E-cadherin, upregulated Vimentin/Snail/Zeb1), and dysregulated expression of EMT-related mRNAs (CDH1, VIM, SNAI1, ZEB1, Fig. S8). These molecular signatures have been well-documented to correlate with tumor invasiveness, supporting the phenotypic shift from a quiescent to an aggressively invasive state.\nFigure S8. Invasion-related molecular signatures in hyper-invasive vs. control cells.\n- Page 8 – Can the authors comment on the ratio of glioma cells that started movement versus the population that stayed within the input sample? Figure 3 does not make it clear if all the cells or only a small fraction actually initiated movement across the device.\nThank you for your question. Not all glioma cells traverse the device, while only a subset completes migration and the majority persists near the primary tumor site or along the migration path. However, precise quantification of the motile-to-retained cell ratio in input samples remains challenging due to continuous tumor cell proliferation and migration causing dynamic temporal fluctuations in total and migratory counts, which are further complicated by delayed invasion (movement initiating at later rather than immediate time points).\n- Page 8/9 – Can the authors comment on the observed movement of glioma cells, i.e., if their movement followed any specific trend, sequence, morphological change or any other behavior that could also be important to highlight?\nThank you for your question. Observations showed that glioma cells primarily exhibited faster invasion in hyper-state, however, their migratory behavior did not exhibit distinct trends, sequential patterns. Although no significant changes in cell morphology or invasion patterns were detected, molecular analyses (Fig. S8) demonstrated an invasion-associated transition—characterized by downregulated E-cadherin and upregulated Vimentin, Snail, and Zeb1—accompanied by dysregulated expression of EMT-related mRNAs (CDH1, VIM, SNAI1, ZEB1). These molecular signatures have been well-documented to correlate with tumor invasiveness, supporting the phenotypic shift from a quiescent to an aggressively invasive state.\n- Page 8/9 – During the 7 day experiment, how was viability of cells ensured if the liquid within the chamber was stopped in order to ensure no further movement induced into the channel?\nThank you for your comment. To maintain cell viability during the 7-day experiment, we periodically replaced half of the culture medium within the microfluidic chamber every 6–12 hours, which effectively ensured cell survival despite the cessation of liquid flow to prevent additional movement in the channel. As shown in Fig. S4, viability assessment showed that the cell survival rate remained above 80% after 7 days of culture. Related discussion has been provided on page 10, lines 258-259.\nFigure S4. Cell viability after 7 days of culture. Green: Active cells expressing GFP; Blue: DAPI staining. Scale bar: 200 μm.\n- Page 9 – It is confusing to use “i” as a variable for both the electrode serial number and the center-to-center distance of electrodes. Also, it is not clear the difference between “i” and “d” on that regard? Moreover, the authors should provide, even if in supplementary and for a small set of experiments, a comparison between the classical approach highlighted of individual cell tracking and their proposed approach, in order to showcase the differences and potential advantages of the proposed method.\nThank you for your question. We apologize for any confusion caused by previous description, and we have provided more detailed information in the revised manuscript (page 10, lines 268-272). Specifically, the variable \"i\" is exclusively defined as the electrode serial number, while \"d\" denotes the center-to-center distance between adjacent electrodes (200 μm in our device).\nBased on the reviewers' comments, we have also added a comparison between the classical single-cell tracking approach and our proposed population-level analysis. Compared to traditional single-cell tracking, this population-level analysis largely reduces data variability due to high intercellular differences in migration speed within the tumor population (Fig. S5).\nFigure S5. Comparison of Standard Error between population and single-cell tracking.\n- Page 10 – Figure 4B – It is not very clear what is a neuron versus glioma cell in the images provided. For example, it seems that the Calcium trigger goes from Neuron (1) to Glioma (2) around the Glioma (3), while at the same time there are other cells (?)/locations with high signal as well that are not annotated or receive any comments. I would ask the authors to better annotate the Figure, as well as providing a better discussion of the phenomena being observed in it. - Page 11, Line 2 – Can the authors comment why the synchronized firing might have went from Neuron (1) to Glioma (2) to (3), instead of (1) to (3), as Glioma (3) is much closer to Neuron (1)? Also, why does Glioma (3) have less firing events compared with the other cells? And can the authors analyze and provide the data measured at other cells that were found surrounding these 3 cells? Is this firing effect short and local, or does it propagate further into other cells?\nThank you for your question. Following the reviewer’s suggestion, we have enhanced the figure annotations and expanded a detailed discussion on page 13, line 316-326 in the revised manuscript. Please note that Cell 2 and Cell 3 in the original figure have been renumbered as Cell 3 and Cell 4 in the revised version, and subsequent discussions will refer to the updated numbering.\nAs shown in Fig. 4b, Cell 1 initially exhibits firing, which propagates to Cell 2, then subsequently to Cells 3 and 5. Upon simultaneous stimulation from Cells 1 and 3, Cell 4—which appears to have a higher activation threshold—is activated and engages in co-firing with Cells 1 and 3. It should be noted that neural network conduction is not solely determined by spatial distance but also depends on signal intensity and inherent cellular electrophysiological thresholds. It can be observed that Cell 4 itself exhibits reduced firing events, suggesting it may have a higher activation threshold and consequently lower excitability. Consequently, despite Cell 4's closer proximity to Cell 1, the signal preferentially activates Cell 3, and only the convergent stimulation from Cell 1 and Cell 3 does Cell 4 become activated and functionally incorporated into the form co-firing network. Also, as suggested by the reviewer, we provided electrical signals from cell 2 and cell 5 (Fig.S7), no obvious co-firing was identified with Cell1, indicating these firing effects are locally transient with no further propagation.\nFigure 4b. Cellular calcium signal transduction diagram\nFigure S7. Signal display of Cells 1, 2, and 5.\n- Page 11 – Line 22 – The authors must properly explain how the “neural codes” were identified and distinguished into more than 1000 types. It is not clear what metrics were used to compare them or how they were evaluated. Also, how was comparison across different experiments and observed glioma behaviors for ex?\nWe appreciate the valuable suggestions. In the revised manuscript, we have updated the explanation of how the “neural codes” were identified and distinguished on pages 26, lines 648-658.\nNeural codes were identified from electrophysiological activities exceeding baseline levels during functional behaviors of cells, with similar waveforms grouped into fixed and unique neural code types to enable consistent tracking across experiments and comparison of temporal usage patterns under varying conditions. Waveforms were classified as follows: Common-mode noise was firstly eliminated by subtracting overall signal fluctuations from each recording site. Next, bidirectional application of zero-phase Butterworth filtering (1~3000Hz) was applied to mitigate phase distortion. The active signals were then extracted and peak-aligned to time 0, calibrating their peak amplitudes to the 0-time reference point, before proceeding to waveform similarity analysis. Waveform similarity comparison and classification were performed by digit-by-digit comparison from the 0-time point outward, with label consistency evaluated against a predefined threshold (10%). Waveforms with ≥90% label consistency were grouped into a fixed and unique neural code type. Each new waveform was cross-checked against existing template classes in the neural code library, and unmatched waveforms were designated as new neural code types. This integrated methodology enabled the identification of over 10000 neural code types and facilitated reliable cross-experimental comparisons.\n- Pag 12 – Figure 4i – I am unfamiliar with the type of plot utilized to compare the simulated signal with the nerve stimulation signal. Can the authors comment on what is being evaluated and how is the comparison done? Also, the authors do not provide an explanation on why the top 70 code types specifically were chosen for comparison.\nThank you for your valuable comment. Figure 4i presents the relative occurrence frequency of code types with the most significant changes and visualizes their proportional distribution via a pie chart, enabling clear comparison of proportional structure changes across conditions. The top 70 code types were selected as they collectively account for most of the total variation, balancing comprehensiveness (capturing major changes) and figure readability, thus adequately representing overall trends of interest. Related discussions have been provided in the revised manuscript on page 14, lines 364-365.\n- Page 12 – Line 12 onwards – It is not made clear how the neural spontaneous electrophysiological signals were applied to glioma cells, as there is not previous explanation on how these signals could be generated and applied to the cells.\nThank you for the comment. Neural spontaneous electrophysiological signals were collected from a dedicated device containing pure neural networks (without tumor cells), and required signals were then applied to a separate device with pure tumor cells via the microelectrodes at the bottom of the microfluidic chamber. Thus, we can evaluate the effect of pure neural spontaneous electrophysiological signals on tumor behavior. Related information has been added on page 26, line 661-670.\n- Page 12/13 – It is not very clear to me what the term “hijacking” constitutes experimentally, i.e., the authors claim that the neural networks are hijacked by glioma cells, however it is not made clear at what point in time does this effectively occur or what are the conditions to be achieved in order to consider that a certain network has been hijacked. Could that metric be a ratio in number of glioma cells versus neurons, or a specific number of physical connections between the 2 types of cells?\nWe thank the reviewer for your professional suggestions. In the revised manuscript, we have systematically evaluated glioma cell behavior across varying glioma-to-neuron ratios to determine a metric ratio in number of glioma cells versus neurons. As shown in Fig. S12, the enhanced hyper activity of the tumor increased with the increase in the proportion of glioma cells to neurons. When the ratio was 1:1, the enhanced hyper activity of the tumor reached the highest level. Then, as the proportion of neurons further increased, this \"hijacking\" state gradually disappeared. Related discussions have been provided in the revised manuscript on page 19, lines 465-470.\n100\n75\n50\n25\n0\n1:10 1:2 1:1 2:1 10:1\nFigure S12. Glioma cell behavior across varying glioma-to-neuron ratios.\n- Page 13 – Figure 5B – Why are the gamma and theta oscillation results presented as equal or very similar normal distributions to then present a percentage value? Can the authors report on an actual mean and std, as well as statistical analysis of this difference?\nWe are grateful for your valuable comment. In the revised manuscript, we have presented the data as an actual mean and std, as well as statistical analysis of this difference (page 39, Fig. 5).\nFigure 5B. Hijacking by hyper-invasive gliomas results in enhanced activity within the gamma (30-100 Hz) and theta (4-8 Hz) frequency bands of neural signals.\nMinor:\n- The resolution and quality of Figures is not appropriate. There are also many cases where the graphs presented have no information on axis, units and further details about the data and statistics. Examples: Figure 3i, Figure 4d, e, f, g, h, l\n- Page 10, Line 20 – Typo “calcium hannel-“, instead of “calcium channel”.\nWe appreciate your attention to these details. All figures (including Figure 3i, 4d, 4e, 4f, 4g, 4h, and 4l) have been revised. Axes, units, data details, and statistical information have been added to ensure clarity and reproducibility (Page 38).\nDue to Microsoft Word constraints, image resolution is compromised. We will provide original high-resolution images for proper publication.\nThe typo \"calcium hannel-\" on Page 10, Line 20 has been corrected to \"calcium channel\" in the revised manuscript.\n)%\n(ytivitcA\nrepyH\ndecnahnE\nGlioma-to-neuron ratios"}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this revised manuscript, the authors do a good job to address all of the major concerns raised by this reviewer including presenting new data from experiments, and in particular, testing this model in another cell line U251. There are no further major revisions requested but a few minor revisions to improve clarity and organization of the overall manuscript.\n- On line 257, the authors state 10,000 cells were seeded into the device, please clarify how many of these cells are tumor cells vs neurons\n- It appears that the authors present for the first time their results regarding testing an additional cell line U251 and also checking tumor proliferation in the discussion section Lines 456-470. It may make more sense to first present this data in the results section as these are results and then discuss these findings or include them in the broader discussion that is present in the conclusion section.\n- The authors discussion of the U251 cell line findings could be further improved for clarity. The authors state “recapitulating co-culture invasion intensity required re-extracting co-culture-derived 464 electrical signals”. What exactly does this mean? Please clarify. Are the authors stating that a different U251-hijacked signal appeared to promote U251 invasiveness more so than the U87-hijacked signal?\n- Can the authors clarify in the manuscript why it appears for all of the CMC graphs shown, all of the cell lines appear to converge at 3mm by day 7? Is 3mm the maximum value that can be measured by this assay? If so, it may be better to focus on which treatment arms or experimental conditions reach this maximum migration distance faster and more clearly explaining this distinction in the manuscript.", "reviewer_2": "The authors have addressed most of my comments. I have two quantity control questions/comments for the new figures:\n- Are the groups statistically different in the new Figure S11?\n- Quantification of the western blots in new Figure S8 is required", "reviewer_3": "I thank the authors for their thorough revisions and openness in addressing the various comments. I believe all major points have been address and that the article is now acceptable for publication. Congratulations on the great results and outcomes!\nPS: I must just leave two very small comments and suggestions for the final version of the article, if possible:\n- Please update the Materials & Methods section to provide details on the new studies performed on molecular signatures of cells (Figure S8);\n- Please try to mention in either the M&M section or in the main text how cell viability was ensured during the tests. The authors provided a proper explanantion on their Responses document but I believe the article was not updated to include that."}, "author_response": "COMMENTS TO AUTHOR:\nReviewer: #1\nIn this revised manuscript, the authors do a good job to address all of the major concerns raised by this reviewer including presenting new data from experiments, and in particular, testing this model in another cell line U251. There are no further major revisions requested but a few minor revisions to improve clarity and organization of the overall manuscript.\n- On line 257, the authors state 10,000 cells were seeded into the device, please clarify how many of these cells are tumor cells vs neurons\nWe sincerely appreciate your constructive comments. Further information has been provided in the revised manuscript. The ratio of tumor cells to nerve cells is generally 1:1. (lines 220-221)\n- It appears that the authors present for the first time their results regarding testing an additional cell line U251 and also checking tumor proliferation in the discussion section Lines 456-470. It may make more sense to first present this data in the results section as these are results and then discuss these findings or include them in the broader discussion that is present in the conclusion section.\nThank you for your suggestion. In the revised manuscript, the data regarding the U251 cell line has been moved to the Results section, and the related findings have been further discussed in the Discussion section (lines 422-431).\n- The authors discussion of the U251 cell line findings could be further improved for clarity. The authors state “recapitulating co-culture invasion intensity required re-extracting co-culture-derived 464 electrical signals”. What exactly does this mean? Please clarify. Are the authors stating that a different U251-hijacked signal appeared to promote U251 invasiveness more so than the U87-hijacked signal?\nWe appreciate your constructive feedback. To clarify the findings regarding the U251 cell line: The electrical signals extracted from U87 cells can promote U251 invasion; however, the signals derived from U251 cells themselves exhibit a stronger promotional effect on U251 invasiveness. The revised description has been updated in the corresponding pages of the revised manuscript (lines 428-431).\n- Can the authors clarify in the manuscript why it appears for all of the CMC graphs shown, all of the cell lines appear to converge at 3mm by day 7? Is 3mm the maximum value that can be measured by this assay? If so, it may be better to focus on which treatment arms or experimental conditions reach this maximum migration distance faster and more clearly explaining this distinction in the manuscript.\nThank you for raising this important point. The 3 mm distance in the CMC graphs represents the maximum collective migration distance of the cells used in this project. Our analysis of migration speed is actually based on the order in which each group reaches this maximum distance or the migration distance achieved within the same time period, which is fully consistent with your suggestion.\nReviewer #2\nThe authors have addressed most of my comments. I have two quantity control questions/comments for the new figures:\n- Are the groups statistically different in the new Figure S11?\nThank you for your question. There is no significant difference between the U251 hijacked group and the Co-culture group. However, these two groups show differences from the other groups in the first 5 days, but no differences are observed on days 6-7. A relevant discussion has been added in the revised manuscript (lines 425-428).\n- Quantification of the western blots in new Figure S8 is required.\nWe appreciate the reviewer’s valuable comment. We have now provided the densitometric quantification of the western blots in new Figure S8, based on three independent biological replicates.\nReviewer #3\nI thank the authors for their thorough revisions and openness in addressing the various comments. I believe all major points have been address and that the article is now acceptable for publication. Congratulations on the great results and outcomes!\nPS: I must just leave two very small comments and suggestions for the final version of the article, if possible:\n- Please update the Materials & Methods section to provide details on the new studies performed on molecular signatures of cells (Figure S8);\nThanks for your comment. This additional information has been incorporated on lines 734-756 of the revised manuscript.\n- Please try to mention in either the M&M section or in the main text how cell viability was ensured during the tests. The authors provided a proper explanantion on their Responses document but I believe the article was not updated to include that.\nWe sincerely appreciate your constructive comments. We have supplemented the revised manuscript with related details in lines 546-549. Specifically, the culture medium within the microfluidic chip was regularly replaced to sustain cell activity, and cell viability was monitored by periodic observation of cellular morphology and electrophysiological signals to ensure normal cellular function."}]

Decision letter