Brain Extracellular Matrix-Based Electronic Brain Biochip

To monitor neuronal activity with high fidelity, in vitro models must recapitulate not only the cellular composition but also the three-dimensional (3D) microenvironment of the brain. Here, we present an electronic brain biochip that integrates animal-derived decellularized extracellular matrix (dECM) hydrogels with flexible, multichannel electrodes to build a multilayer 3D neural network in which each layer can be independently monitored. Brain dECM hydrogels provide tissue-like biochemical and structural cues that accelerate neurite outgrowth and neural connectivity between layers, enabling the formation of functionally active 3D networks within 3 weeks. We use a low-cost, readily available dECM source from porcine brain tissue, upcycling biological waste into high-value neural scaffolds without compromising biocompatibility. The dECM hydrogels are compatible with both rat primary neurons and hiPSC-derived neurons. Flexible electrode interfaces support real-time, multichannel electrophysiological recording and controlled chemical stimulation. The combination of dECM scaffolding and flexible electrode interfaces supports signal capture throughout the 3D network volume. Functional assays under chemical stimulation reveal bursting and synchronized activity in which all layers participate. By coupling a dECM-based, 3D neural architecture to flexible, multichannel electronics, this work establishes a scalable "electronic organoid" platform for the study of neuronal dynamics and neuropharmacology. Collectively, these advances represent an important step toward artificial brain models that bridge the gap between engineered neural tissues and functional neurobiology.

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Publication Details

Journal
ACS Nano
Published
2026-03-11
DOI
https://doi.org/10.1021/acsnano.5c21848
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
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article

Brain Extracellular Matrix-Based Electronic Brain Biochip

C. C. Tchoyoson Lim, Y. Y. Ji, Xia Liu, Chen Hang et al.
ACS Nano
Neuroscience and Neural Engineering
article

Brain Extracellular Matrix-Based Electronic Brain Biochip

C. C. Tchoyoson Lim, Y. Y. Ji, Xia Liu, Chen Hang, Ruihua Dong
article en

Abstract

To monitor neuronal activity with high fidelity, in vitro models must recapitulate not only the cellular composition but also the three-dimensional (3D) microenvironment of the brain. Here, we present an electronic brain biochip that integrates animal-derived decellularized extracellular matrix (dECM) hydrogels with flexible, multichannel electrodes to build a multilayer 3D neural network in which each layer can be independently monitored. Brain dECM hydrogels provide tissue-like biochemical and structural cues that accelerate neurite outgrowth and neural connectivity between layers, enabling the formation of functionally active 3D networks within 3 weeks. We use a low-cost, readily available dECM source from porcine brain tissue, upcycling biological waste into high-value neural scaffolds without compromising biocompatibility. The dECM hydrogels are compatible with both rat primary neurons and hiPSC-derived neurons. Flexible electrode interfaces support real-time, multichannel electrophysiological recording and controlled chemical stimulation. The combination of dECM scaffolding and flexible electrode interfaces supports signal capture throughout the 3D network volume. Functional assays under chemical stimulation reveal bursting and synchronized activity in which all layers participate. By coupling a dECM-based, 3D neural architecture to flexible, multichannel electronics, this work establishes a scalable "electronic organoid" platform for the study of neuronal dynamics and neuropharmacology. Collectively, these advances represent an important step toward artificial brain models that bridge the gap between engineered neural tissues and functional neurobiology.

ACS NanoVol. 20(11)
National University of Singapore (SG), Southern University of Science and Technology (CN)
Openalex Percentile: Top 24%
Neuroscience and Neural Engineering
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