Topology of the Cell Membrane Interface for the Physical Re-Encoding of Neural Signals

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Authors: Feixiang Chen, Jian Chen, Bing Xiao, Jingsen Lin, Yumin Mao, L. H. Wang, Man Guo, Chu Xu, Si Wen, Guohua Xu, Yanyan Liu, Dayong Jin
+1 more Wenbo Bu
Cited by: 1
References: 51

Abstract

Efficient encoding of neural signals can influence sensory and cognitive information, which is crucial for regulating emotions and behaviors. Current encoding strategies primarily focus on active ion channels for signal inputs, often overlooking the role of membrane structural properties in signal transduction. Here, we present a membrane interface topology strategy to reshape membrane structure, enabling neural signal re-encoding and altering information output. We design gold nanorods coated with bioadhesive polydopamine (Au@PDA), which tightly adhere to membranes, forming a topological interface. Our results demonstrate that physical electrical interactions within this interface re-encode action potential patterns, leading to decreased neuronal firing sensitivity and adjustments in plasticity. Typically, the membrane time constant (τ m ), influenced by membrane structural properties, increases by 68%, resulting from a 34% increase in membrane capacitance ( C m ) and a 112% increase in membrane resistance ( R m ). We attribute the increased C m to the electric polarization of Au@PDA within the membrane’s electrical field and the increased R m to the binding of Au@PDA with Na + and K + ions under electric polarization. In vitro, Au@PDA significantly reduces calcium influx caused by high potassium stimulation in the long term, and in vivo, it contributes to pain relief in mice with spared nerve injury and aids in restoring nerve function following spinal cord injury. This study introduces a method for re-encoding neural information via membrane interface topology, offering significant potential for enhancing neuroplasticity and treating chronic neurological diseases.

Fields of Study & Subjects

Lipid Membrane Structure and Behavior Neuroscience and Neural Engineering Graphene and Nanomaterials Applications

Publication details

Journal / source
Journal of the American Chemical Society
Publisher
American Chemical Society (ACS)
Volume
148
Issue
4
Pages
4305–4316
Publication date
8 January 2026
Language
EN