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适体偶联微电极纤维传感器(apta-μFS)在高选择性神经化学传感中的发展。

The Development of Aptamer-Coupled Microelectrode Fiber Sensors (apta-μFS) for Highly Selective Neurochemical Sensing.

机构信息

Department of Mechanical and Aerospace Engineering, School of Engineering, Tohoku University, Sendai, Miyagi 980-8579, Japan.

Department of Materials Science and Engineering, School of Engineering, Tohoku University, Sendai, Miyagi 980-8579, Japan.

出版信息

Anal Chem. 2023 May 2;95(17):6791-6800. doi: 10.1021/acs.analchem.2c05046. Epub 2023 Apr 23.

Abstract

The selective and sensitive sensing of neurochemicals is essential to decipher in-brain chemistry underlying brain pathophysiology. The recent development of flexible and multifunctional polymer-based fibers has been shown useful in recording and modulating neural activities, primarily electrical ones. In this study, we were able to realize fiber-based neurochemical sensing with high sensitivity and selectivity. We achieved a generalizable method to couple aptamers, a type of synthetic receptors on the carbon composites within fibers, as microsensors for highly selective neurochemical detection. Such an aptamer-coupled microelectrode fiber sensor (apta-μFS) enables simple, label-free, and sensitive dopamine (DA) detection down to 5 nM with ultrahigh specificity across major interferents. We succeeded in monitoring DA selectively within the living brain using our apta-μFS. We further showed the proof-of-concept of using microelectronic fiber-based toolsets to target neural pathways across electrical and chemical modalities. In summary, such fiber-based toolsets hold great potential to advance multimodal mechanistic understanding of brain pathophysiology.

摘要

神经化学物质的选择性和灵敏检测对于破译大脑病理生理学中大脑内的化学物质至关重要。最近,基于柔性多功能聚合物的纤维的发展已被证明在记录和调节神经活动方面非常有用,主要是电活动。在这项研究中,我们能够实现具有高灵敏度和选择性的纤维基神经化学传感。我们开发了一种通用的方法,即将适体(纤维内碳复合材料上的一种合成受体)与纤维结合,作为微传感器,用于高度选择性的神经化学检测。这种适配体偶联微电极纤维传感器(apta-μFS)能够简单、无标记、灵敏地检测多巴胺(DA),检测下限低至 5 nM,对主要干扰物具有超高特异性。我们成功地使用我们的 apta-μFS 在活体大脑中选择性地监测 DA。我们进一步展示了使用基于微电子纤维的工具集通过电和化学模态靶向神经通路的概念验证。总之,这种基于纤维的工具集具有很大的潜力,可以促进对大脑病理生理学的多模态机制理解。

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