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用于光遗传学神经元控制的混合上转换纳米材料。

Hybrid upconversion nanomaterials for optogenetic neuronal control.

作者信息

Shah Shreyas, Liu Jing-Jing, Pasquale Nicholas, Lai Jinping, McGowan Heather, Pang Zhiping P, Lee Ki-Bum

机构信息

Department of Chemistry & Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.

出版信息

Nanoscale. 2015 Oct 28;7(40):16571-7. doi: 10.1039/c5nr03411f.

Abstract

Nanotechnology-based approaches offer the chemical control required to develop precision tools suitable for applications in neuroscience. We report a novel approach employing hybrid upconversion nanomaterials, combined with the photoresponsive ion channel channelrhodopsin-2 (ChR2), to achieve near-infrared light (NIR)-mediated optogenetic control of neuronal activity. Current optogenetic methodologies rely on using visible light (e.g. 470 nm blue light), which tends to exhibit high scattering and low tissue penetration, to activate ChR2. In contrast, our approach enables the use of 980 nm NIR light, which addresses the short-comings of visible light as an excitation source. This was facilitated by embedding upconversion nanomaterials, which can convert NIR light to blue luminescence, into polymeric scaffolds. These hybrid nanomaterial scaffolds allowed for NIR-mediated neuronal stimulation, with comparable efficiency as that of 470 nm blue light. Our platform was optimized for NIR-mediated optogenetic control by balancing multiple physicochemical properties of the nanomaterial (e.g. size, morphology, structure, emission spectra, concentration), thus providing an early demonstration of rationally-designing nanomaterial-based strategies for advanced neural applications.

摘要

基于纳米技术的方法提供了开发适用于神经科学应用的精密工具所需的化学控制。我们报告了一种新颖的方法,该方法采用混合上转换纳米材料,并结合光响应离子通道通道视紫红质-2(ChR2),以实现近红外光(NIR)介导的神经元活动的光遗传学控制。当前的光遗传学方法依赖于使用可见光(例如470 nm蓝光)来激活ChR2,而可见光往往具有高散射和低组织穿透性的特点。相比之下,我们的方法能够使用980 nm近红外光,解决了可见光作为激发源的缺点。这是通过将能够将近红外光转换为蓝色发光的上转换纳米材料嵌入聚合物支架中来实现的。这些混合纳米材料支架实现了近红外光介导的神经元刺激,其效率与470 nm蓝光相当。我们的平台通过平衡纳米材料的多种物理化学性质(例如尺寸、形态、结构、发射光谱、浓度),针对近红外光介导的光遗传学控制进行了优化,从而为合理设计基于纳米材料的先进神经应用策略提供了早期示范。

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