Engineering designer probes for imaging membrane potential: novel parts, architectures, and platforms

项目总结/摘要 哺乳动物的大脑由多种神经元和神经胶质细胞类型组成，这些细胞类型在其功能上不同。 形态学、电特性和分子标记。理解行为的神经基础 因此，需要阐明这些细胞类型在健康和疾病中的各自作用， 表示和处理信息。然而，传统的电生理（基于电极） 监测神经活动的方法不能限于特定的细胞类型。建议的解决方案， 该建议是开发改进的遗传编码电压指示器（GEVI）， 亮度受电活动（电压）调节的荧光蛋白。GEVI很有前途 工具，因为它们的表达可限于遗传上确定的细胞群体。此外，通过直接 报告电压而不是替代参数（例如钙），他们可以监测更丰富的 神经元电信号，包括抑制信号（超极化），阈下去极化， 和动作电位的快速序列。 虽然GEVI已经显示出用于电压瞬变的光学检测的早期前景，但是它们的 目前的性能不足以对单个神经元群体中的电压进行单次试验成像 in vivo.本提案旨在采用多管齐下的办法解决这一业绩差距。一是 作者提出优化一种新的GEVI结构，其优化用于长期体内成像。第二、 自然多样性将被挖掘，以发现新的电压敏感蛋白，使电压检测 具有更高灵敏度或时间精度的动态。最后，一个新的高通量筛选平台 将部署为工程GEVI进行优化的GEVI，以快速筛选大型指标库。到 促进下游应用的部署，具有改进性能的GEVI将 在神经元、脑切片和体内的所有关键性能指标上进行了全面评估。 总的来说，实验预计将导致设计师GEVI的广泛实用性和影响， 了解大脑中的各种细胞类型如何合作以实现行为。

Project Summary/Abstract The mammalian brain is composed of a diverse array of neuronal and glial cell types that differ in their morphology, electrical characteristics, and molecular markers. Understanding the neural basis of behavior in health and disease will, therefore, require elucidation of the respective roles of these cell types in representing and processing information. However, traditional electrophysiological (electrode-based) methods for monitoring neural activity cannot be restricted to specific cell types. The solution suggested in this proposal is to develop improved Genetically Encoded Voltage Indicators (GEVIs), which are fluorescent proteins whose brightness is modulated by electrical activity (voltage). GEVIs are promising tools as their expression can be restricted to genetically-defined cell populations. Moreover, by directly reporting voltage rather than a surrogate parameter (e.g. calcium), they can monitor a richer repertoire of neuronal electrical signals, including inhibitory signals (hyperpolarizations), subthreshold depolarizations, and rapid trains of action potentials. While GEVIs have shown early promise for optical detection of voltage transients, their performance is currently insufficient for single-trial imaging of voltage in populations of individual neurons in vivo. This proposal aims to address this performance gap using a multipronged approach. First, the authors propose to optimize a new GEVI architecture optimized for long-term in vivo imaging. Second, natural diversity will be mined to uncover novel voltage-sensing proteins that enable detection of voltage dynamics with more sensitivity or temporal precision. Finally, a new high-throughput screening platform optimized for engineering GEVIs will be deployed for rapidly screening large libraries of indicators. To facilitate deployment in downstream applications, GEVIs with improved properties will be comprehensively evaluated across all key performance metrics in neurons, in brain slices, and in vivo. Overall, the experiments are anticipated to lead to designer GEVIs of broad utility and impact for understanding how the various cell types in the brain cooperate to enable behavior.