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

项目摘要/摘要 哺乳动物的大脑由一系列不同的神经细胞和神经胶质细胞组成，这些细胞的类型不同 形态、电学特征和分子标记。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(以电极为基础) 监测神经活动的方法不能局限于特定类型的细胞。中建议的解决方案 这项提议是开发改进的遗传编码电压指示器(GEVI)，这是 其亮度受电活动(电压)调节的荧光蛋白质。GEVI前景看好 工具本身的表达可以限制在基因定义的细胞群体中。此外，通过直接 报告电压而不是替代参数(例如钙)，他们可以监控更丰富的 神经元电信号，包括抑制信号(超极化)、阈值下去极化、 以及动作电位的快速训练。 虽然GEVI在电压瞬变的光学检测方面表现出了早期的希望，但他们的 目前，对于单个神经元群体中的电压的单次尝试成像来说，性能是不够的 在活体内。这项提议旨在通过多管齐下的方法解决这一绩效差距。首先， 作者建议优化一种新的GEVI架构，该架构针对长期活体成像进行了优化。第二, 将挖掘自然多样性，以发现能够检测电压的新型电压敏感蛋白 具有更高灵敏度或时间精确度的动力学。最后，一个新的高通量筛选平台 将部署针对工程而优化的全球环境监测系统，以便快速筛选大型指标库。至 促进下游应用的部署，性能得到改善的GEVI将 全面评估了神经元、脑片和活体中的所有关键性能指标。 总体而言，预计这些实验将导致设计者GEVI具有广泛的实用性和影响 了解大脑中不同类型的细胞是如何合作来实现行为的。

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.