Sparse, Strong and Large Area Targeting of Genetically Encoded Indicators

 描述(申请人提供)：电(电压)信号是大脑中信息处理的主要底物。直到今天，检测和记录活体动物神经元的电压变化仍然是实验神经科学的最终目标。标准的玻璃和金属电极具有极大的侵入性，它们的使用存在空间分辨率低、覆盖范围有限和细胞身份失明等问题。目前流行的钙敏感指标产生的信号污染了细胞内钙离子的变化，与神经元电信号无关，只能间接报告具有扭曲的排列和高度扭曲的波形的电信号。通过使用遗传编码电压指示器(GEVI)的光学电压成像提供了实现神经元电活动监测的概念上的理想原理。然而，即使是目前性能最好的GEVI也只适用于体内监测复合突触电位(来自未知数量的神经元的总和电压信号)，但无法同时解析来自完整神经组织中许多单个细胞的信号。虽然正在进行的由大脑计划资助的活动预计会产生性能更好、灵敏度更高的GEVI，但这本身并不能解决单细胞分辨率电压成像问题：来自单个细胞的信号在空间上不是分开的。这个问题的解决需要其他技术，包括改进的基因定位和数据分析方法。我们计划开发稀疏GEVI靶向中枢神经系统神经元的方案。稀疏的细胞靶向将允许对神经元活动进行成像，空间重叠很少。我们还计划开发数据分析程序，从稀疏标记的组织中获得的数据中分离单细胞反应，并在此基础上，在密集靶向组织中进行分离。综上所述，我们建议开发一种新的基因定向电压成像工具，它与现有的那些定性不同，以及数据分析方法，以便于使用来自大量嵌入在功能神经元网络中的神经元的电信号。这里开发的新的GEVI标记方案和数据采集和分析算法应该立即对研究健康和疾病中的大脑功能有用和有影响。

 DESCRIPTION (provided by applicant): Electrical (voltage) signal is the primary substrate of information processing in the brain. Detecting and recording voltage changes from neurons in living animals remains the ultimate goal of experimental neuroscience to the present day. Standard glass and metal electrodes are hugely invasive and their use suffers from poor spatial resolution, limited coverage, and blindness to cellular identity. The popular calcium-sensitive indicators generate signals contaminated with changes in intracellular calcium that are unrelated to neuronal electrical signals, and only indirectly report the electrical signals with distorted tiing and highly distorted waveform. A conceptually ideal principle to achieve monitoring of neuronal electrical activity is provided by optical voltage imaging using genetically-encoded voltage indicators (GEVIs). However, even the best performing GEVIs currently available are suitable only for in vivo monitoring of compound synaptic potentials (the summated voltage signal from unidentified number of neurons), but fail to resolve simultaneously signals from many individual cells in intact nervous tissue. While better performing GEVIs with higher sensitivity are expected to emerge from the on-going BRAIN Initiative-funded activities, this alone does not resolve the single-cell resolution voltage imaging problem: signals from individual cells are not spatially segregated. The solution to this issue requires other technologies including refined genetic targeting and data analysis methods. We plan to develop protocols for sparse GEVI targeting of central nervous system neurons. Sparse cellular targeting will allow imaging of neuronal activity with little spatial overlap. We also plan to develop data analysis routines for isolating single cel responses from data obtained in sparsely labelled tissue and, building on this, in densely targeted tissue. In summary, we propose to develop a novel genetically-directed voltage imaging tool that is qualitatively different than those currently available, along with data analyss methods to facilitate the use of electrical signals from large number of neurons embedded in functioning neuronal networks. The novel GEVI labelling protocols and data acquisition and analysis algorithms developed here should be immediately useful and impactful for studying brain function in health and disease.