HuNeuD - The Human Neuron Discovery Program

格兰特概要：人类神经元发现(HuNeuD)计划寻求发现人类神经元和电路的特性。HuNeuD是从HQP培育的我最初的发现奖励和关键合作中成长起来的。HuNeuD的长期目标是确定人类神经元的形态、电生理、转录和连通性，以定义它们的“细胞类型”。先进的物理和计算工具，以及多尺度方法，将把人类电生理记录的不同空间尺度上的细胞类型联系起来--特定细胞类型的活动将首次与人脑处理有关。一般背景：随着用于细胞分型和绘制微电路连通性的工具集的迅速扩大，表征人类细胞类型及其构成的微电路是非常有兴趣的。我们的追求将是对国际上努力创建人类细胞类型图集(即。艾伦研究所细胞类型计划)，使用组织处理和处理的一致技术协议，同时追求我们独特的研究目标。研究计划|人类细胞类型鉴定：在我们全细胞经验的基础上，进一步的细胞类型鉴定将采用单细胞RNA测序(ScRNAseq)。ScRNAseq提取记录的神经元的转录图谱(遗传指纹)，其中形态和电生理学已经建立。细胞类型的表征将在模型切片系统(我们已经创建了其中一个)中进行，以诱导振荡-集体神经元动力学的特征。相位度量将量化细胞类型相对于正在进行的振荡如何以及何时活跃，以固定记录尺度上的细胞类型活动。扩大规模，多电极阵列(MEA)记录，提供所有皮质板层的局部场电位(LFP)的细胞外记录。脑电地形图还记录了单个神经元的尖峰--也就是单单位活动(SUA)。将获得SUA的相位度量。人体活体实验：将我们记录人体尿酸的技术经验转化为高密度层流探针，将在调节振荡的行为任务期间记录跨皮质的尿酸和LFP(类似于MEA)。通过计算相位度量，SUA将被锚定回细胞类型。单元型连通性：从时间序列数据中提取连通性的一种方法是统计力学。伊辛模型捕获了集体神经元动力学的某些方面，但由于它将信号二值化为-1或+1，因此受到限制。波特模型是伊辛模型的推广，可以有任意数量的状态。我们预计POTS模型将成为离散(尖峰)和连续(LFP)时间序列数据的通用框架。多尺度数学模型：计算建模将是将细胞类型锚定于皮质振荡的补充方法。这样的模型可以帮助在概念上架起空间尺度之间的桥梁。

Grant synopsis: The Human Neuron Discovery (HuNeuD) program seeks to discover the properties of human neurons and circuits. HuNeuD has grown out of my initial Discovery Grant cultivated by HQP, and key collaborations. HuNeuD's long term goal is to determine the morphological, electrophysiological, transcriptomic, and connectivity of human neurons that define their `cell-type'. Advanced physical and computational tools, and a multi-scale approach, will link cell-types across different spatial scales of human electrophysiological recordings - for the first time activity of specific cell-types will be related to human brain processing. General background: With a rapidly expanding `toolset' for cell-typing, and mapping connectivity of microcircuits, it is of great interest to characterize human cell-types and the microcircuits they comprise. Our pursuits will compliment international efforts to create an atlas of human cell-types (ie. Allen Institute Cell Types Program) using consistent technical protocols for tissue processing, and handling, while pursuing our unique research objectives. Research plan | Human cell-type characterizations: Building from our whole-cell experience, further cell-type characterization will employ single-cell RNA sequencing (scRNAseq). scRNAseq extracts the transcriptomic profile (genetic fingerprint) of the recorded neuron in which morphology and electrophysiology have been established. The cell-type characterization will be performed in model slice systems (one of which we have created) to induce oscillations - the signature of collective neuronal dynamics. A phase-metric will quantify how and when a cell-type is active relative to ongoing oscillations, to anchor cell-type activity across recording scales. Scaling up, multi-electrode array (MEA) recordings, provide extracellular recordings of local field potentials (LFPs) across all cortical laminae. MEAs also record spiking of single neurons - aka single unit activity (SUA). Phase-metrics will be obtained for the SUA. Human in-vivo experiments: Translating our technical experience of recording SUA in humans, high-density laminar probes will record SUA and LFPs (analogous to the MEA) from across cortical layers during behavioral tasks that modulate oscillations. SUA will be anchored back to cell-types by computing phase-metrics. Cell-type connectivity: One approach to extract connectivity from time series data is statistical mechanics. The Ising model captures some aspects of collective neuronal dynamics, but is limited as it binarizes signals as -1 or +1. The Pott's model, a generalization of the Ising model, can have any number of states. We expect the Potts model to be a general frame work for both discrete (spiking), and continuous (LFP) time series data. Multi-scale mathematical models: Computational modelling will be a complementary approach to anchor cell-types to cortical oscillations. Such models can help conceptually bridge between spatial scales.