Resource Core 1 - Primate/rodent Molecular Anatomy Core (PMAC)

资源核心1-灵长类/啮齿动物分子解剖学核心(PMAC) 线索：克里斯汀·奥弗顿博士和卡尔·戴瑟斯博士 摘要 PMAC将(在U19团队的真实环境中)开发、验证和实施 项目/合作)，用于研究电路结构-功能关系的下一代技术。这个 PMAC包括指导使用和改进最先进的分子、病毒和解剖学策略 项目，不断调整和现代化，以跟上(并推动)这一快速发展的领域 五年总体项目的一部分。PMAC将解决关键的技术密集型问题，这些问题将 作为三个主要类别的灵长类分子和解剖学工具，项目中不断涌现 将为更广泛的U19团队开发：1)基于最新一代水凝胶的体积采集、配准、 跨物种的量化和分析，包括对具有已知活性的相同细胞进行分子分析 使用我们的两种水凝胶-组织化学方法，通过基于IEG的活动轨迹来确定行为模式 (清晰度和星图；2)持续更新的病毒载体，用于解剖/活动可视化和因果关系 控制，精确-设计考虑到动物的历史(包括过去的病毒暴露/免疫 状态)和与多种光学工具的组合使用相关的光谱重叠；以及3)多功能性 用于标记自然使用的电路元件的病毒工具(使用荧光和活动感应或控制工具) 在特定的行为过程中，或基于许多连接、活动和遗传特征的靶向细胞。对齐 皮质神经元的功能(活动)特征及其遗传特征是至关重要的。对于PMAC，我们注意到 最好的时机是我们最近开发的星图(Wang等人科学2018)将允许寻址 这个问题在一般意义上，赋予了深刻的类型学和丰富的分子签名信息(数百个 基因，甚至每个细胞1000个基因)到相同的单个神经元和集合，观察到是自然的 与行为有因果关系。PMAC将特别关注跨境综合方法 观测尺度，就存储和发送海量数据的有效手段与DSC进行协商 数据集。对数据集的分析将使用计算RP3和DSC开发的方法。 U19小组在执行过程中取得的结果，涉及光学优值系数，包括信号到 噪声、像差和速度将在真实世界的指导下以紧密的闭环工作流程反馈给PMAC 应用，对基础科学本身来说是一个关键的机会。

Resource Core 1 - Primate/rodent Molecular Anatomy Core (PMAC) Leads: Kristin Overton PhD and Karl Deisseroth MD PhD Summary The PMAC will develop, validate, and implement (in the real-world setting of the U19 team projects/collaborations), next-generation technologies for studying circuit structure-function relationships. The PMAC involves guided use and refinement of state-of-the-art molecular, viral, and anatomical strategies across projects, continuously adapted and modernized to keep up with (and drive) this fast-moving field over the course of the 5-year overall project. The PMAC will address key technologically-intensive questions that will continuously arise across projects as three main categories of primate-capable molecular and anatomical tool will be developed for the broader U19 team: 1) latest-generation hydrogel-based volume acquisition, registration, quantification, and analysis across species, including molecular analysis of the same cells with known activity patterns during behavior via IEG-based activity traces using two of our hydrogel-tissue chemistry methods (CLARITY and STARmap; 2) continuously-updated viral vectors for anatomy/activity visualization and causal control, precision-designed to take into account both animal history (including past virus exposure/immune status) and spectral overlap relevant to combinatorial usage of multiple optical tools together; and 3) versatile viral tools for tagging (with fluorescent and activity-sensing-or-control tools) circuit elements naturally used during specific behaviors, or for targeting cells based on many features of wiring, activity, and genetics. Aligning the functional (activity) features of cortical neurons with their genetic profiles is crucial. For the PMAC, we note the superb timing in that our recent development of STARmap (Wang et al. Science 2018) will allow addressing this issue in a general sense, assigning deep typology and rich molecular signature information (hundreds of genes, and even >1000 genes per cell) to the same individual neurons and ensembles observed to be naturally and causally involved in behavior. The PMAC will pay particular attention to integrative methods for crossing scales of observation, consulting with the DSC regarding effective means for storing and sending the massive datasets. Analysis of the datasets will be with methods developed by the computational RP3 and the DSC. Results from U19 teams in the course of implementation, relating to optical figures of merit, including signal-to- noise, aberrations, and speed, will be fed back to the PMAC in a tight closed-loop workflow guided by real-world application, a key opportunity for fundamental science itself.