Deciphering the building blocks of the macaque prefrontal cortical microcircuit

破译猕猴前额皮质微电路的构建模块

• 批准号: 10612016
• 负责人: Xiaolong Jiang
• 金额: $ 40.13万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612016
• 关键词: Alzheimer' s Disease; Area; Behavioral; Brain; Brain Diseases; Brain region; Cells; Cellular Morphology; Characteristics; Classification; Classification Scheme; Clinical; Cognitive; Complex; Data; Data Set; Decision Making; Disease; Dissection; Electrophysiology (science); Endowment; Future; Gene Expression Profile; Genes; Genetic; Genetic Markers; Goals; Homologous Gene; Human; In Vitro; Injections; Interneurons; Knowledge; Label; Macaca; Machine Learning; Maps; Mediating; Mental Depression; Methods; Molecular; Monkeys; Morphology; Mus; Neocortex; Neurobiology; Neurons; Pattern; Perception; Physical shape; Prefrontal Cortex; Primates; Property; Protocols documentation; Research; Resolution; Rodent; Scheme; Schizophrenia; Short-Term Memory; Specific qualifier value; Structure; Taxonomy; Testing; Thinking; Time; Viral; Virus; cell type; cognitive capacity; cognitive function; cognitive process; cost effective; cost efficient; design; experimental study; high dimensionality; in vivo; infancy; interdisciplinary approach; interest; machine learning algorithm; molecular marker; multimodality; neocortical; neuropsychiatric disorder; novel; patch sequencing; preservation; scale up; single-cell RNA sequencing; socioeconomics; success; tool; transcriptome; transcriptomics; treatment strategy

Abstract Since Ramon y Cajal, neuroscientists have speculated that even the most complex brain functions might even- tually be understood at the level of neuronal cell types and their connections. More recently, while we have be- gun to understand the wiring principles of cortical microcircuit in rodents at the level of cell types, we are still in infancy in understanding the circuit organization of the primate neocortex at the level of cell types and their connections, slowing the progress toward a mechanistic understanding of complex cognitive capabilities char- acteristic of primates. For instance, the dorsolateral prefrontal cortex (DLPFC) of the primate brain is the most evolutionarily developed brain region that supports complex cognitive processes characteristic of primates, such as reasoning, planning, and abstract thinking. However, we know little about the constituent cell types comprising DLPFC circuit, how each cell type connects each other to form a functional circuit, and what circuit components specific to this circuit endow it with superb computational capabilities for complex cognitive pro- cesses. To fill in this knowledge gap, we scale up a cost-effective, interdisciplinary approach to macaque DLPFC, aiming at identifying all its consitiuent cell types and decipher their connectivity rules, with emphasis on highly diverse GABAergic interneurons. We propose to use multi-cell patch recordings, single-cell RNA sequencing (scRNA-seq), novel and rapid viral GABAergic labeling, and machine learning to achieve two main goals: 1) dissect macaque DLPFC microcircuit by generating a morphological taxonomy of cell types in DLPFC and mapping their connections; and 2) derive transcriptomic signatures of morphologically defined DLPFC neurons using Patch-seq method, a novel scRNA-seq protocol. We have demonstrated the feasibility and suc- cess of this approach in mouse neocortex, and our preliminary data indicate no technical issue in applying this approach to primates. Using multi-cell patch recordings, we will characterize electrophysiology and morphology of thousands of neurons and map connections between tens of thousands of cell pairs from DLPFC. Using Patch-seq method, we will combine patch recording with a novel/sensitive scRNA-seq method (Smart-seq2) to simultaneously obtain electrophysiology, morphology and transcriptome from single neurons, which can further substantiate cell type classification and identify novel molecular markers for each cell type. We will prioritize our effort on superficial layers of DLPFC, but eventually scale up our efforts to all layers if time permits. At the end, the project will uncover a high-resolution microcircuit blueprint of macaque DLPFC with each circuit com- ponent identified by specific genetic markers. Such a comprehensive dataset will provide the essential ground- work to design molecular tools for further functional dissection of the complex cognitive processes subserved by PFC. From a clinical perspective, having reference transcriptomes and connectivity patterns for different cell types in primate DLPFC will facilitate our understanding of the relationship between disease-associated genes, cell types, and circuit deficits in neuropsychiatric diseases, schizophrenia in particular.

摘要 从拉蒙·卡哈尔开始，神经科学家们就推测，即使是最复杂的大脑功能也可能- 最终可以在神经元细胞类型及其连接的水平上理解。最近，当我们- 枪了解啮齿动物在细胞类型水平上的皮层微电路的布线原理，我们仍然在 在理解灵长类动物新皮层的电路组织在细胞类型和它们的水平的婴儿期， 连接，减缓了对复杂认知能力的机械理解的进展， 灵长类动物的特征例如，灵长类大脑的背外侧前额叶皮层（DLPFC）是最重要的。 支持灵长类动物特有的复杂认知过程的进化发展的大脑区域， 例如推理、计划和抽象思维。然而，我们对组成细胞类型知之甚少， 包括DLPFC电路，每个单元类型如何相互连接以形成功能电路，以及 特定于该电路的组件赋予其用于复杂认知过程的卓越计算能力， cesses。为了填补这一知识空白，我们扩大了对猕猴的成本效益，跨学科方法 DLPFC旨在识别其所有组成细胞类型并破译其连接规则，重点是 对高度多样化的γ-氨基丁酸能中间神经元的影响我们建议使用多细胞斑块记录，单细胞RNA 测序（scRNA-seq）、新型快速的病毒GABA能标记和机器学习，以实现两个主要的 目的：1）解剖猕猴后外侧前额叶皮层微电路，对后外侧前额叶皮层细胞类型进行形态学分类 和映射它们的连接;和2）导出形态学定义的DLPFC的转录组学签名 神经元使用Patch-seq方法，一种新的scRNA-seq协议。我们已经证明了它的可行性和可靠性- 我们的初步数据表明，在应用这种方法时没有技术问题。 接近灵长类动物使用多细胞贴片记录，我们将表征电生理学和形态学 我们的工作是研究数千个神经元的结构，并绘制出DLPFC中数万个细胞对之间的连接。使用 Patch-seq方法，我们将联合收割机与新的/敏感的scRNA-seq方法（Smart-seq 2）相结合， 同时从单个神经元获得电生理学、形态学和转录组， 证实细胞类型分类和鉴定每种细胞类型的新分子标记。我们将优先考虑 我们的努力只是在DLPFC的表面层，但如果时间允许，最终会将我们的努力扩展到所有层。在 最后，该项目将揭示一个高分辨率的猕猴DLPFC微电路蓝图，每个电路组件， 通过特定的遗传标记鉴定的组分。这样一个全面的数据集将提供基本的基础- 设计分子工具，进一步功能解剖复杂的认知过程， 从临床的角度来看，具有不同细胞的参考转录组和连接模式， 灵长类DLPFC的类型将有助于我们理解疾病相关基因之间的关系， 细胞类型和神经精神疾病，特别是精神分裂症的电路缺陷。