Comprehensive Classification Of Neuronal Subtypes By Single Cell Transcriptomics

单细胞转录组学对神经元亚型的综合分类

• 批准号: 9324097
• 负责人: AVIV REGEV
• 金额: $ 8.47万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-26 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9324097
• 关键词: Address; BRAIN initiative; Brain; Brain region; Calcium; Categories; Cell Separation; Cells; Censuses; Classification; Computer Analysis; Computing Methodologies; Data; Decision Trees; Dissection; Emerging Technologies; Ensure; Equipment and supply inventories; Functional disorder; Gene Combinations; Genes; Goals; Habenula; Health; Image; In Situ Hybridization; Lead; Learning; Libraries; Maps; Measures; Methods; Modeling; Molecular; Morphology; Mus; Neurons; Pattern; RNA; Reagent; Retina; Structure; Surveys; Taxonomy; Technology; Testing; Tissues; Transcript; Translating; Work; Zebrafish; base; brain cell; cell type; computer framework; cost; falls; high risk; insight; interest; molecular marker; novel diagnostics; novel therapeutics; predictive modeling; scale up; transcriptome; transcriptome sequencing; transcriptomics

 DESCRIPTION (provided by applicant): To understand the brain, we need a "parts list" of its cell types. The list will need to integrate molecular, functional and morphological data, but of these, molecular classification is best suited for comprehensive categorization and the only approach that can lead directly to genetically accessing the types; such access is essential in order to mark and manipulate neurons and to allow rigorous comparison of neurons from normal and diseased brains. We will apply the emerging method of single-cell transcriptional profiling (scRNA-seq) to this task. We will first rigorously compare and optimize cutting- edge methods for cell isolation, transcriptional profiling, and computational analysis to establish an efficientand effective pipeline for categorization. Then, we will apply our suite of methods to two brain regions - mouse retina and zebrafish habenula - that differ in several ways but share key features: they are accessible and compact and it is feasible to map their cell types comprehensively. In each case, we will perform unbiased and exhaustive profiling of 1,000's of neurons, to ensure that even rare classes of neurons are included in the survey. We will validate gene modules obtained from profiling by in situ hybridization, integrate them with structural and functional data, and provide standardized and comprehensive maps of cell type. Finally, we will apply what we have learned to a larger region, the mouse habenula. Profiling and classification in this structure will not only provide a stringent test of our ability to scale up our methods, bu also allow us to ask two important and interesting questions: to what extent cell types are conserved across species (zebrafish vs. mouse habenula) and to what extent cell types are conserved across regions (mouse retina vs. habenula). Together, insights, methods and reagents obtained in this work will provide an essential toolkit for tackling the whole brain.

 描述(申请人提供)：为了了解大脑，我们需要了解它的细胞类型的“部件清单”。这份清单将需要整合分子、功能和形态数据，但在这些数据中，分子分类最适合于全面分类，也是唯一可以直接从基因上获取这些类型的方法；这种获取对于标记和操纵神经元至关重要，并允许对正常和疾病大脑中的神经元进行严格比较。我们将应用新出现的单细胞转录图谱方法(scRNA-seq)来完成这项任务。我们将首先严格比较和优化用于细胞分离、转录图谱和计算分析的尖端方法，以建立一个高效和有效的分类管道。然后，我们将把我们的一套方法应用于两个大脑区域--小鼠视网膜和斑马鱼缰核--这两个区域在几个方面不同，但有共同的关键特征：它们易于访问和紧凑，而且可以全面地绘制它们的细胞类型。在每种情况下，我们都将对1,000‘S的神经元进行公正和详尽的分析，以确保即使是罕见的神经元类别也包括在调查范围内。我们将验证通过原位杂交获得的基因模块，将它们与结构和功能数据相结合，并提供标准化和全面的细胞类型图谱。最后，我们将把我们所学到的知识应用到一个更大的区域--小鼠缰核。这种结构的分析和分类不仅将对我们扩大方法的能力提供严格的测试，而且还允许我们提出两个重要而有趣的问题：细胞类型在多大程度上跨物种保存(斑马鱼与小鼠缰核)，以及细胞类型在多大程度上跨区域保存(小鼠视网膜与缰核)。总之，在这项工作中获得的见解、方法和试剂将为解决整个大脑问题提供必要的工具包。

项目成果

The Design of a CMOS Nanoelectrode Array with 4096 Current-Clamp/Voltage-Clamp Amplifiers for Intracellular Recording/Stimulation of Mammalian Neurons.

• DOI: 10.1109/jssc.2020.3005816
• 发表时间: 2020-09
• 期刊: IEEE journal of solid-state circuits
• 影响因子: 5.4
• 作者: Abbott J;Ye T;Krenek K;Qin L;Kim Y;Wu W;Gertner RS;Park H;Ham D
• 通讯作者: Ham D

Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR-Cas9 barcodes by scGESTALT.

• DOI: 10.1038/s41596-018-0058-x
• 发表时间: 2018-11
• 期刊: Nature protocols
• 影响因子: 14.8
• 作者: Raj B;Gagnon JA;Schier AF
• 通讯作者: Schier AF

Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.

• DOI: 10.1016/j.cell.2016.07.054
• 发表时间: 2016-08-25
• 期刊: CELL
• 影响因子: 64.5
• 作者: Shekhar, Karthik;Lapan, Sylvain W.;Whitney, Irene E.;Tran, Nicholas M.;Macosko, Evan Z.;Kowalczyk, Monika;Adiconis, Xian;Levin, Joshua Z.;Nemesh, James;Goldman, Melissa;McCarroll, Steven A.;Cepko, Constance L.;Regev, Aviv;Sanes, Joshua R.
• 通讯作者: Sanes, Joshua R.

Comprehensive comparative analysis of 5'-end RNA-sequencing methods.

• DOI: 10.1038/s41592-018-0014-2
• 发表时间: 2018-07
• 期刊: Nature methods
• 影响因子: 48
• 作者: Adiconis X;Haber AL;Simmons SK;Levy Moonshine A;Ji Z;Busby MA;Shi X;Jacques J;Lancaster MA;Pan JQ;Regev A;Levin JZ
• 通讯作者: Levin JZ

The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas.

• DOI: 10.1016/j.neuron.2017.10.007
• 发表时间: 2017-11-01
• 期刊: Neuron
• 影响因子: 16.2
• 作者: Ecker JR;Geschwind DH;Kriegstein AR;Ngai J;Osten P;Polioudakis D;Regev A;Sestan N;Wickersham IR;Zeng H
• 通讯作者: Zeng H