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英文摘要
 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.
期刊论文(5)
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会议论文
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
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
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.
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
Core B: Data Management and Bioinformatics Core
  • 批准号:
    10207346
  • 项目类别:
  • 资助金额:
    $20.02万
  • 财政年份:
    2017
  • 负责人:
    AVIV REGEV
  • 依托单位:
Clinical implementation of single cell tumor transcriptome analysis
  • 批准号:
    9035651
  • 项目类别:
  • 资助金额:
    $45.75万
  • 财政年份:
    2016
  • 负责人:
    AVIV REGEV
  • 依托单位:
DNA microscopy for spatially resolved genomic analyses in intact tissue
  • 批准号:
    9360633
  • 项目类别:
  • 资助金额:
    $121.25万
  • 财政年份:
    2016
  • 负责人:
    AVIV REGEV
  • 依托单位:
An integrated multiplexed genomic assay for low input clinical samples1
  • 批准号:
    9305830
  • 项目类别:
  • 资助金额:
    $41.2万
  • 财政年份:
    2015
  • 负责人:
    AVIV REGEV
  • 依托单位:
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