Characterization and quantification of CNS cell specific extracellular microvesicles in blood

血液中中枢神经系统细胞特异性细胞外微泡的表征和定量

基本信息

  • 批准号:
    10471285
  • 负责人:
  • 金额:
    $ 67.56万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-09-21 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

Extracellular microvesicles (EMVs) are small, membrane-bound vesicles released by most cell types, and can be found circulating in the blood and other biofluids. The proteins, miRNAs, and other molecular components they carry as cargo have become a target for the development of novel biomarkers that reflect the EMV parent cell types. In particular, a strategy of targeting cellular markers carried on their membrane surfaces has been used to probe the state of the brain by examining EMVs carrying L1CAM, a relatively CNS-specific neuronal marker. Measurement of cargo proteins in such EMVs has shown particular promise in identifying blood-based biomarkers for neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD). Their utility in probing the state of the brain in other pathological conditions, such as after a traumatic injury, remains to be determined. Additional targets are now being developed to identify EMVs from non-neuronal brain cell types, including GLAST and GLT-1 for astrocytes and CNPase for oligodendrocytes. Despite this progress, identification of cell-specific markers remains crude, focused only on markers that tend to be present across a broad cell type. The cells themselves, in contrast, encompass multiple sub-types with different functional niches, likely differentially affected in pathological states. Thus, it should be possible to identify sub- types of EMVs and examine their composition for more specific reflections of brain processes. However, the appropriate surface markers, or combinations of markers, to target have not yet been identified. Despite the promise of EMV-based biomarkers for CNS conditions, serious challenges to their widespread adoption for clinical usage remain. The current strategies for EMV isolation are largely centered on ultracentrifugation, yield vesicle samples with contamination by large protein aggregates, and usually require large sample volumes. The newly developed immunocapture method that has allowed specific measurement of L1CAM EMV cargos is far more specific, as it targets surface markers, but is expensive, time consuming, and tends to have poor yield. Therefore, novel technologies are needed to identify EMVs of interest, isolate them, and quantify cargos. Here, we will address these challenges by developing novel strategies and technologies to better quantify and characterize brain-derived plasma EMVs in the R21 stage, and then validating them in a large cohort of human subjects in the R33 stage. First, we will optimize two new EMV capture and sorting strategies, precipitation using Smart Beads, and sorting using a microfluidics device, to isolate specific categories of EMVs based on surface markers. Second, we will identify new, more specific targets, to isolate sub-populations of EMVs that might better represent disease-relevant cells of interest. Next, in the R33 stage, we will scale up these new techniques to examine large cohorts, and measure cargo proteins of interest within these specific EMV populations as biomarkers of brain dysfunction caused by AD, PD, and traumatic brain injury.
细胞外微泡(EMV)是由大多数细胞类型释放的小的膜结合囊泡,并且可以 在血液和其他生物液体中循环。这些蛋白质、miRNAs和其他分子成分 它们作为货物携带的EMV已经成为开发反映EMV母体的新型生物标志物的目标 细胞类型。特别地,靶向细胞膜表面上携带的细胞标记物的策略已经被发现。 用于通过检查携带L1 CAM(一种相对中枢神经系统特异性的神经元)的EMV来探测大脑状态 标记。在这样的EMV中货物蛋白的测量已经显示出在鉴定基于血液的肿瘤中特别有希望。 在一些实施方案中,本发明涉及神经退行性疾病如阿尔茨海默病(AD)和帕金森病(PD)的生物标志物。 它们在探测大脑在其他病理条件下的状态方面的效用,例如在创伤性损伤之后, 还有待确定。目前正在开发其他靶点,以从非神经元细胞中识别EMV。 脑细胞类型,包括用于星形胶质细胞的GLAST和GLT-1以及用于少突胶质细胞的CNG 3。尽管如此 尽管进展缓慢,但细胞特异性标志物的鉴定仍然很粗糙,仅关注倾向于存在的标志物, 广泛的细胞类型。相比之下,细胞本身包含多种亚型, 功能小生境,可能在病理状态下受到不同影响。因此,应该能够识别子 EMV的类型,并检查它们的组成,以更具体地反映大脑过程。但 尚未确定靶向的适当表面标记或标记的组合。 尽管基于EMV的生物标志物有望用于CNS疾病,但其广泛应用面临严重挑战。 临床应用仍然存在。目前的EMV隔离策略主要集中在 超离心,产生被大蛋白质聚集体污染的囊泡样品,并且通常需要 大量样品。新开发的免疫捕获方法,允许特异性测量 的L1 CAM EMV货物是更具体的,因为它的目标表面标记,但昂贵,耗时, 并且往往具有较差的产率。因此,需要新的技术来识别感兴趣的EMV,分离EMV。 他们,并量化货物。 在这里,我们将通过开发新的战略和技术来应对这些挑战,以更好地量化和 在R21阶段表征脑源性血浆EMV,然后在一个大的人类队列中验证它们。 在R33阶段。首先,我们将优化两个新的EMV捕获和排序策略,沉淀 使用智能珠,并使用微流体设备进行分选,以基于以下内容分离特定类别的EMV: 表面标记。其次,我们将确定新的,更具体的目标,以隔离EMV的亚群, 可以更好地代表感兴趣的疾病相关细胞。接下来,在R33阶段,我们将扩大这些新的 技术来检查大的队列,并测量这些特定EMV中感兴趣的货物蛋白 作为由AD、PD和创伤性脑损伤引起的脑功能障碍的生物标志物。

项目成果

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Min Shi其他文献

Min Shi的其他文献

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{{ truncateString('Min Shi', 18)}}的其他基金

Extracellular vesicle transport of brain-derived proteins to the blood in Alzheimer disease
阿尔茨海默病中细胞外囊泡将脑源性蛋白质转运至血液
  • 批准号:
    10031274
  • 财政年份:
    2020
  • 资助金额:
    $ 67.56万
  • 项目类别:
MicroRNAs in CNS-derived extracellular microvesicles as peripheral blood biomarkers for Alzheimer disease
CNS 来源的细胞外微泡中的 MicroRNA 作为阿尔茨海默病的外周血生物标志物
  • 批准号:
    10463539
  • 财政年份:
    2019
  • 资助金额:
    $ 67.56万
  • 项目类别:
MicroRNAs in CNS-derived extracellular microvesicles as peripheral blood biomarkers for Alzheimer disease
CNS 来源的细胞外微泡中的 MicroRNA 作为阿尔茨海默病的外周血生物标志物
  • 批准号:
    10682404
  • 财政年份:
    2019
  • 资助金额:
    $ 67.56万
  • 项目类别:
Exploring microvesicular transport across the blood-brain barrier as a novel a-synuclein clearance mechanism and source of Parkinson's disease biomarkers
探索微泡穿越血脑屏障的运输作为一种新型的α-突触核蛋白清除机制和帕金森病生物标志物的来源
  • 批准号:
    9751979
  • 财政年份:
    2018
  • 资助金额:
    $ 67.56万
  • 项目类别:
Peptide Biomarkers for Alzheimer Disease
阿尔茨海默病的肽生物标志物
  • 批准号:
    10183119
  • 财政年份:
    2017
  • 资助金额:
    $ 67.56万
  • 项目类别:
Peptide Biomarkers for Alzheimer Disease
阿尔茨海默病的肽生物标志物
  • 批准号:
    9922846
  • 财政年份:
    2017
  • 资助金额:
    $ 67.56万
  • 项目类别:
MicroRNAs in human body fluids as Parkinson disease biomarkers
人体体液中的 MicroRNA 作为帕金森病的生物标志物
  • 批准号:
    8768667
  • 财政年份:
    2014
  • 资助金额:
    $ 67.56万
  • 项目类别:
MicroRNAs in human body fluids as Parkinson disease biomarkers
人体体液中的 MicroRNA 作为帕金森病的生物标志物
  • 批准号:
    8862556
  • 财政年份:
    2014
  • 资助金额:
    $ 67.56万
  • 项目类别:
Statistical methods in collaborative research
合作研究中的统计方法
  • 批准号:
    10699689
  • 财政年份:
  • 资助金额:
    $ 67.56万
  • 项目类别:
Statistical methods in collaborative research
合作研究中的统计方法
  • 批准号:
    10928619
  • 财政年份:
  • 资助金额:
    $ 67.56万
  • 项目类别:
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