Integrative, multi-parametric characterization of the EV surface protein and nucleic acid landscape by nano-flow and sorting cytometry

通过纳流和分选细胞术对 EV 表面蛋白和核酸景观进行综合、多参数表征

• 批准号: 10018937
• 负责人: IONITA Calin GHIRAN
• 金额: $ 39.71万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2021-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10018937
• 关键词: Antibodies; Antigens; Behavior; Biogenesis; Biological; Biological Assay; Biological Markers; Blood; Cancer Patient; Cataloging; Catalogs; Cell physiology; Cells; Clinical; Communities; Complement; Complex; Computing Methodologies; Consumption; Cytometry; DNA; Data; Data Set; Detection; Diagnostic; Disease; Disease Marker; Disease Progression; Disease Surveillance; Epitopes; Flow Cytometry; Fluorescence; Fluorescence Resonance Energy Transfer; Fluorochrome; Gene Expression; Genetic Transcription; Goals; Health; Human; Immune response; Informatics; Label; Lipids; Malignant Neoplasms; Measures; Medicine; Membrane; Membrane Proteins; Metals; Methods; Molecular; Molecular Profiling; Multiparametric Analysis; Neoplasm Metastasis; Nucleic Acids; Paracrine Communication; Pathologic; Phycoerythrin; Plasma; Population; Property; Proteins; Protocols documentation; RNA; Relapse; Reproducibility; Reproducibility of Results; Research; Research Personnel; Resolution; Reverse Transcriptase Polymerase Chain Reaction; Sensitivity and Specificity; Sorting - Cell Movement; Specimen; Standardization; Surface Plasmon Resonance; Techniques; Technology; Testing; Time; Tissues; Training; Translations; Treatment Efficacy; United States National Institutes of Health; Universities; Vertebral column; Western Blotting; analytical method; base; bioinformatics infrastructure; bioinformatics pipeline; cell type; clinical practice; cloud based; cohort; collaborative approach; college; density; extracellular vesicles; flexibility; improved; informatics tool; innovation; light scattering; nano; nanoflow cytometry; novel; novel strategies; prognostic; tool; transcriptome sequencing; web-accessible

Abstract A novel paradigm in paracrine signaling has recently emerged based on the findings identifying extracellular vesicles (EVs) as intercellular conveyors of biological information both in normal and pathological conditions such as cancer. EVs and their cargo have been shown by us and others to regulate gene expression and alter cell function in various cell types. Moreover, during pathological conditions such as cancer, the number and compositions of EVs alter the host immune response as well as synchronize the behavior of secondary tumors. Isolation and molecular profiling of EVs (i.e. RNAs, proteins, lipids, metabolites) both in health and disease are critical for understanding EVs' biogenesis and for using EV as biomarkers for disease status. EV RNA and proteins are expected to vary, according to tissues of origin and the biological state of the EV-producing cells. Some of the current limitations in EV field that limit their use as disease markers are: i) lack of effective sorting methods that force bulk EVs analyses biasing detection against low abundant species, ii) DNA/RNA/proteins quantification methods and bioinformatics pipelines, which are time consuming expensive. Novel approaches are needed aimed at improving the antigen detection limit, characterization of EV subsets with single EV resolution, while generating reliable and reproducible results. These new standards in EV research are a necessary prerequisite for novel disease diagnostic and prognostic strategies, biomarker-based surveillance for disease progression, treatment efficacy, and relapse. In the present application, we propose a collaborative approach aimed at streamlining EV analyzes and improving antigen detection by i) detection of specific RNA/ssDNA molecules in EV populations by combining nano-flow cytometry and molecular beacons (Drs. Ghiran and Tyagi, BIDMC/HMS, and Rutgers University, respectively), ii) the use plasmon resonance nano-tags for EV antigen detection, using nano-flow cytometry (Dr. Jones, NCI) and the iii) integration of RNA and protein multidimensional analyses by a dedicated cloud- based, free, bioinformatics pipeline, which will extract by (Dr. Milosavljevic, Aleksandar, Baylor College of Medicine). The results of our collaborative effort will provide the scientific community with: i) new methods for EV sorting, detection of specific protein, RNA/ssDNA molecules on EV subpopulations with a sensitivity currently unattained by any large scale technique, ii) and protocols necessary for standardization across the labs and to translation to clinical practice, iii) bioinformatics infrastructure necessary for extraction of subtle but relevant data present in multi-parametric analyses. Importantly, the scientific community will be able to use every component produced by our team either together for comprehensive EV subset analyses, or as stand-alone tools.

抽象的 旁分泌信号传导中的一种新型范式最近根据发现细胞外的发现而出现 在正常和病理条件下，囊泡（EV）作为生物学信息的细胞间输送体 例如癌症。 EV及其货物已由我们和其他人显示，以调节基因表达并改变 各种细胞类型中的细胞功能。此外，在癌症，数量和数量等病理状况下 EV的组成改变了宿主的免疫反应，并同步二次肿瘤的行为。 健康和疾病中电动汽车（即RNA，蛋白质，脂质，代谢产物）的分离和分子分析是 了解EVS的生物发生以及将EV用作疾病状态的生物标志物至关重要。 EV RNA和 根据原产组织和产生EV产生细胞的生物学状态，蛋白质有望变化。 EV领域中限制其用作疾病标志物的一些当前局限性是：i）缺乏有效分类 强迫大量EVS分析对低丰富物种的偏置检测的方法，ii）DNA/RNA/蛋白 量化方法和生物信息学管道，它们耗时昂贵。新颖的方法 需要旨在提高抗原检测极限，具有单个EV子集的表征 EV分辨率，同时产生可靠且可再现的结果。 EV研究中的这些新标准是 新型疾病诊断和预后策略的必要先决条件，基于生物标志物的监视 用于疾病进展，治疗功效和复发。 在本应用程序中，我们提出了一种旨在简化电动汽车分析和的协作方法 通过i）通过组合来改善EV中特定RNA/ssDNA分子的检测 纳米流动细胞仪和分子信标（Ghiran和Tyagi博士，BIDMC/HMS和Rutgers University， ），ii）使用纳米流动细胞术的等离子共振纳米标签进行EV抗原检测 （琼斯博士，NCI）和iii）通过专用的云 - RNA和蛋白质多维分析的整合 基于，免费的生物信息学管道，该管道将由（Milosavljevic博士，Aleksandar博士，贝勒学院，贝勒学院 药品）。我们协作努力的结果将为科学界提供：i）新方法 EV分类，特异性蛋白的检测，RNA/ssDNA分子在EV亚群上具有灵敏度 目前，任何大规模技术，ii）和跨整个标准化所必需的协议无关 实验室和转化为临床实践，iii）提取微妙的生物信息学基础设施 多参数分析中存在的相关数据。 重要的是，科学界将能够使用我们团队生产的每个组成部分 共同用于全面的电动汽车子集分析，或作为独立工具。