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

摘要 基于识别细胞外信号的发现，最近出现了一种新的旁分泌信号范例 囊泡(EV)在正常和病理条件下都是生物信息的细胞间传输者 比如癌症。我们和其他人已经展示了电动汽车及其货物可以调节基因表达和改变 细胞在各种类型的细胞中发挥功能。此外，在癌症等病理情况下，数量和 EVS的组成改变了宿主的免疫反应，并使继发性肿瘤的行为同步。 健康和疾病中肠道病毒(即RNA、蛋白质、脂类、代谢物)的分离和分子图谱是 对于了解EV的生物发生和将EV用作疾病状态的生物标志物至关重要。EV RNA和 蛋白质预计会根据起源的组织和产生EV的细胞的生物状态而变化。 目前EV领域中限制其作为疾病标志物使用的一些限制是：i)缺乏有效的分类 强制散装EVS对低丰度物种进行偏置检测的方法，II)DNA/RNA/蛋白质 量化方法和生物信息学管道，这些都是耗时和昂贵的。新方法 需要提高抗原检测下限，用单个EV亚群表征 EV分辨率，同时生成可靠且可重现的结果。电动汽车研究中的这些新标准是 基于生物标记物的监测是新的疾病诊断和预后策略的必要前提 用于疾病进展、治疗效果和复发。 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 通过联合检测EV人群中的特定RNA/ssDNA分子来改进抗原检测 纳米流式细胞术和分子信标(Ghiran和Tyagi博士、BIDMC/HMS和罗格斯大学， 分别)，ii)使用等离子体共振纳米标签，使用纳米流式细胞术检测EV抗原 (琼斯博士，NCI)和III)通过专用云集成RNA和蛋白质多维分析- 基于免费的生物信息学管道，它将由(贝勒学院的Milosavljevic博士，Aleksandar 医学)。我们合作努力的结果将为科学界提供：i)新的方法 EV分选，EV亚群上特异性蛋白质、RNA/单链DNA分子的敏感性检测 目前任何大规模技术都无法实现，ii)标准化所需的协议 实验室并转化为临床实践，iii)提取微妙但 多参数分析中的相关数据。 重要的是，科学界也将能够使用我们团队生产的每一个组件 一起用于全面的电动汽车子集分析，或作为独立的工具。

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.