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，最近出现了一种旁分泌信号传导的新范例 囊泡（EV）作为正常和病理条件下生物信息的细胞间传递者 例如癌症。我们和其他人已经证明电动汽车及其货物可以调节基因表达并改变 各种细胞类型的细胞功能。此外，在癌症等病理状况下，数量和 EV 的成分改变宿主免疫反应并同步继发性肿瘤的行为。 健康和疾病中 EV（即 RNA、蛋白质、脂质、代谢物）的分离和分子分析 对于了解 EV 的生物发生以及使用 EV 作为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组织和 EV 产生细胞的生物状态而变化。 目前 EV 领域的一些限制限制了其作为疾病标记物的使用： i) 缺乏有效的分类 强制批量 EV 分析的方法会导致检测偏向于低丰度物种，ii) DNA/RNA/蛋白质 量化方法和生物信息学流程非常耗时且昂贵。新颖的方法 需要旨在提高抗原检测限、单一EV亚群的表征 EV 分辨率，同时生成可靠且可重复的结果。电动汽车研究的这些新标准是 新疾病诊断和预后策略、基于生物标志物的监测的必要先决条件 用于疾病进展、治疗效果和复发。 在本申请中，我们提出了一种协作方法，旨在简化 EV 分析和 通过以下方式改进抗原检测 i) 通过结合检测 EV 群体中的特定 RNA/ssDNA 分子 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）使用等离子共振纳米标签进行 EV 抗原检测，使用纳米流式细胞术 （Jones 博士，NCI）以及 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，该管道将由（贝勒学院 Aleksandar 的 Milosavljevic 博士）提取 医学）。我们合作努力的结果将为科学界提供：i）新方法 EV 分选，高灵敏度检测 EV 亚群上的特定蛋白质、RNA/ssDNA 分子 目前任何大规模技术都无法实现，ii) 和跨领域标准化所需的协议 实验室并转化为临床实践，iii）提取微妙但必要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件 一起进行全面的 EV 子集分析，或作为独立工具。

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.