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

摘要 最近出现了一种新的旁分泌信号模式，其基础是发现细胞外 在正常和病理条件下，囊泡（EV）作为生物信息的细胞间传送器 例如癌症。我们和其他人已经证明EV及其货物可以调节基因表达并改变基因表达。 细胞在不同类型的细胞中发挥作用。此外，在诸如癌症的病理状况期间， EV的组合物改变宿主免疫应答以及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）的分离和分子谱分析是 这对于理解EV的生物起源和使用EV作为疾病状态的生物标志物至关重要。EV RNA和 根据来源组织和产生EV的细胞的生物学状态，蛋白质预期会变化。 目前EV领域中限制其作为疾病标志物使用的一些局限性是： 迫使大量EV分析的方法使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管道，这是耗时昂贵的。新方法 需要旨在提高抗原检测限，表征EV亚群与单 EV分辨率，同时产生可靠和可重复的结果。电动汽车研究中的这些新标准是 新的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测 用于疾病进展、治疗效果和复发。 在本申请中，我们提出了一种旨在简化EV分析的协作方法， i）通过组合检测EV群体中的特异性RNA/ssDNA分子， 纳米流式细胞术和分子信标（Ghiran和Tyagi博士，BIDMC/HMS，和Rutgers大学， ii）使用等离子体共振纳米标签进行EV抗原检测，使用纳米流式细胞术 (Dr. Jones，NCI）和iii）通过专用云整合RNA和蛋白质多维分析- 的，免费的，生物信息学管道，这将提取（博士Milosavljevic，亚历山大，贝勒大学 医学）。我们的合作成果将为科学界提供：i） EV分选，检测EV亚群上的特异性蛋白、RNA/ssDNA分子，具有灵敏度 目前任何大规模技术都无法实现，ii）和跨 实验室和翻译到临床实践，iii）提取微妙但 多参数分析中的相关数据。 重要的是，科学界将能够使用我们团队生产的每一个组件， 用于综合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.