Quantification of brain-derived extracellular vesicle microRNAs in blood by a liposome-mediated CRISPR assay for traumatic brain injury detection

摘要 创伤性脑损伤（TBI）每年影响1000万人，其中10-15%的人患有慢性脑损伤。 大脑残疾TBI的诊断仍然是一个临床挑战，因为目前的测试无法对TBI进行分类 由于复杂和独特的损伤状态，无法使用 成像技术或单标记测定。因此，迫切需要一种TBI诊断， 并准确描述TBI的特征，识别有长期脑损伤风险的患者。细胞外囊泡 (EVs)作为TBI的生物标志物具有很大的潜力，因为它们封装了 TBI特异性生物标志物由主要脑细胞类型释放，并且它们的浓度增加 在脑外伤后的血液循环中然而，基于EV的测试利用冗长且多步骤的EV隔离方法， 对于临床实验室使用是不切实际的。为了克服这些障碍，我们开发了脂质体-EV融合系统， 将EV分离和TBI生物标志物检测集成在一个步骤中，以分析一组TBI特异性miRNA 从多种脑细胞类型释放的EV中的标志物（例如，神经元、神经胶质细胞和星形胶质细胞） 洁净.特异性是通过脑细胞类型特异性EV表面标志物与 在装载有超灵敏miRNA指数扩增的脂质体表面上匹配抗体 反应（EXPAR）系统试剂以诱导脂质体-EV融合。在该融合事件之后，EXPAR试剂 与EV miRNA货物混合以产生快速可检测的信号。由于EXPAR反应仅发生在 抗体介导的融合事件，患者样品可以直接分析，而无需裂解或miRNA分离 尽量减少处理或设备要求的程序。初步的血浆和脑脊液数据表明 系统可以区分TBI和非TBI病例中的一组EV标志物。我们建议评估临床 该EXPAR-FDS脂质体测定的实用性通过：1）建立一步测定以直接定量TBI相关的 未处理的患者血浆中脑特异性EV中的标志物;和2）开发快速TBI的预测模型 分类和预测未来恢复以提供关于损伤进展和治疗功效的信息， 并使用来自TBI的配对血浆和CSF样品在纵向队列中进行临床验证， 非TBI患者。这个特征良好的队列将使我们的模型能够立即做出初步预测 并使用后续样本检查预测准确性。这项研究旨在克服目前 通过分析多种脑细胞类型释放的EV中的一组miRNA标记物， 提高临床特异性，并全面表征每种疾病的独特和复杂的疾病状态 TBI后的个人我们相信，我们的精简分析，分析微创血浆样本， 如果没有EV隔离，将允许强大的TBI诊断和后续评估，以避免人员，设备 和现有方法的基础设施要求。

Abstract Traumatic brain injury (TBI) affects 10 million people annually, with 10-15% of these individuals suffering chronic brain disabilities. Diagnosis of TBI remains a clinical challenge because current tests are unable to classify TBI severity or predict patient recovery due to complex and unique injury states that cannot be analyzed fully using imaging techniques or single-marker assays. Thus, there is an urgent need for a TBI diagnosis that can rapidly and accurately characterize TBI and identify patients at risk for long-term brain impairment. Extracellular vesicles (EVs) in minimally invasive sample types have great potential as biomarkers for TBI since they encapsulate biomarker signatures specific to TBI, are released by major brain cell-types, and their concentration increases in the circulation after TBI. However, EV-based tests utilize lengthy and multi-step EV isolation methods that are impractical for clinical lab use. To overcome these obstacles, we developed a liposome-EV fusion system, which integrates EV isolation and TBI biomarker detection in a single step, to analyze a panel of TBI-specific miRNA markers in EVs released from multiple brain cell-types (e.g., neurons, glia, and astrocytes) without prior EV purification. Specificity is achieved through the interaction of brain cell-type-specific EV surface markers with matching antibodies on the surface of liposomes loaded with ultrasensitive miRNA exponential amplification reaction (EXPAR) system reagents to induce liposome-EV fusion. After this fusion event, the EXPAR reagents mix with the EV miRNA cargo to create rapidly detectable signal. Since EXPAR reactions only occur after antibody-mediated fusion events, patient samples can be directly analyzed without lysis or miRNA isolation procedures to minimize handling or equipment requirements. Preliminary plasma and CSF data indicate our system can distinguish a panel of EV markers among TBI and non-TBI cases. We propose to evaluate the clinical utility of this EXPAR-FDS liposome-assay by: 1) establishing a one-step assay to directly quantify TBI-associated markers in brain-specific EVs in unprocessed patient plasma; and 2) developing a predictive model for rapid TBI classification and to predict future recovery to provide information on injury progression and treatment efficacy, and to conduct a clinical validation in a longitudinal cohort using paired plasma and CSF samples from TBI and non-TBI patients. This well-characterized cohort will allow our model to make an initial prediction immediately after injury and check prediction accuracy using follow-up samples. This study aims to overcome current diagnostic limitations by analyzing a panel of miRNA markers in EVs released by multiple brain cell-types to increase clinical specificity and to comprehensively characterize the unique and complex disease state of each individual after a TBI. We believe that our streamlined assay that analyzes minimally invasive plasma samples without EV isolation will permit robust TBI diagnosis and follow-up evaluation to avoid the personnel, equipment and infrastructure requirements of current approaches.