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

通过脂质体介导的 CRISPR 测定对血液中脑源性细胞外囊泡 microRNA 进行定量，用于检测创伤性脑损伤

• 批准号: 10575436
• 负责人: Tony Y. Hu
• 金额: $ 43.73万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10575436
• 关键词: Affect; Antibodies; Astrocytes; Biological Assay; Biological Markers; Blood; Blood Circulation; Blood specimen; Brain; Chronic; Classification; Clinical; Clinical Data; Clustered Regularly Interspaced Short Palindromic Repeats; Collaborations; Complex; Consumption; Craniocerebral Trauma; Cytolysis; Data; Detection; Diagnosis; Diagnostic; Diagnostic tests; Disease; Encapsulated; Ensure; Equipment; Evaluation; Event; Future; Goals; Human Resources; Imaging Techniques; Individual; Infrastructure; Injury; Kinetics; Liposomes; Longitudinal cohort; Malignant Neoplasms; Measures; Mediating; Methods; MicroRNAs; Modeling; Monitor; Neuroglia; Neurological observations; Neurons; Outcome; Patient-Focused Outcomes; Patients; Performance; Persons; Plasma; Population; Procedures; Reaction; Reagent; Recovery; Reproducibility; Risk; Sampling; Severities; Signal Transduction; Specificity; Surface; Symptoms; System; Testing; Time; Traumatic Brain Injury; Treatment Efficacy; Validation; Work; antibody conjugate; base; biomarker signature; brain cell; brain dysfunction; brain tissue; cell type; clinical application; clinical diagnosis; clinical effect; cohort; detection platform; diagnostic strategy; disability; equipment training; extracellular vesicles; follow-up; improved; insight; microRNA biomarkers; mild traumatic brain injury; minimally invasive; multiplex detection; predictive modeling; rapid diagnosis; response; specific biomarkers; vesicular release

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.

抽象的 创伤性脑损伤 (TBI) 每年影响 1000 万人，其中 10-15% 患有慢性疾病 脑部残疾。 TBI 的诊断仍然是一个临床挑战，因为当前的测试无法对 TBI 进行分类 由于复杂且独特的损伤状态无法充分分析，因此可以预测患者的严重程度或恢复情况 成像技术或单标记检测。因此，迫切需要能够快速进行 TBI 诊断。 准确描述 TBI 特征并识别有长期脑损伤风险的患者。细胞外囊泡 微创样本类型中的 EV（EV）具有作为 TBI 生物标志物的巨大潜力，因为它们封装了 TBI 特有的生物标志物特征由主要脑细胞类型释放，并且其浓度增加 TBI 后的循环中。然而，基于 EV 的测试采用冗长且多步骤的 EV 隔离方法，这些方法 对于临床实验室使用来说不切实际。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 多种脑细胞类型（例如神经元、神经胶质细胞和星形胶质细胞）释放的 EV 中的标记物，之前没有 EV 纯化。特异性是通过脑细胞类型特异性 EV 表面标记物与 装载有超灵敏 miRNA 指数扩增的脂质体表面上的匹配抗体 反应（EXPAR）系统试剂诱导脂质体-EV融合。在这次融合事件之后，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，可以直接分析患者样本，无需裂解或分离 miRNA 尽量减少处理或设备要求的程序。初步血浆和脑脊液数据表明我们 系统可以区分 TBI 和非 TBI 病例中的一组 EV 标记。我们建议评估临床 EXPAR-FDS 脂质体测定的实用性如下：1) 建立一种一步测定法来直接量化 TBI 相关的 未处理的患者血浆中脑特异性 EV 的标记物； 2) 开发快速 TBI 的预测模型 分类并预测未来的恢复，以提供有关损伤​​进展和治疗效果的信息， 并使用来自 TBI 和 CSF 的配对血浆和脑脊液样本在纵向队列中进行临床验证 非 TBI 患者。这个特征明确的队列将使我们的模型能够立即做出初步预测 受伤后并使用后续样本检查预测准确性。本研究旨在克服目前 通过分析多种脑细胞类型释放的 EV 中的一组 miRNA 标记物来解决诊断局限性 提高临床特异性并全面表征每种疾病的独特而复杂的疾病状态 TBI 后的个体。我们相信我们的简化分析方法可以分析微创血浆样本 无需 EV 隔离即可进行可靠的 TBI 诊断和后续评估，以避免人员、设备 以及当前方法的基础设施要求。