A nanomagnetic platform technology to characterize traumatic brain injury using brain derived extracellular vesicles

使用脑源性细胞外囊泡表征创伤性脑损伤的纳米磁性平台技术

• 批准号: 10261451
• 负责人: David Aaron Issadore
• 金额: $ 57.1万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10261451
• 关键词: Address; Advanced Development; Astrocytes; Axon; Behavioral; Benchmarking; Biological Assay; Biological Markers; Biological Models; Blinded; Blood; Blood Volume; Brain; Brain Concussion; Brain Edema; Brain Injuries; Calibration; Car Phone; Caring; Cell Line; Cellular Phone; Chronic; Classification; Clinical; Color; Data; Development; Devices; Diagnosis; Diagnostic; Disease; Elements; Emotional; Evaluation; Goals; Gold; Human; Image; Impairment; Individual; Inflammation; Inflammatory; Injury; Laboratories; Machine Learning; Magnetic Resonance Imaging; Measures; Methods; MicroRNAs; Modeling; Molecular Profiling; Monitor; Neurons; Nucleic Acids; Outcome; Patients; Performance; Plasma; Process; Production; Prognosis; RNA; RNA Probes; Recording of previous events; Recovery; Research Personnel; Risk; Sampling; Seizures; Severities; Sorting - Cell Movement; Stream; Surface; System; Technology; Testing; Time; Traumatic Brain Injury; United States; Vesicle; Work; X-Ray Computed Tomography; base; biomarker discovery; brain cell; cell type; cost; design; digital; disability; drug efficacy; endophenotype; exosome; extracellular vesicles; imaging study; improved; individual response; injured; injury and repair; injury recovery; innovation; innovative technologies; machine learning algorithm; manufacturability; medical attention; microRNA biomarkers; microchip; mild traumatic brain injury; molecular marker; mouse model; nanomagnetic; nanoscale; neuropathology; next generation; noninvasive diagnosis; nucleic acid detection; point of care; precision medicine; predict clinical outcome; prognostic; prospective; ratiometric; research clinical testing; response to injury; sample archive; technology development; transcriptome sequencing; vascular injury

In the United States, 2.8 million people every year seek medical attention for traumatic brain injury (TBI), of which 80% are considered to have a mild TBI (i.e. concussion). Even in those with mild injuries, 10-20% of individuals will suffer long-term disability including seizures and emotional and behavioral issues. One of the primary challenges in TBI care is appropriately classifying this heterogeneous injury and identifying patients at risk for these chronic impairments. Conventional imaging studies, including magnetic resonance imaging (MRI) and computed tomography (CT), are commonly used to classify TBI, but do not reliably capture the full extent of the injury, particularly in those patients with mild injuries. Currently, there are few molecular markers to assist in the assessment of an individual's unique injury and subsequent recovery and biomarkers are desperately needed in the field that correlate with these varied endophenotypes, track the progress of the disease, and predict clinical outcomes. To address this challenge, we propose to develop a microchip-based platform that can be used to characterize TBI and its recovery using the RNA cargo found in brain-derived circulating extracellular vesicles (EVs), including exosomes. Unlike prior work that has mainly focused on single biomarkers, our approach measures a panel of circulating EV miRNA markers processed with machine learning algorithms, to more comprehensively capture the state of the injured and recovering brain. Our proposal combines surface marker-specific nanomagnetic isolation of brain-derived EVs from a variety of cell types, biomarker discovery using RNA sequencing, and machine learning processing of EV miRNA cargo to measure the state of injury and recovery in TBI.

在美国，每年有 280 万人因脑外伤寻求医疗救助 损伤（TBI），其中 80% 被认为患有轻度 TBI（即脑震荡）。即使在那些 轻度损伤时，10-20% 的人会遭受长期残疾，包括癫痫发作和 情绪和行为问题。 TBI 护理的主要挑战之一是适当地 对这种异质性损伤进行分类并识别有这些慢性损伤风险的患者 损伤。传统成像研究，包括磁共振成像 (MRI) 和 计算机断层扫描 (CT) 通常用于对 TBI 进行分类，但不能可靠地捕获 损伤的全部程度，特别是对于那些轻伤的患者。目前，有少数 分子标记有助于评估个体的独特损伤和随后的损伤 该领域迫切需要与这些不同的相关的恢复和生物标志物 内表型、跟踪疾病进展并预测临床结果。致地址 针对这一挑战，我们建议开发一个基于微芯片的平台，可用于 使用脑源性循环中发现的 RNA 货物表征 TBI 及其恢复 细胞外囊泡（EV），包括外泌体。与之前主要关注的工作不同 单一生物标志物，我们的方法测量一组经过处理的循环 EV miRNA 标志物 借助机器学习算法，更全面地捕捉受伤者的状态 恢复大脑。我们的建议结合了表面标记特定的纳米磁性隔离 来自多种细胞类型的脑源性 EV、使用 RNA 测序发现生物标志物，以及 EV miRNA 货物的机器学习处理，以测量损伤和恢复的状态 创伤性脑损伤。