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

基本信息

批准号：
10019696
负责人：
David Aaron Issadore
金额：
$ 60.32万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
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/10019696
关键词：
Address Advanced Development Archives 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 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 outcome forecast point of care precision medicine predict clinical outcome prognostic prospective ratiometric research clinical testing response to injury technology development transcriptome sequencing vascular injury

项目摘要

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280万人为创伤性大脑寻求医疗护理伤害（TBI），其中80％被认为具有轻度TBI（即脑震荡）。即使在那些受到轻度伤害，10-20％的个体将遭受长期残疾，包括癫痫发作和情感和行为问题。 TBI护理中的主要挑战之一是适当的对这种异质损伤进行分类，并确定有这些慢性的患者障碍。常规成像研究，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，但不能可靠地捕获受伤的全部程度，特别是在那些轻度受伤的患者中。目前，很少分子标记物以协助评估个人的独特伤害，然后恢复和生物标志物在现场迫切需要与这些不同的不同内表型，跟踪疾病的进展并预测临床结果。解决这项挑战，我们建议开发一个基于微芯片的平台，可用于使用在脑衍生的循环中发现的RNA货物来表征TBI及其恢复细胞外囊泡（EV），包括外泌体。与以前主要关注的工作不同单个生物标志物，我们的方法测量了处理的一组循环EV miRNA标记。使用机器学习算法，更全面地捕获受伤的状态和恢复大脑。我们的建议结合了表面标记特异性的纳米磁隔离来自多种细胞类型的大脑衍生的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 TBI。