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

在美国，每年有280万人因创伤性脑损伤而寻求医疗照顾。 创伤（TBI），其中80%被认为具有轻度TBI（即脑震荡）。即使在那些 轻微受伤，10-20%的人将遭受长期残疾，包括癫痫发作， 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， 损伤常规成像研究，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TBI的特征。 损伤的全部程度，特别是对于那些轻伤的患者。目前，只有少数 分子标志物，以帮助评估一个人的独特的伤害和随后的 恢复和生物标志物是迫切需要的领域，与这些不同的 内表型，跟踪疾病的进展，并预测临床结果。解决 为了应对这一挑战，我们建议开发一种基于微芯片的平台， 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），包括外来体。不像以前的工作，主要集中在 单一生物标志物，我们的方法测量了一组循环EV miRNA标记物， 通过机器学习算法，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异性纳米磁性隔离， 来自各种细胞类型的脑源性EV，使用RNA测序的生物标志物发现，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

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.