ABSTRACT: The diagnosis and prognosis of traumatic brain injury (TBI) is complicated by variability in the type and severity of injuries and the multiple endophenotypes that describe each patient's response and recovery to the injury. It has been challenging to capture the multiple dimensions that describe an injury and its recovery to provide clinically useful information. To address this challenge, we have performed an open-ended search for panels of microRNA (miRNA) biomarkers, packaged inside of brain-derived extracellular vesicles (EVs), that can be combined algorithmically to accurately classify various states of injury. We mapped GluR2+ EV miRNA across a variety of injury types, injury intensities, history of injuries, and time elapsed after injury, and sham controls in a pre-clinical murine model (n = 116), as well as in clinical samples (n = 36). We combined next-generation sequencing with a technology recently developed by our lab, Track Etched Magnetic Nanopore (TENPO) sorting, to enrich for GluR2+ EVs and profile their miRNA. By mapping and comparing brain-derived EV miRNA between various injuries, we have identified signaling pathways in the packaged miRNA that connect these biomarkers to underlying mechanisms of TBI. Many of these pathways are shared between the pre-clinical model and the clinical samples, and present distinct signatures across different injury models and times elapsed after injury. Using this map of EV miRNA, we applied machine learning to define a panel of biomarkers to successfully classify specific states of injury, paving the way for a prognostic blood test for TBI. We generated a panel of eight miRNAs (miR-150-5p, miR-669c-5p, miR-488-3p, miR-22-5p, miR-9-5p, miR-6236, miR-219a.2-3p, miR-351-3p) for injured mice versus sham mice and four miRNAs (miR-203b-5p, miR-203a-3p, miR-206, miR-185-5p) for TBI patients versus healthy controls.
