Novel Biomarkers within Extracellular Vesicles for the Identification of Traumatic Brain Injury

Traumatic brain injury (TBI) is defined by the National Institute of Neurological Disorders and Stroke (NINDS) as “…external physical forces cause damage to the brain, whether from impact, penetrating objects, blast waves or rapid movement of the brain within the skull”. According to the most recent CDC data, there were roughly 214,000 TBI-related hospitalizations and 70,000 TBI-related deaths in 2021. The current diagnosis of TBI includes a neurological exam such as Glasgow Coma Scale (GCS) and imaging tests (CT, MRI). However, these modalities have several limitations, including inter-rater reliability, inconsistencies in diagnosis, and lack of predictive prognosis, all of which highlights the complex pathophysiology of this disease process. Thus, there is an urgent need for identification of novel methods for an early detection and quantitative monitoring of TBI. In this project, we investigated extracellular vesicles (EVs) in blood plasma as a potential source of novel TBI biomarkers. Key characteristics of EVs, including the protection of the packaged cargo that reflects processes occurring in the cell of origin and their ability to cross the blood-brain-barrier (BBB), make them a highly valuable source of biomarkers. We subjected 10–12-week-old C57BL/6J male mice to moderate/severe TBI using the pre-clinical, closed head, weight drop model followed by EVs isolation from blood plasma at multiple time points post injury. We detected time-dependent qualitative and quantitative changes in the biophysical properties of EVs. Furthermore, we discovered TBI induced changes in specific EVs subpopulations of microglia/macrophage CD11b+ and astrocyte ACSA-2+ vesicles post-injury. These temporal dynamics of EVs are also reflected in both mitochondrial DNA content, nuclear DNA content, and brain-derived cellular markers NFL, GFAP, and Iba1. Additionally, we combined a global mass spectrometry proteomics approach with biostatistical analysis and computational Graph Neural Network (GNN)-based modeling to discover a panel of potential novel biomarkers for the detection and severity of TBI. Lastly, we confirmed the dynamic release of mtDNA and its fragments in EVs from neurons using an in-vitro TBI model and EVs derived from glucose oxidase stressed retinal pigment epithelial cells, respectively. Together, our findings indicate that a combination of DNA quantity, SAA, and CFD proteins in EVs may be used as diagnostic tools for the rapid, accurate assessment of TBI detection and its sequelae.