Extracellular vesicles as distinct biomarker reservoirs for mild traumatic brain injury diagnosis

Kryshawna Beard; Zijian Yang; Margalit Haber; Miranda Flamholz; Ramon Diaz-Arrastia; Danielle Sandsmark; David F Meaney; David Issadore

doi:10.1093/braincomms/fcab151

Extracellular vesicles as distinct biomarker reservoirs for mild traumatic brain injury diagnosis

Brain Commun. 2021 Jul 8;3(3):fcab151. doi: 10.1093/braincomms/fcab151. eCollection 2021.

Authors

Kryshawna Beard¹, Zijian Yang², Margalit Haber³, Miranda Flamholz³, Ramon Diaz-Arrastia³, Danielle Sandsmark³, David F Meaney^{4

5}, David Issadore^{4

6}

Affiliations

¹ Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
² Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104, USA.
³ Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.
⁴ Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
⁵ Department of Neurosurgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
⁶ Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.

Abstract

Mild traumatic brain injury does not currently have a clear molecular diagnostic panel to either confirm the injury or to guide its treatment. Current biomarkers for traumatic brain injury rely mainly on detecting circulating proteins in blood that are associated with degenerating neurons, which are less common in mild traumatic brain injury, or with broad inflammatory cascades which are produced in multiple tissues and are thus not brain specific. To address this issue, we conducted an observational cohort study designed to measure a protein panel in two compartments-plasma and brain-derived extracellular vesicles-with the following hypotheses: (i) each compartment provides independent diagnostic information and (ii) algorithmically combining these compartments accurately classifies clinical mild traumatic brain injury. We evaluated this hypothesis using plasma samples from mild (Glasgow coma scale scores 13-15) traumatic brain injury patients (n = 47) and healthy and orthopaedic control subjects (n = 46) to evaluate biomarkers in brain-derived extracellular vesicles and plasma. We used our Track Etched Magnetic Nanopore technology to isolate brain-derived extracellular vesicles from plasma based on their expression of GluR2, combined with the ultrasensitive digital enzyme-linked immunosorbent assay technique, Single-Molecule Array. We quantified extracellular vesicle-packaged and plasma levels of biomarkers associated with two categories of traumatic brain injury pathology: neurodegeneration and neuronal/glial damage (ubiquitin C-terminal hydrolase L1, glial fibrillary acid protein, neurofilament light and Tau) and inflammation (interleukin-6, interleukin-10 and tumour necrosis factor alpha). We found that GluR2+ extracellular vesicles have distinct biomarker distributions than those present in the plasma. As a proof of concept, we showed that using a panel of biomarkers comprised of both plasma and GluR2+ extracellular vesicles, injured patients could be accurately classified versus non-injured patients.

Keywords: biomarkers; extracellular vesicles; machine learning; traumatic brain injury.

Grants and funding

R33 MH118170/MH/NIMH NIH HHS/United States