Ultrasensitive Single Extracellular Vesicle Detection Using High Throughput Droplet Digital Enzyme-Linked Immunosorbent Assay.

Extracellular vesicles (EVs) have attracted enormous attention for their diagnostic and therapeutic potential. However, it has proven challenging to achieve the sensitivity to detect individual nanoscale EVs, the specificity to distinguish EV subpopulations, and a sufficient throughput to study EVs among an enormous background. To address this fundamental challenge, we developed a droplet-based optofluidic platform to quantify specific individual EV subpopulations at high throughput. The key innovation of our platform is parallelization of droplet generation, processing, and analysis to achieve a throughput (∼20 million droplets/min) more than 100× greater than typical microfluidics. We demonstrate that the improvement in throughput enables EV quantification at a limit of detection = 9EVs/µL, a >100× improvement over gold standard methods. Additionally, we demonstrate the clinical potential of this system by detecting human EVs in complex media. Building on this work, we expect this technology will allow accurate quantification of rare EV subpopulations for broad biomedical applications.

Biofluid, Imaging, Physiological, and Functional Biomarkers of Mild Traumatic Brain Injury and Subconcussive Head Impacts.

Post-concussive symptoms are frequently reported by individuals who sustain mild traumatic brain injuries (mTBIs) and subconcussive head impacts, even when evidence of intracranial pathology is lacking. Current strategies used to evaluate head injuries, which primarily rely on self-report, have a limited ability to predict the incidence, severity, and duration of post-concussive symptoms that will develop in an individual patient. In addition, these self-report measures have little association with the underlying mechanisms of pathology that may contribute to persisting symptoms, impeding advancement in precision treatment for TBI. Emerging evidence suggests that biofluid, imaging, physiological, and functional biomarkers associated with mTBI and subconcussive head impacts may address these shortcomings by providing more objective measures of injury severity and underlying pathology. Interest in the use of biomarker data has rapidly accelerated, which is reflected by the recent efforts of organizations such as the National Institute of Neurological Disorders and Stroke and the National Academies of Sciences, Engineering, and Medicine to prioritize the collection of biomarker data during TBI characterization in acute-care settings. Thus, this review aims to describe recent progress in the identification and development of biomarkers of mTBI and subconcussive head impacts and to discuss important considerations for the implementation of these biomarkers in clinical practice.

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

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.

Clinical Applications of Extracellular Vesicles in the Diagnosis and Treatment of Traumatic Brain Injury.

Extracellular vesicles (EVs) have emerged as key mediators of cell-cell communication during homeostasis and in pathology. Central nervous system (CNS)-derived EVs contain cell type-specific surface markers and intralumenal protein, RNA, DNA, and metabolite cargo that can be used to assess the biochemical and molecular state of neurons and glia during neurological injury and disease. The development of EV isolation strategies coupled with analysis of multi-plexed biomarker and clinical data have the potential to improve our ability to classify and treat traumatic brain injury (TBI) and resulting sequelae. Additionally, their ability to cross the blood-brain barrier (BBB) has implications for both EV-based diagnostic strategies and for potential EV-based therapeutics. In the present review, we discuss encouraging data for EV-based diagnostic, prognostic, and therapeutic strategies in the context of TBI monitoring and management.

Multi-Dimensional Mapping of Brain-Derived Extracellular Vesicle MicroRNA Biomarker for Traumatic Brain Injury Diagnostics.

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.