The loss of nerve cell function in the central or peripheral nervous system is usually the first step towards neurodegeneration. In 2016, an estimated 5.4 million Americans were living with Alzheimer’s disease (AD) while approximately a million people are living under the threat of developing Parkinson’s disease (PD) by 2020. The risk of developing a neurodegenerative disorder increases drastically with age. The National Institute on Aging (NIA), one of the 27 Institutes and Centers of NIH, funds extramural research to understand the impact of genetic, biological, clinical, behavioral, social, and economic factors on aging. Dr. Dimitrios Kapogiannis is a leading clinical investigator in the Laboratory of Neurosciences (NIA) and adjunct Associate Professor at the Department of Neurology at Johns Hopkins University. The Kapogiannis lab is interested in translational and clinical studies on cognitive aging with major focus on developing blood-based biomarkers of AD. Apart from publishing pioneering papers in classical neurology journals, Dr. Kapogiannis also features on the editorial boards of many high impact journals such as Aging Research Reviews and Aging and Mechanisms of Disease. We quizzed Dr. Maja Mustapic, Staff Scientist at the Kapogiannis lab, on the present-day status of Extracellular Vesicles (EVs) as circulating biomarkers of neurodegenerative diseases, and the challenges faced by scientific community in bringing EV based diagnostics to the medical forefront.
Dr. Maja Mustapic from the Laboratory of Neurosciences (NIA).
Dr. Mustapic has been actively involved in the field of biomarker discovery. Her current work revolves around identifying neuronal enriched EVs (nEVs) expressing L1CAM from plasma as predictive biomarkers of AD. According to a new paper published in JAMA Neurology, Mustapic and team had conducted a large cohort study on nearly 900 samples collected since 1958 from 128 participants of Baltimore Longitudinal Study of Aging.1 This insightful study on nEV biomarkers could be the first step to predict AD nearly 4 years before its clinical onset with about 90% accuracy. However, Dr. Mustapic recognizes that multiple tissues in brain including the astrocytes could also have a link to age-related brain pathology. For this purpose, the Kapogiannis lab investigated the astrocytic EVs (aEVs) and suggested that the combined data from aEVs and nEVs could markedly enhance the power of biomarkers to diagnose neurodegenerative disorders in vulnerable populations. In another exciting collaboration alongside Wake Forest School of Medicine with Dr. Suzanne Craft, it was found that nEV biomarkers of insulin resistance were interrelated to cognitive changes.2 This outcome was observed in response to low dose insulin administered intra-nasally highlighting the involvement of insulin cascade in neurons of origin.
Altogether, these conclusions point towards the possibility of EVs to serve as early biomarkers in brain pathologies. However, when asked if EVs could clinically be used as markers to predict AD in near future, Dr. Mustapic replied, “We’re close, but we are still not there”. She stresses that EV characterization remains an unaddressed issue and all the mechanisms by which EVs elicit their responses are still not fully known. “The biggest hurdle is the normalization of cell surface markers for EV sub- populations”, adds Dr. Mustapic, remarking on the widespread inconsistencies in EV nomenclature and classification. To rationalise this, she believes that comparing various EV isolation and characterisation techniques will simplify organising signals obtained from heterogeneous sub-populations. This will not only allow interpreting distinctive clinical responses, but also help to contrast method sensitivity and specificity besides EV recovery. “We prefer using Size Exclusion Chromatography (SEC) over precipitation techniques to isolate purer EV sub-populations”, cites Dr. Mustapic to emphasize on balancing EV yield versus EV purity depending on the end goal. The Kapogiannis group is currently investigating the use of smart SEC columns and Automated Fraction Collector (AFC) with advanced functionality. Process automation can significantly streamline the overall workflow, in addition to offering greater accuracy and precision in measurements. SEC columns are a powerful analytical tool providing a standardized, reproducible and robust means of removing non-vesicular proteins from complex biological samples for clinical biomarker research.3,4
