The global burden of Alzheimer's disease (AD) is expected to increase owing to population aging. Current major challenges in AD research include the lack of reliable biomarkers for diagnosis, identification of high-risk groups, and assessment to disease progression. One under-explored opportunity lies in the analysis of extracellular vesicles (EVs), membrane-bound nanovesicles shed by most cells including neuron and glial cells in the central nerve system (CNS). These EVs are abundantly present in easily accessible biofluids (e.g. >108 EVs in blood) and carry molecular cargos (proteins, RNAs, DNAs) reflective of the state of originating cells. Recent studies have suggested EVs are associated with key pathological proteins characterizing neurodegenerative diseases including AD. Therefore, sensitive and accurate detection of EVs and their molecular markers could pave a new way to access the physiological states of cells in CNS and the progression of AD. In the currently funded research (NCI R00CA201248-03, Novel Nano-Plasmonic Technology for Quantitative Analysis of Cancer Exosomes), we developed a highly sensitive EV sensing platform, named ?nPLEX? (nano-plasmonic extracellular vesicles), that can detect and molecularly profile EVs directly from clinical samples. The nPLEX affords EV profiling with high throughput (12 different markers in 60 min) and sensitivity >100 times better than conventional analytical methods. We showed that the platform could detect tumor-derived EVs in plasma samples and identified a signature of EV markers that showed higher sensitivity, specificity and accuracy than the existing serum marker for cancer detection. In response to NOT-AG-18-039 (Alzheimer's-focused administrative supplements for NIH grants that are not focused on Alzheimer's disease), the goal of this proposal is to apply the nPLEX technology to detect and molecularly profile brain-derived EVs for Alzheimer's disease. We hypothesize that the developed nPLEX platform can sensitively detect and profile EVs in various clinical samples (e.g. brain tissues, cerebrospinal fluids, plasma). We anticipate that such applications not only further validate the usefulness and robustness of the developed technology, but also shed light on potential use of EVs as diagnostic and prognostic biomarkers for AD, which will lead to more in-depth studies and accelerate their clinical applications.
Our understanding on the role of extracellular vesicles (EVs) in Alzheimer's diseases is currently limited due to the lack of sensitive detection technologies for EVs. Here, we aim to apply highly sensitive nanoplasmonic sensing technology to detect and molecularly profile brain-derived EVs in clinical samples of patients with Alzheimer's disease.
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