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Abstract
Despite intense interests in developing blood measurements of Alzheimer’s disease (AD), the progress has been confounded by limited sensitivity and poor correlation to brain pathology. Here, we present a dedicated analytical platform for measuring different populations of circulating amyloid β (Aβ) proteins – exosome-bound vs. unbound – directly from blood. The technology, termed amplified plasmonic exosome (APEX), leverages in situ enzymatic conversion of localized optical deposits and double-layered plasmonic nanostructures to enable sensitive, multiplexed population analysis. It demonstrates superior sensitivity (~200 exosomes), and enables diverse target co-localization in exosomes. Employing the platform, we find that prefibrillar Aβ aggregates preferentially bind with exosomes. We thus define a population of Aβ as exosome-bound (Aβ42+ CD63+) and measure its abundance directly from AD and control blood samples. As compared to the unbound or total circulating Aβ, the exosome-bound Aβ measurement could better reflect PET imaging of brain amyloid plaques and differentiate various clinical groups.
Detecting Alzheimer’s disease from blood samples is challenging because amyloid β blood levels are lower than the ELISA detection limit. Here the authors capture amyloid β bound to circulating exosomes on a plasmonic nanosensor, followed by enzymatic amplification to improve detection sensitivity.
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1 National University of Singapore, Department of Biomedical Engineering, Faculty of Engineering, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431); National University of Singapore, Biomedical Institute for Global Health Research and Technology, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431)
2 Agency for Science, Technology and Research, Institute of Microelectronics, Singapore, Singapore (GRID:grid.185448.4) (ISNI:0000 0004 0637 0221)
3 National University of Singapore, Clinical Imaging Research Center, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431)
4 National University of Singapore, Biomedical Institute for Global Health Research and Technology, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431); Agency for Science, Technology and Research, Institute of Molecular and Cell Biology, Singapore, Singapore (GRID:grid.185448.4) (ISNI:0000 0004 0637 0221)
5 National University of Singapore, Biomedical Institute for Global Health Research and Technology, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431); National University Hospital, Department of Laboratory Medicine, Singapore, Singapore (GRID:grid.412106.0) (ISNI:0000 0004 0621 9599)
6 National University Hospital, Memory Ageing and Cognition Center, Singapore, Singapore (GRID:grid.412106.0) (ISNI:0000 0004 0621 9599); National University of Singapore, Department of Pharmacology, Yong Loo Lin School of Medicine, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431)
7 National University of Singapore, Department of Biomedical Engineering, Faculty of Engineering, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431); National University of Singapore, Biomedical Institute for Global Health Research and Technology, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431); Agency for Science, Technology and Research, Institute of Molecular and Cell Biology, Singapore, Singapore (GRID:grid.185448.4) (ISNI:0000 0004 0637 0221); National University of Singapore, Department of Surgery, Yong Loo Lin School of Medicine, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431)