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J Extracell Vesicles. 2019 Jun 19;8(1):1597603. doi: 10.1080/20013078.2019.1597603. eCollection 2019.

High-fidelity detection and sorting of nanoscale vesicles in viral disease and cancer.

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Vaccine Branch, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, USA.
Laboratory of Pathology, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, USA.
Experimental Immunology and Transplantation Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
Wake Forest School of Medicine Flow Cytometry Core, Winston Salem, NC, USA.
Stanford University School of Medicine, Stanford, CA, USA.
Beckman Coulter, Fort Collins, CO, USA.
Beth Israel Deaconess Medical Center, Boston, MA, USA.
University of California, Davis, Sacramento, CA, USA.
QuantaCyte Corporation, NJ, USA.
Radiation Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
Dana-Farber Cancer Institute, Boston, MA, USA.
Vaccine Research Center, National Institute of Allergy and Infectious Disease, NIH, Bethesda, MD, USA.
Developmental Therapeutics Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
Basic Research Lab, National Cancer Institute, NIH, Frederick, MD, USA.
Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA.
Theranostic Nanomedicine Section, National Institute of Biomedical Imaging and Bioengineering, NIH, Bethesda, MD, USA.


Biological nanoparticles, including viruses and extracellular vesicles (EVs), are of interest to many fields of medicine as biomarkers and mediators of or treatments for disease. However, exosomes and small viruses fall below the detection limits of conventional flow cytometers due to the overlap of particle-associated scattered light signals with the detection of background instrument noise from diffusely scattered light. To identify, sort, and study distinct subsets of EVs and other nanoparticles, as individual particles, we developed nanoscale Fluorescence Analysis and Cytometric Sorting (nanoFACS) methods to maximise information and material that can be obtained with high speed, high resolution flow cytometers. This nanoFACS method requires analysis of the instrument background noise (herein defined as the "reference noise"). With these methods, we demonstrate detection of tumour cell-derived EVs with specific tumour antigens using both fluorescence and scattered light parameters. We further validated the performance of nanoFACS by sorting two distinct HIV strains to >95% purity and confirmed the viability (infectivity) and molecular specificity (specific cell tropism) of biological nanomaterials sorted with nanoFACS. This nanoFACS method provides a unique way to analyse and sort functional EV- and viral-subsets with preservation of vesicular structure, surface protein specificity and RNA cargo activity.


Nanofacs; extracellular vesicles; flow cytometry; phenotyping; sorting

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