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Title: Facile, generic capture and on-fiber differentiation of exosomes via confocal immunofluorescence microscopy using a capillary-channeled polymer fiber solid-phase extraction tip
There is great interest in advancing methodologies for the isolation and characterization of exosomes (30–150 nm, extracellular vesicles (EVs)) for fundamental biochemical research and liquid biopsy applications. This is due to the accessibility of exosomal surface biomarkers, providing relevant biochemical information from their cells of origin. Exosome-based techniques hold potential for diagnostic applications through less invasive sampling ( versus the physical extraction methods of pathology). This study demonstrates a simple spin-down tip methodology for generic exosome capture, followed by immunoaffinity-based fluorescent labeling to classify EVs captured on a polyester capillary-channeled polymer (C-CP) fiber stationary phase. An antibody to the generic EV tetraspanin protein (CD81) is employed to confirm the presence of biologically active EVs on the fiber surface. An antibody to the CA125 protein, upregulated in the case of ovarian cell stress, is included as a cancer marker protein. Scanning electron microscopy and confocal fluorescence microscopy were performed directly on the capture fibers to visualize the morphology and assess the bioactivity/identity of captured vesicles. This report provides a proof-of-concept for an efficient means of isolating, purifying, immunolabeling, and fluorescent imaging for the biomarker assessment of extracellular vesicles on a single platform . Herein lies the novelty of the overall approach. The ability to affect the entire isolation, immunolabeling, and imaging process in <5 hours is demonstrated. The C-CP fiber spin-down tip is an efficient exosome isolation methodology for microliter samples from diverse media (human urine and cell culture media here) towards diverse means of characterization and identification.  more » « less
Award ID(s):
2107882
NSF-PAR ID:
10345365
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Sensors & Diagnostics
Volume:
1
Issue:
3
ISSN:
2635-0998
Page Range / eLocation ID:
525 to 533
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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