Extracellular vesicles (EVs) are nanoparticles encapsulated with a lipid bilayer, and they constitute an excellent source of biomarkers for multiple diseases. However, the heterogeneity in their molecular compositions constitutes a major challenge for their recognition and profiling, thereby limiting their application as an effective biomarker. A single-EV analysis technique is crucial to both the discovery and the detection of EV subpopulations that carry disease-specific signatures. Herein, a plasmonic nanohole array is designed for capturing single EVs and subsequently performing fluorescence detection of their membrane proteins by exploiting plasmonic amplification of the fluorescence signal. Unlike other reported methods, our design relies on an exclusive detection of single EVs captured inside nanoholes, thus allowing us to study only plasmonic effects and avoid other metal-induced phenomena while leveraging on the proximity of emitters to the plasmonic hotspots. The method is optimized through numerical simulations and verified by a combination of atomic force, scanning electron microscopy, and fluorescence microscopy. Fluorescence enhancement is then estimated by measuring the CD9 expression of small EVs derived from the human embryonic kidney (HEK293) cell line and carefully considering the spatial distribution of emission and excitation intensities. Fluorescence intensities of immunostained EVs show a moderate overall enhancement of intensity and follow the intensity trend predicted by simulation for nanohole arrays with different nanohole periods. Moreover, the number of observed EVs in the best-performing nanohole array increases by more than 12 times compared with EVs immobilized on a reference substrate, uncovering a vast amount of weakly fluorescent EVs that would remain undetected with the regular fluorescent method. Our nanohole array provides a basis for a future platform of single-EV analyses, also promising to capture the signature arising from low-expressing proteins.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11696387 | PMC |
http://dx.doi.org/10.1021/acsomega.4c05492 | DOI Listing |
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