Journal of NanobiotechnologyMolecular BiologyPeer-reviewed journal article
Tetraspanin immunocapture phenotypes extracellular vesicles according to biofluid source but may limit identification of multiplexed cancer biomarkers
Rachel R. MizenkoiD, Terza BrostoffiD, Tatu RojaliniD, Hanna J. KosteriD, Hila S. Swindell, Gary Scott LeiserowitziD, Aijun WangiD, Randy P. CarneyiD
Abstract
Tetraspanin expression of extracellular vesicles (EVs) is often used as a surrogate for their general detection and classification from background contaminants. This common practice typically assumes a consistent expression of tetraspanins across EV sources, thus obscuring subpopulations of variable or limited tetraspanin expression. While some recent studies indicate differential expression of tetraspanins across bulk isolated EVs, here we present analysis of single EVs isolated using various field-standard methods from a variety of in vitro and in vivo sources to identify distinct patterns in colocalization of tetraspanin expression. We report an optimized method for the use of antibodycapture single particle interferometric reflectance imaging sensing (SP-IRIS) and fluorescence detection to identify subpopulations according to tetraspanin expression and compare our findings with nanoscale flow cytometry. Using SP-IRIS and immunofluorescence, we report that tetraspanin profile is consistent from a given EV source regardless of isolation method, but that tetraspanin profiles are distinct across various sources. Tetraspanin profiles as measured by flow cytometry do not share similar trends, suggesting that limitations in subpopulation detection significantly impact apparent protein expression. We further analyzed tetraspanin expression of single EVs captured non-specifically, revealing that tetraspanin capture can bias the apparent multiplexed tetraspanin profile. Finally, we demonstrate that this bias can have significant impact on diagnostic sensitivity for tumor-associated EV surface markers. Our findings may reveal key insights into the complexities of the EV biogenesis and signaling pathways and better inform EV capture and detection platforms for diagnostic or other downstream use.
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- FWCI
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- top 1%
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