ArticleScientific reports2025
Immuno-acoustic trapping for extracellular vesicle subpopulations.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extracellular vesicles (EVs) in biofluids are heterogeneous in origin, surface protein expression, and biomolecular cargo. Isolation of specific EV subpopulations may enable studies of biomarkers originating from specific cell phenotypes and disease states. Acoustic trapping is a rapid, automatable EV isolation technique requiring minimal sample preprocessing; however, it lacks the specificity needed for some clinical studies. Combining acoustic trapping with immunoaffinity bead-based isolation could provide access to more specific EV content and broaden the applications of acoustic trapping. Here, we present a novel immuno-acoustic trapping methodology implemented on an automated platform with simple design. EVs were rapidly isolated (8 min) from ~ 17 µL blood plasma, generating two EV fractions from a single trapping run: acoustically trapped EVs and EVs bound specifically to anti-CD9 silica beads. We benchmark our technique against a manual (90 min) immunoaffinity isolation with multiple centrifugation washes. Nanoparticle tracking analysis and transmission electron microscopy with CD9+ immunogold labeling confirmed isolation of intact EVs. Quantitative proteomic profiling showed that immuno-acoustic fractions contained proteins more strongly associated with CD9 than both acoustically and immunoaffinity-isolated fractions. This methodology can be adapted to isolate specific EV subpopulations expressing other surface proteins while simultaneously collecting a broad EV population as background reference.
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