Evidence map›Paper›PMID 41476096›Full record

ArticleScientific reports2025

Immuno-acoustic trapping for extracellular vesicle subpopulations.

Axel Broman, Megan Havers, Roman Sattarov, Thomas Laurell

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Axel Broman *Department of Biomedical Engineering, Lund University, Lund, Sweden. axel.broman@bme.lth.se.
Megan Havers *Department of Biomedical Engineering, Lund University, Lund, Sweden. megan.havers@bme.lth.se.
Roman SattarovDepartment of Clinical Sciences, Lund University, Lund, Sweden.
Thomas LaurellDepartment of Biomedical Engineering, Lund University, Lund, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AcousticsExtracellular VesiclesHumansProteomicsTetraspanin 29Tetraspanin 29Acoustic trappingExtracellular vesiclesImmuno-acoustic trappingImmunoaffinityProteomicsSubpopulation

Identifiers

PMID41476096
PMCPMC12756313

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.