Evidence map›Paper›PMID 39776353›Full record

ArticleJournal of extracellular vesicles2025

DetectEV: A functional enzymatic assay to assess integrity and bioactivity of extracellular vesicles.

Giorgia Adamo, Sabrina Picciotto, Paola Gargano, Angela Paterna, Samuele Raccosta, Estella Rao, Daniele Paolo Romancino, Giulio Ghersi, Mauro Manno, Monica Salamone and 1 more

Erratum issuedAbstract read
In one paragraph

Article in Journal of extracellular vesicles, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Review
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  3. Natural Product-Derived Vesicles in Nanotherapeutics.Journal of extracellular vesicles · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Giorgia AdamoCell-Tech HUB and Institute for Research and Biomedical Innovation (IRIB), National Research Council of Italy (CNR), Palermo, Italy.ORCID https://orcid.org/0000-0002-6887-763X
Sabrina PicciottoCell-Tech HUB and Institute for Research and Biomedical Innovation (IRIB), National Research Council of Italy (CNR), Palermo, Italy.
Paola GarganoCell-Tech HUB and Institute for Research and Biomedical Innovation (IRIB), National Research Council of Italy (CNR), Palermo, Italy.
Angela PaternaCell-Tech HUB and Institute of Biophysics (IBF), National Research Council of Italy (CNR), Palermo, Italy.
Samuele RaccostaCell-Tech HUB and Institute of Biophysics (IBF), National Research Council of Italy (CNR), Palermo, Italy.
Estella RaoCell-Tech HUB and Institute of Biophysics (IBF), National Research Council of Italy (CNR), Palermo, Italy.
Daniele Paolo RomancinoCell-Tech HUB and Institute for Research and Biomedical Innovation (IRIB), National Research Council of Italy (CNR), Palermo, Italy.
Giulio GhersiDepartment of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, Palermo, Italy.
Mauro MannoCell-Tech HUB and Institute of Biophysics (IBF), National Research Council of Italy (CNR), Palermo, Italy.
Monica SalamoneCell-Tech HUB and Institute for Research and Biomedical Innovation (IRIB), National Research Council of Italy (CNR), Palermo, Italy.
Antonella BongiovanniCell-Tech HUB and Institute for Research and Biomedical Innovation (IRIB), National Research Council of Italy (CNR), Palermo, Italy.

Funding

Consiglio Nazionale delle Ricerche @IRIB2023European Union's Horizon 2020 research and innovation programme 801338European Union's Horizon 2020 research and innovation programme 952183MUR PNRR "National Center for Gene Therapy and Drugs based on RNA Technology CN00000041 CN3 RNA
6 · The paper itself

Abstract

The application of extracellular vesicles (EVs) as therapeutics or nanocarriers in cell-free therapies necessitates meticulous evaluations of different features, including their identity, bioactivity, batch-to-batch reproducibility, and stability. Given the inherent heterogeneity in EV preparations, this assessment demands sensitive functional assays to provide key quality control metrics, complementing established methods to ensure that EV preparations meet the required functionality and quality standards. Here, we introduce the detectEV assay, an enzymatic-based approach for assessing EV luminal cargo bioactivity and membrane integrity. This method is fast, cost-effective, and quantifiable through enzymatic units. Utilizing microalgae-derived EVs, known as nanoalgosomes, as model systems, we optimised the assay parameters and validated its sensitivity and specificity in quantifying the enzymatic activity of esterases within the EV lumen while also evaluating EV membrane integrity. Compared to conventional methods that assess physicochemical features of EVs, our single-step analysis efficiently detects batch-to-batch variations by evaluating changes in luminal cargo bioactivity and integrity across various EV samples, including differences under distinct storage conditions and following diverse isolation and exogenous loading methods, all using small sample sizes. The detectEV assay's application to various human-derived EV types demonstrated its versatility and potential universality. Additionally, the assay effectively predicted EV functionality, such as the antioxidant activity of different nanoalgosome batches. Our findings underscore the detectEV assay's utility in comprehensive characterization of EV functionality and integrity, enhancing batch-to-batch reproducibility and facilitating their therapeutic applications.

Indexed as

Enzyme AssaysExtracellular VesiclesEsterasesHumansMicroalgaeReproducibility of ResultsEsterasesbioactivityextracellular vesiclesfunctional enzymatic assayintegrityquality checkstandardization

Identifiers

PMID39776353
PMCPMC11705427

What OpenQuestion holds

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Registered trials

None linked

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.