Evidence map›Paper›PMID 35955677›Full record

ArticleInternational journal of molecular sciences2022

Detailed Characterization of Small Extracellular Vesicles from Different Cell Types Based on Tetraspanin Composition by ExoView R100 Platform.

Kai Breitwieser, Leon F Koch, Tobias Tertel, Eva Proestler, Luisa D Burgers, Christoph Lipps, James Adjaye, Robert Fürst, Bernd Giebel, Meike J Saul

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed
6.2field-weighted citation impact, top 2% of its field
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

48 citing papers in PubMed, 76 citations in OpenAlex.

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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

10 authors at 5 institutions in 1 country.

Kai BreitwieserFachbereich Biologie, Technische Universität Darmstadt, 64287 Darmstadt, Germany.
Leon F KochFachbereich Biologie, Technische Universität Darmstadt, 64287 Darmstadt, Germany.
Tobias TertelInstitute for Transfusion Medicine, University Hospital Essen, University of Duisburg-Essen, 45122 Essen, Germany.
Eva ProestlerFachbereich Biologie, Technische Universität Darmstadt, 64287 Darmstadt, Germany.
Luisa D BurgersInstitute of Pharmaceutical Biology, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany.
Christoph LippsDepartment of Internal Medicine I, Cardiology, Justus-Liebig-University Gießen, 35392 Gießen, Germany.ORCID 0000-0002-9350-5854
James AdjayeInstitute for Stem Cell Research and Regenerative Medicine, Universitätsklinikum Düsseldorf, 40225 Düsseldorf, Germany.ORCID 0000-0002-6075-6761
Robert FürstInstitute of Pharmaceutical Biology, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany.ORCID 0000-0002-9926-7578
Bernd GiebelInstitute for Transfusion Medicine, University Hospital Essen, University of Duisburg-Essen, 45122 Essen, Germany.ORCID 0000-0003-2446-948X
Meike J SaulFachbereich Biologie, Technische Universität Darmstadt, 64287 Darmstadt, Germany.
Technische Universität Darmstadt · DEGoethe University Frankfurt · DEUniversity of Duisburg-Essen · DEDüsseldorf University Hospital · DEJustus-Liebig-Universität Gießen · DE

Funding

Dr. Hans Messer-Foundation Dr. Hans Messer-Foundation Award 2021Dr. Ing. Wilhelm und Maria Kirmser Foundation n/aTechnische Universität Darmstadt Athene Young Investigator programWilhelm Sander-Foundation 2018.115.1
6 · The paper itself

Abstract

Small extracellular vesicles (sEV) hold enormous potential as biomarkers, drug carriers, and therapeutic agents. However, due to previous limitations in the phenotypic characterization of sEV at the single vesicle level, knowledge of cell type-specific sEV signatures remains sparse. With the introduction of next-generation sEV analysis devices, such as the single-particle interferometric reflectance imaging sensor (SP-IRIS)-based ExoView R100 platform, single sEV analyses are now possible. While the tetraspanins CD9, CD63, and CD81 were generally considered pan-sEV markers, it became clear that sEV of different cell types contain several combinations and amounts of these proteins on their surfaces. To gain better insight into the complexity and heterogeneity of sEV, we used the ExoView R100 platform to analyze the CD9/CD63/CD81 phenotype of sEV released by different cell types at a single sEV level. We demonstrated that these surface markers are sufficient to distinguish cell-type-specific sEV phenotypes. Furthermore, we recognized that tetraspanin composition in some sEV populations does not follow a random pattern. Notably, the tetraspanin distribution of sEV derived from mesenchymal stem cells (MSCs) alters depending on cell culture conditions. Overall, our data provide an overview of the cell-specific characteristics of sEV populations, which will increase the understanding of sEV physiology and improve the development of new sEV-based therapeutic approaches.

Indexed as

Extracellular VesiclesMesenchymal Stem CellsBiomarkersTetraspanin 30TetraspaninsBiomarkersTetraspanin 30Tetraspaninscharacterizationphenotypesmall extracellular vesiclestetraspanins

Identifiers

PMID35955677
PMCPMC9369185
OpenAlexW4289315704

What OpenQuestion holds

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LicenceCC BY
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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.