Evidence map›Paper›PMID 30232365›Full record

ArticleScientific reports2018

Sensitive and rapid quantification of exosomes by fusing luciferase to exosome marker proteins.

Tomoya Hikita, Mamiko Miyata, Risayo Watanabe, Chitose Oneyama

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing 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

51 citing papers in PubMed.

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  10. Fluorogenic RNA-based biomaterials for imaging and tracking the cargo of extracellular vesicles.Journal of controlled release : official journal of the Controlled Release Society · 2024
    Article
  11. Article
  12. International journal of nanomedicine · 2024
    Article
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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

4 authors.

Tomoya HikitaDivision of Cancer Cell Regulation, Aichi Cancer Center Research Institute, Chikusa-ku, Nagoya, Japan.
Mamiko MiyataDivision of Cancer Cell Regulation, Aichi Cancer Center Research Institute, Chikusa-ku, Nagoya, Japan.
Risayo WatanabeDivision of Cancer Cell Regulation, Aichi Cancer Center Research Institute, Chikusa-ku, Nagoya, Japan.
Chitose OneyamaDivision of Cancer Cell Regulation, Aichi Cancer Center Research Institute, Chikusa-ku, Nagoya, Japan. coneyama@aichi-cc.jp.ORCID 0000-0002-5719-9431

Funding

Japan Science and Technology Agency (JST) JP1005457
6 · The paper itself

Abstract

Exosomes have emerged as important mediators of intercellular communication. Although their modes of action have been elucidated, the molecular mechanisms underlying their secretion, sorting of molecules, uptake into recipient cells, and biological distribution in vivo remain elusive. Here, we present a novel system for quantifying secreted exosomes by introducing ectopic or CRISPR/Cas9-mediated knock-in of luciferase-fusion exosome markers such as CD63. This luciferase-based method makes it possible to measure exosomes secreted into the culture medium with high linearity and wide dynamic range in a high-throughput manner. We demonstrate that data obtained by luminescent quantification are well correlated with data obtained by conventional nanoparticle tracking analysis under multiple conditions. In addition, our system is capable of evaluating the recipient cells or tissues that take up exosomes, as well as visualizing exosomes in vivo. The proposed system represents a powerful tool for understanding the molecular mechanisms underlying exosome production, uptake, and long-term distribution.

Indexed as

A549 CellsAnimalsCell LineCRISPR-Cas SystemsExosomesHCT116 CellsHT29 CellsHumansMiceNanoparticlesNanotechnologyTetraspanin 30CD63 protein, humanTetraspanin 30

Identifiers

PMID30232365
PMCPMC6145919

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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.