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Immune stimuli shape the small non-coding transcriptome of extracellular vesicles released by dendritic cells

Overview of attention for article published in Cellular and Molecular Life Sciences, May 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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19 X users

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83 Mendeley
Title
Immune stimuli shape the small non-coding transcriptome of extracellular vesicles released by dendritic cells
Published in
Cellular and Molecular Life Sciences, May 2018
DOI 10.1007/s00018-018-2842-8
Pubmed ID
Authors

Tom A. P. Driedonks, Susanne G. van der Grein, Yavuz Ariyurek, Henk P. J. Buermans, Henrike Jekel, Franklin W. N. Chow, Marca H. M. Wauben, Amy H. Buck, Peter A. C. ‘t Hoen, Esther N. M. Nolte-‘t Hoen

Abstract

The release and uptake of nano-sized extracellular vesicles (EV) is a highly conserved means of intercellular communication. The molecular composition of EV, and thereby their signaling function to target cells, is regulated by cellular activation and differentiation stimuli. EV are regarded as snapshots of cells and are, therefore, in the limelight as biomarkers for disease. Although research on EV-associated RNA has predominantly focused on microRNAs, the transcriptome of EV consists of multiple classes of small non-coding RNAs with potential gene-regulatory functions. It is not known whether environmental cues imposed on cells induce specific changes in a broad range of EV-associated RNA classes. Here, we investigated whether immune-activating or -suppressing stimuli imposed on primary dendritic cells affected the release of various small non-coding RNAs via EV. The small RNA transcriptomes of highly pure EV populations free from ribonucleoprotein particles were analyzed by RNA sequencing and RT-qPCR. Immune stimulus-specific changes were found in the miRNA, snoRNA, and Y-RNA content of EV from dendritic cells, whereas tRNA and snRNA levels were much less affected. Only part of the changes in EV-RNA content reflected changes in cellular RNA, which urges caution in interpreting EV as snapshots of cells. By comprehensive analysis of RNA obtained from highly purified EV, we demonstrate that multiple RNA classes contribute to genetic messages conveyed via EV. The identification of multiple RNA classes that display cell stimulation-dependent association with EV is the prelude to unraveling the function and biomarker potential of these EV-RNAs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 18%
Student > Ph. D. Student 12 14%
Student > Bachelor 11 13%
Student > Master 9 11%
Student > Doctoral Student 6 7%
Other 7 8%
Unknown 23 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 24%
Agricultural and Biological Sciences 12 14%
Medicine and Dentistry 6 7%
Immunology and Microbiology 4 5%
Pharmacology, Toxicology and Pharmaceutical Science 4 5%
Other 14 17%
Unknown 23 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 May 2020.
All research outputs
#2,506,976
of 23,794,258 outputs
Outputs from Cellular and Molecular Life Sciences
#345
of 4,151 outputs
Outputs of similar age
#53,385
of 332,273 outputs
Outputs of similar age from Cellular and Molecular Life Sciences
#8
of 47 outputs
Altmetric has tracked 23,794,258 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,151 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,273 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.