| dc.contributor.author | P. Bracht, Jillian W. | |
| dc.contributor.author | Gimenez‑Capitan, Ana | |
| dc.contributor.author | Huang, Chung‑Ying | |
| dc.contributor.author | M Potie, Nicolas Thierry | |
| dc.contributor.author | Pedraz Valdunciel, Carlos | |
| dc.contributor.author | Warren, Sarah | |
| dc.contributor.author | Rosell, Rafael | |
| dc.contributor.author | Molina‑Vila, Miguel A. | |
| dc.date.accessioned | 2024-10-03T07:03:42Z | |
| dc.date.available | 2024-10-03T07:03:42Z | |
| dc.date.issued | 2021-02-12 | |
| dc.identifier.citation | P. Bracht, J.W. et. al. Sci Rep 11, 3712 (2021). [https://doi.org/10.1038/s41598-021-83132-0] | es_ES |
| dc.identifier.uri | https://hdl.handle.net/10481/95450 | |
| dc.description.abstract | Extracellular vesicles (EVs) are double-layered phospholipid membrane vesicles that are released by
most cells and can mediate intercellular communication through their RNA cargo. In this study, we
tested if the NanoString nCounter platform can be used for the analysis of EV-mRNA. We developed
and optimized a methodology for EV enrichment, EV-RNA extraction and nCounter analysis. Then,
we demonstrated the validity of our workflow by analyzing EV-RNA profiles from the plasma of 19
cancer patients and 10 controls and developing a gene signature to differentiate cancer versus control
samples. TRI reagent outperformed automated RNA extraction and, although lower plasma input is
feasible, 500 μL provided highest total counts and number of transcripts detected. A 10-cycle preamplification
followed by DNase treatment yielded reproducible mRNA target detection. However,
appropriate probe design to prevent genomic DNA binding is preferred. A gene signature, created
using a bioinformatic algorithm, was able to distinguish between control and cancer EV-mRNA profiles
with an area under the ROC curve of 0.99. Hence, the nCounter platform can be used to detect mRNA
targets and develop gene signatures from plasma-derived EVs. | es_ES |
| dc.description.sponsorship | European Union’s Horizon 2020 research and innovation program
under the Marie Skłodowska-Curie Grant Agreement ELBA No. 765492 | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Springer Nature | es_ES |
| dc.rights | Atribución 4.0 Internacional | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.title | Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform | es_ES |
| dc.type | journal article | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/765492 | es_ES |
| dc.rights.accessRights | open access | es_ES |
| dc.identifier.doi | 10.1038/s41598-021-83132-0 | |
| dc.type.hasVersion | VoR | es_ES |