Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform

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 develo...

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Autores principales:	Jillian W. P. Bracht, Ana Gimenez-Capitan, Chung-Ying Huang, Nicolas Potie, Carlos Pedraz-Valdunciel, Sarah Warren, Rafael Rosell, Miguel A. Molina-Vila
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b409b0d38d0943b78e064bba707fd0fb
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spelling	oai:doaj.org-article:b409b0d38d0943b78e064bba707fd0fb2021-12-02T13:30:17ZAnalysis of extracellular vesicle mRNA derived from plasma using the nCounter platform10.1038/s41598-021-83132-02045-2322https://doaj.org/article/b409b0d38d0943b78e064bba707fd0fb2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83132-0https://doaj.org/toc/2045-2322Abstract 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 pre-amplification 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.Jillian W. P. BrachtAna Gimenez-CapitanChung-Ying HuangNicolas PotieCarlos Pedraz-ValduncielSarah WarrenRafael RosellMiguel A. Molina-VilaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jillian W. P. Bracht Ana Gimenez-Capitan Chung-Ying Huang Nicolas Potie Carlos Pedraz-Valdunciel Sarah Warren Rafael Rosell Miguel A. Molina-Vila Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform
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 pre-amplification 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.
format	article
author	Jillian W. P. Bracht Ana Gimenez-Capitan Chung-Ying Huang Nicolas Potie Carlos Pedraz-Valdunciel Sarah Warren Rafael Rosell Miguel A. Molina-Vila
author_facet	Jillian W. P. Bracht Ana Gimenez-Capitan Chung-Ying Huang Nicolas Potie Carlos Pedraz-Valdunciel Sarah Warren Rafael Rosell Miguel A. Molina-Vila
author_sort	Jillian W. P. Bracht
title	Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform
title_short	Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform
title_full	Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform
title_fullStr	Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform
title_full_unstemmed	Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform
title_sort	analysis of extracellular vesicle mrna derived from plasma using the ncounter platform
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b409b0d38d0943b78e064bba707fd0fb
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Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform

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