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Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform
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 o...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881020/ https://www.ncbi.nlm.nih.gov/pubmed/33580122 http://dx.doi.org/10.1038/s41598-021-83132-0 |
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author | Bracht, Jillian W. P. Gimenez-Capitan, Ana Huang, Chung-Ying Potie, Nicolas Pedraz-Valdunciel, Carlos Warren, Sarah Rosell, Rafael Molina-Vila, Miguel A. |
author_facet | Bracht, Jillian W. P. Gimenez-Capitan, Ana Huang, Chung-Ying Potie, Nicolas Pedraz-Valdunciel, Carlos Warren, Sarah Rosell, Rafael Molina-Vila, Miguel A. |
author_sort | Bracht, Jillian W. P. |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-7881020 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78810202021-02-16 Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform Bracht, Jillian W. P. Gimenez-Capitan, Ana Huang, Chung-Ying Potie, Nicolas Pedraz-Valdunciel, Carlos Warren, Sarah Rosell, Rafael Molina-Vila, Miguel A. Sci Rep Article 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. Nature Publishing Group UK 2021-02-12 /pmc/articles/PMC7881020/ /pubmed/33580122 http://dx.doi.org/10.1038/s41598-021-83132-0 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Bracht, Jillian W. P. Gimenez-Capitan, Ana Huang, Chung-Ying Potie, Nicolas Pedraz-Valdunciel, Carlos Warren, Sarah Rosell, Rafael Molina-Vila, Miguel A. Analysis of extracellular vesicle mRNA derived from plasma using the nCounter platform |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881020/ https://www.ncbi.nlm.nih.gov/pubmed/33580122 http://dx.doi.org/10.1038/s41598-021-83132-0 |
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