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

Affiliation

Bracht JWP(1)(2), Gimenez-Capitan A(3), Huang CY(4), Potie N(5)(6), Pedraz-Valdunciel C(7)(8), Warren S(4), Rosell R(8), Molina-Vila MA(9).
Author information:
(1)Pangaea Oncology, Laboratory of Oncology, Quirón Dexeus University Hospital, Sabino Arana 5-19, 08028, Barcelona, Spain. [Email]
(2)Department of Biochemistry, Molecular Biology and Biomedicine, Universitat Autónoma de Barcelona
(UAB), 08193, Cerdanyola, Spain. [Email]
(3)Pangaea Oncology, Laboratory of Oncology, Quirón Dexeus University Hospital, Sabino Arana 5-19, 08028, Barcelona, Spain.
(4)NanoString Technologies, Seattle, WA, USA.
(5)Department of Genetics, Faculty of Science, University of Granada, 18071, Granada, Spain.
(6)Bioinformatics Laboratory, Biotechnology Institute, Centro de Investigacion Biomedica, PTS, Avda. del Conocimiento s/n, 18100, Granada, Spain.
(7)Department of Biochemistry, Molecular Biology and Biomedicine, Universitat Autónoma de Barcelona
(UAB), 08193, Cerdanyola, Spain.
(8)Germans Trias i Pujol Health Sciences Institute and Hospital
(IGTP), Badalona, Barcelona, Spain.
(9)Pangaea Oncology, Laboratory of Oncology, Quirón Dexeus University Hospital, Sabino Arana 5-19, 08028, Barcelona, Spain. [Email]

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.