MicroRNA profiling of low concentration extracellular vesicle RNA utilizing NanoString nCounter technology. Issue 1 (28th January 2023)

• Record Type: Journal Article
• Authors: Crossland, Rachel E.; Albiero, Anna; Sanjurjo‐Rodríguez, Clara; Reis, Monica; Resteu, Anastasia; Anderson, Amy E.; Dickinson, Anne M.; Pratt, Arthur G.; Birch, Mark; McCaskie, Andrew W.; Jones, Elena; Wang, Xiao‐nong
• Is Part Of: Journal of extracellular biology. Volume 2:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-28
• Subjects: extracellular vesicle -- microRNA -- NanoString -- profiling; Coated vesicles -- Periodicals; Extracellular matrix -- Periodicals; Cytology -- Periodicals; Molecular biology -- Periodicals; Extracellular Vesicles; Extracellular Matrix; Coated vesicles; Cytology; Extracellular matrix; Periodical; Periodicals; 571.65
• DOI: 10.1002/jex2.72
• Languages: English
• ISSNs: 2768-2811

Abstract

Abstract: Extracellular vesicles (EV) and the microRNAs that they contain are increasingly recognised as a rich source of informative biomarkers, reflecting pathological processes and fundamental biological pathways and responses. Their presence in biofluids makes them particularly attractive for biomarker identification. However, a frequent caveat in relation to clinical studies is low abundance of EV RNA content. In this study, we used NanoString nCounter technology to assess the microRNA profiles of n = 64 EV low concentration RNA samples (180–49125 pg), isolated from serum and cell culture media using precipitation reagent or sequential ultracentrifugation. Data was subjected to robust quality control parameters based on three levels of limit of detection stringency, and differential microRNA expression analysis was performed between biological subgroups. We report that RNA concentrations > 100 times lower than the current NanoString recommendations can be successfully profiled using nCounter microRNA assays, demonstrating acceptable output ranges for imaging parameters, binding density, positive/negative controls, ligation controls and normalisation quality control. Furthermore, despite low levels of input RNA, high‐level differential expression analysis between biological subgroups identified microRNAs of biological relevance. Our results demonstrate that NanoString nCounter technology offers a sensitive approach for the detection and profiling of low abundance EV‐derived microRNA, and may provide a solution for research studies that focus on limited sample material.