A comprehensive method for identification of suitable reference genes in extracellular vesicles

• Published in: Journal of extracellular vesicles Vol. 6; no. 1; pp. 1347019 - n/a
• Main Authors: Gouin, Kenneth, Peck, Kiel, Antes, Travis, Johnson, Jennifer Leigh, Li, Chang, Vaturi, Sharon Denise, Middleton, Ryan, de Couto, Geoff, Walravens, Ann-Sophie, Rodriguez-Borlado, Luis, Smith, Rachel Ruckdeschel, Marbán, Linda, Marbán, Eduardo, Ibrahim, Ahmed Gamal-Eldin
• Format: Journal Article
• Language: English
• Published: Sweden Taylor & Francis 01.12.2017; John Wiley & Sons, Inc; Wiley
• Subjects: Carbon dioxide; cardiosphere-derived cells; CDCs; Chromosome 3; Chromosome 5; Datasets; Extracellular vesicles; Gene expression; Genomics; Manufacturers; microRNAs; miRNA; miRs; Physiology; qPCR; reference genes; Reverse transcription; RT-qPCR; Sensitivity analysis; stem cells; United States > US; microRNAs; reference genes; stem cells; Extracellular vesicles; qPCR; cardiosphere-derived cells; RT-qPCR; CDCs; miRs
• ISSN: 2001-3078; 2001-3078
• DOI: 10.1080/20013078.2017.1347019

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is one of the most sensitive, economical and widely used methods for evaluating gene expression. However, the utility of this method continues to be undermined by a number of challenges including normalization using appropriate reference genes. The need to develop tailored and effective strategies is further underscored by the burgeoning field of extracellular vesicle (EV) biology. EVs contain unique signatures of small RNAs including microRNAs (miRs). In this study we develop and validate a comprehensive strategy for identifying highly stable reference genes in a therapeutically relevant cell type, cardiosphere-derived cells. Data were analysed using the four major approaches for reference gene evaluation: NormFinder, GeNorm, BestKeeper and the Delta Ct method. The weighted geometric mean of all of these methods was obtained for the final ranking. Analysis of RNA sequencing identified miR-101-3p, miR-23a-3p and a previously identified EV reference gene, miR-26a-5p. Analysis of a chip-based method (NanoString) identified miR-23a, miR-217 and miR-379 as stable candidates. RT-qPCR validation revealed that the mean of miR-23a-3p, miR-101-3p and miR-26a-5p was the most stable normalization strategy. Here, we demonstrate that a comprehensive approach of a diverse data set of conditions using multiple algorithms reliably identifies stable reference genes which will increase the utility of gene expression evaluation of therapeutically relevant EVs.