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 researchers from Cedars Sinai Medical Center and Capricor Therapeutics 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, researchers 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.
Workflow for the reference gene identification method
Reference genes were identified using small RNA sequencing and a chip-based method. Each data set was unique and included different donors and diverse conditions. Identification of referencegenes from each data set was conducted in parallel using the four major algorithms for reference gene identification (NormFinder, GeNorm, BestKeeper and Delta Ct). (a) Common microRNAs (miRs) were selected from each set for further validation using reverse transcription–quantitative polymerase chain reaction (RT-qPCR) in a third unique sample set. (b) Venn diagram showing miRs identified by sequencing compared to those identified by NanoString. Data are representative of the two donors in common between data sets 1 and 2.