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

Area of Science:

• Molecular Biology
• Biotechnology
• Genomics

Background:

• Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is a sensitive gene expression analysis method.
• Accurate normalization using stable reference genes is essential for RT-qPCR reliability.
• Extracellular vesicle (EV) research requires specific strategies for gene expression analysis due to unique RNA content.

Purpose of the Study:

• To develop and validate a comprehensive strategy for identifying stable reference genes in cardiosphere-derived cells for EV research.
• To establish reliable normalization methods for microRNA (miR) expression analysis in EVs.

Main Methods:

• Utilized RNA sequencing and NanoString chip-based methods to identify candidate reference genes.
• Applied four major reference gene evaluation algorithms: NormFinder, GeNorm, BestKeeper, and Delta Ct method.
• Validated candidate genes using RT-qPCR and determined the most stable normalization strategy through a weighted geometric mean approach.

Main Results:

• RNA sequencing identified miR-101-3p, miR-23a-3p, and miR-26a-5p as stable candidates.
• NanoString analysis indicated miR-23a, miR-217, and miR-379 as stable.
• RT-qPCR validation confirmed that the geometric mean of miR-23a-3p, miR-101-3p, and miR-26a-5p is the most stable normalization strategy for EVs.

Conclusions:

• A comprehensive approach combining diverse data sets and multiple algorithms reliably identifies stable reference genes.
• The validated normalization strategy using miR-23a-3p, miR-101-3p, and miR-26a-5p enhances the utility of gene expression evaluation in therapeutically relevant EVs.
• This study provides a robust method for improving the accuracy and reproducibility of EV-based gene expression studies.