Dissemin is shutting down on January 1st, 2025

Published in

MDPI, Biomedicines, 4(11), p. 1079, 2023

DOI: 10.3390/biomedicines11041079

Links

Tools

Export citation

Search in Google Scholar

A Strategy for the Selection of RT-qPCR Reference Genes Based on Publicly Available Transcriptomic Datasets

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

In the next-generation sequencing era, RT-qPCR is still widely employed to quantify levels of nucleic acids of interest due to its popularity, versatility, and limited costs. The measurement of transcriptional levels through RT-qPCR critically depends on reference genes used for normalization. Here, we devised a strategy to select appropriate reference genes for a specific clinical/experimental setting based on publicly available transcriptomic datasets and a pipeline for RT-qPCR assay design and validation. As a proof-of-principle, we applied this strategy to identify and validate reference genes for transcriptional studies of bone-marrow plasma cells from patients with AL amyloidosis. We performed a systematic review of published literature to compile a list of 163 candidate reference genes for RT-qPCR experiments employing human samples. Next, we interrogated the Gene Expression Omnibus to assess expression levels of these genes in published transcriptomic studies on bone-marrow plasma cells from patients with different plasma cell dyscrasias and identified the most stably expressed genes as candidate normalizing genes. Experimental validation on bone-marrow plasma cells showed the superiority of candidate reference genes identified through this strategy over commonly employed “housekeeping” genes. The strategy presented here may apply to other clinical and experimental settings for which publicly available transcriptomic datasets are available.