Abstract
Reverse transcription, quantitative polymerase chain reaction (RT-qPCR) is a powerful technique to quantify gene expression by transcript abundance. Expression of target genes is normalized to expression of stable reference genes to account for sample preparation variability. Thus, the identification and validation of stably expressed reference genes is crucial for making accurate, quantitative, statistical conclusions in gene expression studies. Traditional housekeeping genes identified decades ago based on high and relatively stable expression are often used, although many have shown these to not be valid, particularly in highly dynamic systems such as stem cell differentiation. In this study we outline a rational approach to identify stable reference genes valid throughout human pluripotent stem cell (hPSC) differentiation to hPSC-derived cardiomyocytes (hPSC-CMs). Several publicly available transcriptomic data sets were analyzed to identify genes with low variability in expression throughout differentiation. These putative novel reference genes were subsequently validated in RT-qPCR analyses to assess their stability under various perturbations, including maturation during extended culture, lactate purification, and various differentiation efficiencies. Expression in hPSC-CMs was also compared with whole human heart tissue. A core set of three novel reference genes (EDF1, DDB1, and ZNF384) exhibited robust stability across the conditions tested, whereas expression of the traditional housekeeping genes tested (ACTB, B2M, GAPDH, and RPL13A) varied significantly under these conditions. Impact statement This article presents an unbiased method for the selection and validation of novel reference genes for real-time quantitative polymerase chain reaction normalization using data from RNA sequencing datasets. This method identified more robust and stable reference genes for gene expression studies during human pluripotent stem cell differentiation to cardiomyocytes than commonly used reference genes. This study also provides a roadmap for identifying reference genes for assessing gene expression during other dynamic cellular processes, including stem cell differentiation to other cell types.