RNA-Seq is not required to determine stable reference genes for qPCR normalization

Abstract

Assessment of differential gene expression by qPCR is heavily influenced by the choice of reference genes. Although numerous statistical approaches have been proposed to determine the best reference genes, they can give rise to conflicting results depending on experimental conditions. Hence, recent studies propose the use of RNA-Seq to identify stable genes followed by the application of different statistical approaches to determine the best set of reference genes for qPCR data normalization. In this study, however, we demonstrate that the statistical approach to determine the best reference genes from commonly used conventional candidates is more important than the preselection of ‘stable’ candidates from RNA-Seq data. Using a qPCR data normalization workflow that we have previously established; we show that qPCR data normalization using conventional reference genes render the same results as stable reference genes selected from RNA-Seq data. We validated these observations in two distinct cross-sectional experimental conditions involving human iPSC derived microglial cells and mouse sciatic nerves. These results taken together show that given a robust statistical approach for reference gene selection, stable genes selected from RNA-Seq data do not offer any significant advantage over commonly used reference genes for normalizing qPCR assays.

Development and Initial Testing of EcoToxChip, a Novel Toxicogenomics Tool for Environmental Management and Chemical Risk Assessment

“…Coenye (2021) reported that approximately 15%–20% of genes were nonconcordant (i.e., directionally opposite or one method resulted in differential expression and the other did not) when comparing differential expression results between qPCR and RNAseq. Sampathkumar et al (2022) also reported correlation coefficients typically ≥0.8 in terms of concordant differential expression (i.e., comparable magnitudes, directionally consistent) between the two approaches. Future studies should compare EcoToxChip differential expression (i.e., fold change) from an exposure experiment with corresponding RNAseq data to further explore this relationship and should include samples for fathead minnow and X. laevis .…”

“…A certain amount of discordance is expected given that qPCR genes with high C t values (e.g., >~32; low expressed) are less robust in general with regard to reproducibility given the constraints of PCR at later cycles and may therefore impact such correlative relationships. Other studies have reported that nonconcordant genes tend to: 1) be associated with transcript‐length bias, whereby longer transcripts have higher counts regardless of expression levels; 2) have fewer exons; and 3) have low expression (i.e., a majority of reads stem from a small set of highly expressed genes and therefore discriminate against low‐expression genes) compared with concordant genes (Everaert et al, 2017; Sampathkumar et al, 2022). In addition, the number of poor quality and multimapping reads was higher for nonconcordant genes.…”

C-Type Natriuretic Peptide Acts as a Microorganism-Activated Regulator of the Skin Commensals Staphylococcus epidermidis and Cutibacterium acnes in Dual-Species Biofilms

“…Hence, potentially the 16S rRNA gene may not be a proper reference for this biofilm community. According to Sampathkumar and colleagues, sometimes choosing the reference based only on RNA-seq data may be an erroneous approach [ 43 ], and additional bioinformatic studies are needed. Additionally, potentially the genes we found are small, have few exons, and are actually expressed lower compared to genes with consistent expression measurements between the two methods, according to another study [ 44 ].…”

Transcriptome-based selection and validation of optimal reference genes in perirenal adipose developing of goat (Capra hircus)

“…Transcriptome sequencing is an important research method for gene expression analysis and screening differentially expressed and functional genes. Notably, screening reference genes using transcriptome data is an effective experimental method for screening reference genes in non-model species ( 32 – 34 ). RPS4X and RPS6 are more stable than traditionally used housekeeping genes in the goat rumen ( 35 ), while NCBP3, SDHA , and PTPRA are more stable than traditionally used housekeeping genes in goat skin tissue ( 10 ).…”