Robust RT-qPCR Data Normalization: Validation and Selection of Internal Reference Genes during Post-Experimental Data Analysis

Abstract

Reverse transcription and real-time PCR (RT-qPCR) has been widely used for rapid quantification of relative gene expression. To offset technical confounding variations, stably-expressed internal reference genes are measured simultaneously along with target genes for data normalization. Statistic methods have been developed for reference validation; however normalization of RT-qPCR data still remains arbitrary due to pre-experimental determination of particular reference genes. To establish a method for determination of the most stable normalizing factor (NF) across samples for robust data normalization, we measured the expression of 20 candidate reference genes and 7 target genes in 15 Drosophila head cDNA samples using RT-qPCR. The 20 reference genes exhibit sample-specific variation in their expression stability. Unexpectedly the NF variation across samples does not exhibit a continuous decrease with pairwise inclusion of more reference genes, suggesting that either too few or too many reference genes may detriment the robustness of data normalization. The optimal number of reference genes predicted by the minimal and most stable NF variation differs greatly from 1 to more than 10 based on particular sample sets. We also found that GstD1, InR and Hsp70 expression exhibits an age-dependent increase in fly heads; however their relative expression levels are significantly affected by NF using different numbers of reference genes. Due to highly dependent on actual data, RT-qPCR reference genes thus have to be validated and selected at post-experimental data analysis stage rather than by pre-experimental determination.

Genomic responses to the socio-sexual environment in male Drosophila melanogaster exposed to conspecific rivals

“…For the selected reference genes (α-Tub84b, eif-1a, and CG13220), we next confirmed that the presence plot for each gene matched its corresponding FlyBase model (St Pierre et al 2014). We then selected primers using FlyPrimerBank (Hu et al 2013) and relevant publications (Ling and Salvaterra 2011) and optimized them for qRT-PCR.…”

“…For primer optimization (Eurofins MWG Operon), we produced standard curves using at least five 1:5 dilutions of RNA starting at 50 ng cDNA. Primer sequences were taken from FlyPrimerBank and citations (Ling and Salvaterra 2011;Hu et al 2013). Gene names, primer sequences, amplicon length, efficiency, R 2 of primers are given in Supplemental Table S9.…”

LTR-mediated retroposition as a mechanism of RNA-based duplication in metazoans

“…For LTR retrotransposons, we designed primers on the basis of the canonical sequence (Supplemental Table 9). To determine the internal controls in qRT-PCR, we followed the protocol in Gan et al (2013) and chose the three most transcriptionally constant genes (eIF-1A, Rap2l, and SdhA) out of 26 in Ling and Salvaterra (2011) and Ponton et al (2011). Strand-specific RNAseq data for line 208 were generated by following the dUTP protocol (Levin et al 2010).…”

Autophagy-Derived Alzheimer’s Pathogenesis

“…Basal levels of neuronal autophagy are believed to decrease with age (Komatsu et al, 2007); however, direct evidence supporting this view is absent. In Drosophila brains autophagy activity during normal aging appears to be stable based on observations that no significant changes occurs in expression levels for several autophagyrelated genes (Ling & Salvaterra, 2011b). Moreover, induction of neuronal autophagy in a conditional Drosophila model is protective in young animals, but likely detrimental in older animals (Ling & Salvaterra, 2011a).…”