RNA integrity and the effect on the real-time qRT-PCR performance

Physiology Weihenstephan, Center of Life and Food Sciences (ZIEL), Technical University of Munich, 85350 Freising, Germany.
Molecular Aspects of Medicine (Impact Factor: 10.24). 04/2006; 27(2-3):126-39. DOI: 10.1016/j.mam.2005.12.003
Source: PubMed


The assessment of RNA integrity is a critical first step in obtaining meaningful gene expression data. Working with low-quality RNA may strongly compromise the experimental results of downstream applications which are often labour-intensive, time-consuming, and highly expensive. Using intact RNA is a key element for the successful application of modern molecular biological methods, like qRT-PCR or micro-array analysis. To verify RNA quality nowadays commercially available automated capillary-electrophoresis systems are available which are on the way to become the standard in RNA quality assessment. Profiles generated yield information on RNA concentration, allow a visual inspection of RNA integrity, and generate approximated ratios between the mass of ribosomal sub-units. In this review, the importance of RNA quality for the qRT-PCR was analyzed by determining the RNA quality of different bovine tissues and cell culture. Independent analysis systems are described and compared (OD measurement, NanoDrop, Bioanalyzer 2100 and Experion). Advantage and disadvantages of RNA quantity and quality assessment are shown in performed applications of various tissues and cell cultures. Further the comparison and correlation between the total RNA integrity on PCR performance as well as on PCR efficiency is described. On the basis of the derived results we can argue that qRT-PCR performance is affected by the RNA integrity and PCR efficiency in general is not affected by the RNA integrity. We can recommend a RIN higher than five as good total RNA quality and higher than eight as perfect total RNA for downstream application.

Download full-text


Available from: Michael W Pfaffl
  • Source
    • "Total RNA of enterospheres of both groups was extracted using the RNeasy Micro Kit (Qiagen). RNA quality was evaluated on Agilent 2100 Bioanalyzer with RNA integrity numbers (RIN) of the samples in this study being in the range from 8 to 10. RIN numbers higher than 8 are considered optimal for downstream application [16]. Double-stranded cDNA was synthesized from 100 ng of total RNA, subsequently linearly amplified, and biotinylated using the GeneChip WT cDNA Synthesis and Amplification Kit (Affymetrix, Santa Clara, CA, USA) according to the manufacturer's instructions. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Postnatal neural progenitor cells of the enteric nervous system are a potential source for future cell replacement therapies of developmental dysplasia like Hirschsprung’s disease. However, little is known about the molecular mechanisms driving the homeostasis and differentiation of this cell pool. In this work, we conducted Affymetrix GeneChip experiments to identify differences in gene regulation between proliferation and early differentiation of enteric neural progenitors from neonatal mice. We detected a total of 1333 regulated genes that were linked to different groups of cellular mechanisms involved in cell cycle, apoptosis, neural proliferation, and differentiation. As expected, we found an augmented inhibition in the gene expression of cell cycle progression as well as an enhanced mRNA expression of neuronal and glial differentiation markers. We further found a marked inactivation of the canonical Wnt pathway after the induction of cellular differentiation. Taken together, these data demonstrate the various molecular mechanisms taking place during the proliferation and early differentiation of enteric neural progenitor cells.
    Full-text · Article · Jan 2016 · Stem cell International
  • Source
    • "Upon purification, concentration and quality of all samples were assessed using the NanoDrop ND-1000 spectrophotometer (Thermo Scientific) and the 2100 Bioanalyzer (Agilent). RNA integrity values of samples were above the recommended minimum value of 5 for quantitative real-time RT PCR applications and ranged from 8.5 to 9.8 (Fleige and Pfaffl, 2006). Total cDNA was prepared using Maxima First Strand cDNA Synthesis Kit for RT-qPCR (Thermo Scientific). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Aromatase cytochrome P450arom (cyp19) is the only enzyme that has the ability to convert androgens into estrogens. Estrogens, which are produced locally in the vertebrate brain play many fundamental roles in neuroendocrine functions, reproductive functions, socio-sexual behaviors, and neurogenesis. Radial glial cells (RGCs) are neuronal progenitor cells that are abundant in fish brains and are the exclusive site of aromatase B expression and neuroestrogen synthesis. Using a novel in vitro RGC culture preparation we studied the regulation of aromatase B by 17β-estradiol (E2) and dopamine (DA). We have established that activation of the dopamine D1 receptor (D1R) by SKF 38393 up-regulates aromatase B gene expression most likely through the phosphorylation of cyclic AMP response element binding protein (CREB). This up-regulation can be enhanced by low concentration of E2 (100 nM) through increasing the expression of D1R and the level of p-CREB protein. However, a high concentration of E2 (1 μM) and D1R agonist together failed to up-regulate aromatase B, potentially due to attenuation of esr2b expression and p-CREB levels. Furthermore, we found the up-regulation of aromatase B by E2 and DA both requires the involvement of esr1 and esr2a. The combined effect of E2 and DA agonist indicates that aromatase B in the adult teleost brain is under tight control by both steroids and neurotransmitters to precisely regulate neuroestrogen levels.
    Full-text · Article · Dec 2015 · Frontiers in Neuroscience
  • Source
    • "Two SVM models were trained for paclitaxel and gemcitabine: one using the normalized gene expression values, and the other using expression values binned into 10 categories, using the Matlab function: quantile(X,10). Binning was performed because amplifiable RNA template concentration in FFPE blocks is not known precisely, because it is subject to long term degradation and reactivity (Antonov et al., 2005;Fleige and Pfaffl, 2006). Expression measurements were obtained for 11 genes from the paclitaxel SVM, and 6 genes for the gemcitabine SVM. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI50) and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients. Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemoresistance to paclitaxel (n = 31 genes) or gemcitabine (n = 18) were analyzed. A multi-factorial, principal component analysis (MFA) indicated expression was the strongest indicator of sensitivity for paclitaxel, and copy number and expression were informative for gemcitabine. The factors were combined using support vector machines (SVM). Expression of 15 genes (ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NKFB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2) predicted cell line sensitivity to paclitaxel with 82% accuracy. Copy number profiles of 3 genes (ABCC10, NT5C, TYMS) together with expression of 7 genes (ABCB1, ABCC10, CMPK1, DCTD, NME1, RRM1, RRM2B), predicted gemcitabine response with 85% accuracy. Expression and copy number studies of two independent sets of patients with known responses were then analyzed with these models. These included tumor blocks from 21 patients that were treated with both paclitaxel and gemcitabine, and 319 patients on paclitaxel and anthracycline therapy. A new paclitaxel SVM was derived from an 11-gene subset since data for 4 of the original genes was unavailable. The accuracy of this SVM was similar in cell lines and tumor blocks (70-71%). The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease.
    Full-text · Article · Sep 2015 · Molecular oncology
Show more