RT-qPCR-Based Quantification of Small Non-Coding RNAs

  • Fjoralba Zeka
  • Pieter Mestdagh
  • Jo Vandesompele
Part of the Methods in Molecular Biology book series (MIMB, volume 1296)


MicroRNAs (miRNAs) are small non-coding RNA molecules that negatively regulate messenger RNA (mRNA) translation into protein. MiRNAs play a key role in gene expression regulation, and their involvement in disease biology is well documented. This has fueled the development of numerous tools for the quantification of miRNA expression levels. These tools are based on three technologies: (microarray) probe hybridization, RNA sequencing, and reverse transcription quantitative polymerase chain reaction (RT-qPCR). In this chapter, we describe a quantification system based on RT-qPCR technology, which is currently considered as the most sensitive, flexible, and accurate method for quantification of not only miRNA but also RNA expression in general. To this purpose, we have divided the protocol in three sections: reverse transcription (RT) reaction, optional preamplification (PA), and finally qPCR. Three quality-control (QC) steps are implemented in this workflow for assessment of RNA extraction efficiency, sample purity (e.g., absence of inhibitors), and inter-run variations, by examining the detection level of different spike-in synthetic miRNAs. We conclude by demonstrating raw data preprocessing and normalization using expression data obtained from high-throughput miRNA profiling of human RNA samples.

Key words

MicroRNA Reverse transcription reaction Preamplification qPCR miScript Quality control 



Evaluation of this workflow was supported by Fournier-Majoie Foundation.


  1. 1.
    Krol J, Loedige I, Filipowicz W (2010) The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet 11:597–610PubMedGoogle Scholar
  2. 2.
    Kozomara A, Griffiths-Jones S (2013) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42:D68–D73CrossRefPubMedCentralPubMedGoogle Scholar
  3. 3.
    Berezikov E (2011) Evolution of microRNA diversity and regulation in animals. Nat Rev Genet 12:846–860CrossRefPubMedGoogle Scholar
  4. 4.
    Soifer HS, Rossi JJ, Sætrom P (2007) MicroRNAs in disease and potential therapeutic applications. Mol Ther 15:2070–2079CrossRefPubMedGoogle Scholar
  5. 5.
    Lesko LJ (2007) Personalized medicine: elusive dream or imminent reality? Clin Pharmacol Ther 81:807–816CrossRefPubMedGoogle Scholar
  6. 6.
    Schwarzenbach H, Hoon DSB, Pantel K (2011) Cell-free nucleic acids as biomarkers in cancer patients. Nat Cell Biol 11:426–437Google Scholar
  7. 7.
    Mestdagh P, Hartmann N, Baeriswyl L, Andreasen D, Bernard N, Chen C, Cheo D, D’Andrade P, DeMayo M, Dennis L, Derveaux S, Feng Y, Fulmer SS, Gerstmayer B, Gouffon J, Grimley C, Lader E, Lee KY, Luo S, Mouritzen P, Narayanan A, Patel S, Peiffer S, Rüberg S, Schroth G, Schuster D, Shaffer JM, Shelton EJ, Silveria S, Ulmanella U, Veeramachaneni V, Staedtler F, Peters T, Guettouche T, Wong L, Vandesompele J (2014) Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat Methods 11:809–815CrossRefPubMedGoogle Scholar
  8. 8.
    Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, Vandesompele J (2009) A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol 10:R64CrossRefPubMedCentralPubMedGoogle Scholar
  9. 9.
    D’haene B, Mestdagh P, Hellemans J, Vandesompele J (2012) MiRNA expression profiling: from reference genes to global mean normalization. Methods Mol Biol 822:261–272CrossRefPubMedGoogle Scholar
  10. 10.
    Ståhlberg A, Håkansson J, Xian X, Semb H, Kubista M (2004) Properties of the reverse transcription reaction in mRNA quantification. Clin Chem 50:509–515CrossRefPubMedGoogle Scholar
  11. 11.
    Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8:R19CrossRefPubMedCentralPubMedGoogle Scholar
  12. 12.
    Aranda R, Dineen SM, Craig RL, Guerrieri RA, Robertson JM (2009) Comparison and evaluation of RNA quantification methods. Anal Biochem 387:122–127CrossRefPubMedGoogle Scholar
  13. 13.
    Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3 research0034.1–1333Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Fjoralba Zeka
    • 1
  • Pieter Mestdagh
    • 1
  • Jo Vandesompele
    • 1
  1. 1.Center for Medical GeneticsGhent UniversityGhentBelgium

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