Abstract
MicroRNAs (miRNAs) are small noncoding RNA molecules that function as negative regulators of gene expression. They are essential components of virtually every biological process and deregulated miRNA expression has been reported in a multitude of human diseases including cancer. Owing to their small size (20–22 nucleotides), accurate quantification of miRNA expression is particularly challenging. In this chapter, we present different RT-qPCR technologies that enable whole genome miRNA expression quantification.
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References
Benes, V and Castoldi, M (2010) Expression profiling of microRNA using real-time quantitative PCR, how to use it and what is available, Methods 50, 244–249.
Chen, C Ridzon, D. A Broomer, A. J Zhou, Z Lee, D. H Nguyen, J. T Barbisin, M Xu, N. L Mahuvakar, V. R Andersen, M. R Lao, K. Q Livak, K. J and Guegler, K. J. (2005) Real-time quantification of microRNAs by stem-loop RT-PCR, Nucleic Acids Res 33, e179.
Mestdagh, P Feys, T Bernard, N Guenther, S Chen, C Speleman, F and Vandesompele, J. (2008) High-throughput stem-loop RT-qPCR miRNA expression profiling using minute amounts of input RNA, Nucleic Acids Res 36, e143.
Shi, R and Chiang, V. L. (2005) Facile means for quantifying microRNA expression by real-time PCR, Biotechniques 39, 519–525.
Bustin, S. A Benes, V Garson, J. A Hellemans, J Huggett, J Kubista, M Mueller, R Nolan, T Pfaffl, M. W Shipley, G. L Vandesompele, J and Wittwer, C. T. (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments, Clin Chem 55, 611–622.
Vandesompele, J De Preter, K Pattyn, F Poppe, B Van Roy, N De Paepe, A and Speleman, F. (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes, Genome Biol 3, RESEARCH0034.
Andersen, C. L Jensen, J. L and Orntoft, T. F. (2004) Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets, Cancer Res 64, 5245–5250.
Mestdagh, P Van Vlierberghe, P De Weer, A Muth, D Westermann, F Speleman, F and Vandesompele, J. (2009) A novel and universal method for microRNA RT-qPCR data normalization, Genome Biol 10, R64.
Mestdagh, P Lefever, S Pattyn, F Ridzon, D. A Fredlund, E Fieuw, A Vermeulen, J De Paepe, A Wong, L Speleman, F Chen, C and Vandesompele, J. (in preparation) The microRNA body map: dissecting microRNA function through integrative genomics.
Peltier, H. J and Latham, G. J. (2008) Normalization of microRNA expression levels in quantitative RT-PCR assays: identification of suitable reference RNA targets in normal and cancerous human solid tissues, RNA 14, 844–852.
Hellemans, J Mortier, G De Paepe, A Speleman, F and Vandesompele, J. (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data, Genome Biol 8, R19.
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Mestdagh, P., Derveaux, S., Vandesompele, J. (2012). Whole-Genome RT-qPCR MicroRNA Expression Profiling. In: Kaufmann, M., Klinger, C. (eds) Functional Genomics. Methods in Molecular Biology, vol 815. Springer, New York, NY. https://doi.org/10.1007/978-1-61779-424-7_10
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DOI: https://doi.org/10.1007/978-1-61779-424-7_10
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