Assessment of RT-qPCR normalization strategies for accurate quantification of extracellular microRNAs in murine serum

PLoS One. 2014 Feb 19;9(2):e89237. doi: 10.1371/journal.pone.0089237. eCollection 2014.


Extracellular microRNAs (miRNAs) are under investigation as minimally-invasive biomarkers for a wide range of disease conditions. We have recently shown in a mouse model of the progressive muscle-wasting condition Duchenne muscular dystrophy (DMD) that a set of highly elevated serum miRNAs reflects the regenerative status of muscle. These miRNAs are promising biomarkers for monitoring DMD disease progression and the response to experimental therapies. The gold standard miRNA detection methodology is Reverse Transcriptase-quantitative Polymerase Chain Reaction (RT-qPCR), which typically exhibits high sensitivity and wide dynamic range. Accurate determination of miRNA levels is affected by RT-qPCR normalization method and therefore selection of the optimal strategy is of critical importance. Serum miRNA abundance was measured by RT-qPCR array in 14 week old mice, and by individual RT-qPCR assays in a time course experiment spanning 48 weeks. Here we utilize these two datasets to assess the validity of three miRNA normalization strategies (a) normalization to the average of all Cq values from array experiments, (b) normalization to a stably expressed endogenous reference miRNA, and (c) normalization to an external spike-in synthetic oligonucleotide. Normalization approaches based on endogenous control miRNAs result in an under-estimation of miRNA levels by a factor of ∼2. An increase in total RNA and total miRNA was observed in dystrophic serum which may account for this systematic bias. We conclude that the optimal strategy for this model system is to normalize to a synthetic spike-in control oligonucleotide.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Analysis of Variance
  • Animals
  • Biomarkers / blood*
  • Caenorhabditis elegans
  • Calibration
  • Mice
  • MicroRNAs / blood*
  • Research Design*
  • Reverse Transcriptase Polymerase Chain Reaction / methods*


  • Biomarkers
  • MicroRNAs