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MicroRNA profiling in plasma samples using qPCR arrays: Recommendations for correct analysis and interpretation
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MicroRNA profiling in plasma samples using qPCR arrays: Recommendations for correct analysis and interpretation
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Journal Article
MicroRNA profiling in plasma samples using qPCR arrays: Recommendations for correct analysis and interpretation
2018
Overview
MicroRNA (miRNA) regulate gene expression through posttranscriptional mRNA degradation or suppression of translation. Many (pre)analytical issues remain to be resolved for miRNA screening with TaqMan Low Density Arrays (TLDA) in plasma samples, such as optimal RNA isolation, preamplification and data normalization. We optimized the TLDA protocol using three RNA isolation protocols and preamplification dilutions. By using 100μL elution volume during RNA isolation and adding a preamplification step without dilution, 49% of wells were amplified. Informative target miRNA were defined as having quantification cycle values ≤35 in at least 20% of samples and low technical variability (CV across 2 duplicates of 1 sample <4%). A total of 218 miRNA was considered informative (= 59% of all target miRNA). Different normalization strategies were compared: exogenous Ath-miR-159a, endogenous RNA U6, and three mathematical normalization techniques: geNorm (Qbase, QB) and NormFinder (NF) normalization algorithms, and global mean calculation. To select the best normalization method, technical variability, biological variability, stability, and the extent to which the normalization method reduces data dispersion were calculated. The geNorm normalization algorithm reduced data dispersion to the greatest extent, while endogenous RNA U6 performed worst. In conclusion, for miRNA profiling in plasma samples using TLDA cards we recommend: 1. Implementing a preamplification step in the TLDA protocol without diluting the final preamplification product 2. A stepwise approach to exclude non-informative miRNA based on quality control parameters 3. Against using snoRNA U6 as normalization method for relative quantification 4. Using the geNorm algorithm as normalization method for relative quantification.
