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Normalization with Corresponding Naïve Tissue Minimizes Bias Caused by Commercial Reverse Transcription Kits on Quantitative Real-Time PCR Results
Real-time reverse transcription polymerase chain reaction (PCR) is the gold standard for expression analysis. Designed to improve reproducibility and sensitivity, commercial kits are commonly used for the critical step of cDNA synthesis. The present study was designed to determine the impact of thes...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5127578/ https://www.ncbi.nlm.nih.gov/pubmed/27898720 http://dx.doi.org/10.1371/journal.pone.0167209 |
Sumario: | Real-time reverse transcription polymerase chain reaction (PCR) is the gold standard for expression analysis. Designed to improve reproducibility and sensitivity, commercial kits are commonly used for the critical step of cDNA synthesis. The present study was designed to determine the impact of these kits. mRNA from mouse brains were pooled to create serial dilutions ranging from 0.0625 μg to 2 μg, which were transcribed into cDNA using four different commercial reverse-transcription kits. Next, we transcribed mRNA from brain tissue after acute brain injury and naïve mice into cDNA for qPCR. Depending on tested genes, some kits failed to show linear results in dilution series and revealed strong variations in cDNA yield. Absolute expression data in naïve and trauma settings varied substantially between these kits. Normalization with a housekeeping gene failed to reduce kit-dependent variations, whereas normalization eliminated differences when naïve samples from the same region were used. The study shows strong evidence that choice of commercial cDNA synthesis kit has a major impact on PCR results and, consequently, on comparability between studies. Additionally, it provides a solution to overcome this limitation by normalization with data from naïve samples. This simple step helps to compare mRNA expression data between different studies and groups. |
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