Abstract
qRT-PCR is a generally acknowledged method for gene expression analysis due to its precision and reproducibility. However, it is well known that the accuracy of qRT-PCR data varies greatly depending on the experimental design and data analysis. Recently, a set of guidelines has been proposed that aims to improve the reliability of qRT-PCR. However, there are additional factors that have not been taken into consideration in these guidelines that can seriously affect the data obtained using this method. In this study, we report the influence that object morphology can have on qRT-PCR data. We have used a number of Arabidopsis thaliana mutants with altered floral morphology as models for this study. These mutants have been well characterised (including in terms of gene expression levels and patterns) by other techniques. This allows us to compare the results from the qRT-PCR with the results inferred from other methods. We demonstrate that the comparison of gene expression levels in objects that differ greatly in their morphology can lead to erroneous results.
Highlights
Over the past twenty years real-time qRT-PCR has become a powerful approach for the accurate quantification of gene expression
Incorrect normalisation may lead to serious inaccuracy in data analysis. It is well-known that a normalisation strategy that relies on the use of reference genes is preferable for real-time qRT-PCR experiments [e.g. 4, 5]
Gene expression levels obtained from the analysis of ag-1 indicated that WUS expression was reduced by two orders of magnitude
Summary
Over the past twenty years real-time qRT-PCR has become a powerful approach for the accurate quantification of gene expression. In order to address this problem, a set of guidelines describing the minimum information necessary for the evaluation of qRTPCR experiments was recently proposed [2] These guidelines are widely accepted in the biological science community; suffice it to say that the instructions for authors of several high-impact journals include the recommendation to follow these guidelines [e.g. 3]. Incorrect normalisation may lead to serious inaccuracy in data analysis It is well-known that a normalisation strategy that relies on the use of reference genes (the genes for which expression is stable in all samples being compared) is preferable for real-time qRT-PCR experiments [e.g. 4, 5]. In some cases the degree of inaccuracy can reach a 10-fold error [6] To avoid this problem, some approaches for validation were proposed, including geNorm, NormFinder, BestKeeper, qBase [7,8,9,10]. All of these approaches were subject to preliminary tests on human tissues, and have been applied to a wide range of other objects
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