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Title: Design and sampling plan optimization for RT-qPCR experiments in plants: a case study in blueberry

item DIE, JOSE - US Department Of Agriculture (USDA)
item FLORES, FERNANDO - Universidad De Huelva
item Rowland, Lisa

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/21/2016
Publication Date: 3/7/2016
Citation: Die, J.V., Flores, F., Rowland, L.J. 2016. Design and sampling plan optimization for RT-qPCR experiments in plants: a case study in blueberry. Frontiers in Plant Science. 7:271.

Interpretive Summary: A technique called real-time quantitative PCR or q-PCR is often used by scientists in the laboratory for measuring gene expression or how strongly genes are turned on. Unfortunately, because of the many steps involved that need careful attention, this technique is prone to error and results can be highly variable from experiment to experiment and laboratory to laboratory. In a case study using different plant tissues (leaves, stems, and fruit) from different blueberry varieties, we determined which steps of the procedure are the most variable and, therefore, would benefit the most from more repetitions. This information will be valuable to other plant scientists in designing better q-PCR experiments for measuring gene expression.

Technical Abstract: The qPCR assay has become a routine technology in plant biotechnology and agricultural research. It is unlikely to be technically improved in the near future, but there are still challenges which center around minimizing the variability in results and transparency when reporting technical data in support of the conclusions of a study. There are a number of aspects of the pre- and post-assay workflow that contribute to variability of results. Here, through the study of the introduction of error in qPCR measurements at different stages of the workflow, we describe the most important causes of technical variability in a case study using blueberry. In this study, we found that the stage for which increasing the number of replicates would be the most beneficial depends on the tissue used. For example, we would recommend the use of more RT replicates when working with leaf tissue, while the use of more sampling (RNA extraction) replicates would be recommended when working with stems or fruits to obtain the most optimal results. The use of more qPCR replicates provides the least benefit as it is the most reproducible step. By knowing the distribution of error over an entire experiment and the costs at each step, we have developed a script to identify the optimal sampling plan within the limits of a given budget. These findings should help plant scientists improve the design of qPCR experiments and refine their laboratory practices in order to conduct qPCR assays in a more reliable-manner to produce more consistent and reproducible data.