Automation and miniaturization of high-throughput qPCR for gene expression profiling.

Abstract

Quantitative PCR (qPCR) is a technique commonly employed in laboratories and core facilities. In our previous study, we had shown the possibility to automate steps in a panel-specific gene expression workflow by pairing Mosquito HV with BioMark HD. Here we aimed to automate the full workflow and explore miniaturization capabilities. Each step of the gene expression workflow was scripted on Mosquito HV genomics software. We performed three different automated runs: i. Replicates of a Reference RNA sample (obtained by pooling RNA isolated from 10 healthy individuals) were run on an immunology gene expression panel. We tested the full reaction (FR) and three miniaturization conditions, namely: 1.5x, 2.5x and 5x; the data obtained from the automated FR replicates was compared to the data obtained from the manual processing; ii. Biological RNA samples (isolated from n=45 individuals) were run as FR and 1.5x on the immunology gene expression panel; iii. Biological RNA samples (isolated from n=45 individuals) were run as FR and 1.5x on a pregnancy gene expression panel. The expression of each gene was calculated using the 2^{(-delta delta Ct)} method. Successful amplification was observed for the reference samples when using FR and 1.5x conditions. The 2.5x condition exhibited suboptimal amplification with a lower success rate while the 5x condition retrieved no amplification. The 2.5x and 5x miniaturization conditions were excluded from further runs. A strong significant positive correlation was observed between the manual and automated workflows for the reference RNA sample, underscoring the robustness of the gene expression assay. The automation of the immunology and pregnancy gene expression panels on the 45 individual samples retrieved a success rate >70% for both the FR and the 1.5x miniaturization conditions. A significant positive correlation was also observed between the FR and 1.5x miniaturization conditions for both panels. Our results show that the adoption and the 1.5x miniaturization capabilities of Mosquito HV system for automating the gene expression workflow did not interfere with data quality and reproducibility.