Automation of high-throughput mRNA-seq library preparation: a robust, hands-free and time efficient methodology

Over the last decade, whole transcriptome profiling, also known as RNA-sequencing (RNA-seq), has quickly gained traction as a reliable method for unbiased assessment of gene expression. Integration of RNA-seq expression data into other omics datasets (e.g., proteomics, metabolomics, or epigenetics) solidifies our understanding of cell-specific regulatory patterns, yielding pathways to investigate the key rules of gene regulation. A limitation to efficient, at-scale utilization of RNA-seq is the time-demanding library preparation workflows, which is a 2-day or longer endeavor per cohort/sample size. To tackle this bottleneck, we designed an automated workflow that increases throughput capacity, while minimizing human error to enhance reproducibility. To this end, we converted the manual protocol of the NEBNext Directional Ultra II RNA Library Prep Kit for Illumina on the Beckman Coulter liquid handler, Biomek i7 Hybrid workstation. A total of 84 RNA samples were isolated from two human cell lines and subjected to comparative manual and automated library preparation methods. Qualitative and quantitative results indicated a high degree of similarity between libraries generated manually or through automation. Yet, there was a significant reduction in both hands-on and assay time from a 2-day manual to a 9-hour automated workflow. Using linear regression analysis, we found the Pearson correlation coefficient between libraries generated manually or by automation to be almost identical to a sample being sequenced twice (R-2 = 0.985 vs 0.983). This demonstrates that high-throughput automated workflows can be of great benefit to genomic laboratories by enhancing efficiency of library preparation, reducing hands-on time and increasing throughput potential.

Automated summary

RNA-seq has become a reliable method for unbiased assessment of gene expression, and integrating it with other omics datasets enhances our understanding of cell-specific regulatory patterns. However, the time-consuming library preparation process has been a bottleneck. To address this issue, an automated workflow was designed to increase efficiency.