Implementation of an Automated Diagnostic WES and WGS Workflow for Multiple Sample Types Using Covaris on the Hamilton Microlab® STAR Liquid Handling platform

The Genome Diagnostics lab of the Human Genetics Dept. at Radboud UMC provides rapid whole exome sequencing since 2017, with a max turnaround time of 15 days (from sample to report). With the continuously increasing number of requests, the manual workflow was no longer sufficient. Automated workflows are needed more than ever to meet the increasing demand in sample throughput and sequencing capacity, whilst minimizing the risk of human errors. A major goal was to enlarge the sample volume in the clinical rapid WES service while retaining the turnaround time. We implemented an automated WES workflow based on Twist Bioscience protocol on a Hamilton Microlab® STAR Liquid Handling platform. This workflow has enabled high-quality exome data with less hands-on time and a decrease in human error potential with end-to-end sample tracking. However, being a core facility, our lab receives patient DNA samples from a large number of national and international laboratories, often varying in concentration, volume, and buffer composition. We noticed that these variables have a significant impact on enzymatic fragmentation showing more vulnerability to failures. To eliminate these concerns about sample quality, we integrated mechanical shearing using the Covaris R230 Focused-ultrasonicator on-deck, enabling standardized and robust shearing, regardless of sample input.

 

Using this automated library prep workflow, we have been able to expand our clinical rapid WES service, through increased sample throughput for multiple sample types, such as blood, saliva, amniotic fluid, chorionic villus, cell line and FFPE tissue, whilst maintaining the turnaround time to patient report.

 

Based on the knowledge of the WES workflow, we started a project to sequence whole genomes in a cost-effective and automated workflow. Based on the insights we gained of the WES workflow, we recently developed a new workflow for WGS to meet the increasing demand in sample throughput and sample types.

 

Learning Objectives