Extraction-free clinical detection of SARS-CoV-2 virus from saline gargle samples using Hamilton STARlet liquid...

Clinical labs have struggled with high demand for SARS-CoV-2 testing during the pandemic, leading to shortages in reagents, staff, and sample collection systems. Traditional testing methods require nucleic acid extraction, a time-consuming and costly step. Prior research has shown that alternative sample types and automation can improve throughput. However, no prior work had resolved how to implement a fully automated, extraction-free system using widely available equipment. This gap motivated the development of a streamlined workflow using self-collected samples and a robotic liquid handler. The need for cost-effective and efficient testing remains unmet in many clinical settings. Standard methods still rely on manual steps that increase labor intensity. This paper introduces a novel approach to reduce both reagent use and staff workload.

The study aimed to optimize high-throughput SARS-CoV-2 PCR testing by eliminating nucleic acid extraction and automating sample handling. The specific problem addressed was the high labor and reagent demand in traditional testing protocols. The motivation was to reduce the burden on clinical staff and healthcare systems during the pandemic. The researchers proposed using self-collected saline gargle samples to minimize manual processing. They also aimed to reduce hands-on time by automating sample handling with the Hamilton STARlet. The goal was to maintain diagnostic accuracy while improving efficiency. The study focused on validating an automated workflow that could be widely adopted. The researchers sought to demonstrate that this approach could be both faster and less resource-intensive.

The study used self-collected saline gargle samples to streamline SARS-CoV-2 testing. A Hamilton STARlet liquid handler was programmed to automate sample handling and PCR setup. A custom barcode scanning script was developed to identify sample tubes and initiate processing. Pre-frozen PCR reaction mixtures were used to reduce setup time. The system eliminated the need for nucleic acid extraction, a step typically requiring manual labor. Validation was performed on 1060 samples to assess accuracy and reliability. The automated system was compared to traditional workflows in terms of hands-on time and turnaround time. The study focused on measuring false positive and false negative rates, as well as retesting frequency due to technical issues.

The automated workflow produced no false positive or false negative results in both validation and live testing. Of 1060 validation samples, 3.6% (39/1060) required retesting due to single gene positivity, internal control failure, or liquid aspiration errors. The overall turnaround time was 185 minutes compared to 200 minutes in traditional workflows. However, hands-on time was reduced by 76%, potentially decreasing staff fatigue and burnout. The system used pre-frozen PCR mixtures to speed up assay setup. Sample handling was fully automated using the Hamilton STARlet and a custom barcode script. The elimination of nucleic acid extraction reduced reagent costs and labor. The study demonstrated that automated workflows can maintain diagnostic accuracy while improving efficiency.

The study demonstrated that automated, extraction-free SARS-CoV-2 testing using self-collected saline gargle samples is feasible. The Hamilton STARlet system successfully reduced hands-on time by 76% without compromising diagnostic accuracy. No false positive or false negative results were observed in the validation set. The workflow maintained a 3.6% retesting rate due to technical issues, which is comparable to traditional methods. The use of pre-frozen PCR mixtures and automated sample handling reduced reagent costs and staff workload. The system's performance suggests it could be widely adopted in clinical settings. The authors propose that this approach could help alleviate staff fatigue and reduce the burden on healthcare systems. The findings support the use of automated workflows to improve high-throughput testing efficiency.