High-throughput and automated screening for COVID-19

Abstract

The COVID-19 pandemic has become a global challenge for the healthcare systems of many countries with 6 million people having lost their lives and 530 million more having tested positive for the virus. Robust testing and a comprehensive track and trace process for positive patients are essential for effective pandemic control, leading to high demand for diagnostic testing. In order to comply with demand and increase testing capacity worldwide, automated workflows have come into prominence as they enable high-throughput screening, faster processing, exclusion of human error, repeatability, reproducibility and diagnostic precision. The gold standard for COVID-19 testing so far has been RT-qPCR, however, different SARS-CoV-2 testing methods have been developed to be combined with high throughput testing to improve diagnosis. Case studies in China, Spain and the United Kingdom have been reviewed and automation has been proven to be promising for mass testing. Free and Open Source scientific and medical Hardware (FOSH) plays a vital role in this matter but there are some challenges to be overcome before automation can be fully implemented. This review discusses the importance of automated high-throughput testing, the different equipment available, the bottlenecks of its implementation and key selected case studies that due to their high effectiveness are already in use in hospitals and research centres.

Keywords: COVID-19; SARS-coV-2; automation; diagnostic; high-throughput.

Figure 1

(created with BioRender.com). Workflow comparing different diagnostic methodologies for COVID-19. Images describe a general overview of how each method works, starting from the sample taking and then to viral RNA extraction. From this point, the next step is either direct amplification or retro transcription into DNA for further detection using different methods, potentially RT-PCR or CRISPR-based analysis, followed by the visualisation step.

Figure 2

(created with BioRender.com). RT-qPCR test workflow comparing both manual and automated nucleic-acid extraction in Huo-Yan Lab. Automation platforms increase testing capacity whilst simultaneously decreasing processing time. Modified from Liu et al. (22).

Figure 3

(created with BioRender.com). Schematic illustration of three different automated diagnostic workflows from patient samples and their corresponding required equipment (A) RT-PCR diagnostic workflow (B) LAMP diagnostic workflow (C) CRISPR-Cas13a nucleic acid detection workflow Equipment used for each workflow is also shown in the figure. A qPCR device is a necessity for RT-qPCR work while the plate reader is used for CRISPR-Cas and LAMP workflows to detect absorbance change. Figures modified and adapted from Crone et al. (2).

Figure 4

(created with BioRender.com). Different protocols using the open-source OT-2 automated platform for COVID-19 testing (A) automated workflow Designed at CBD. An initial run preparation was performed using open-source Python coding. Initial sample setup, sample preparation, and plate filling and qPCR mix preparation were performed by OT-2 robots. RNA extraction was processed by KingFisher Flex and RT- qPCR was run by ABI 7500. Analysis results were exported as a user-friendly R file. Figure adapted from Villanueva-Cañas et al. (33) (B) the layout and workflow of a semi-automated CONTAIN lab. Three separate sections were used for plating samples, RNA extraction and RT-qPCR respectively. OT-2 robots were used for the RNA extraction process and qPCR mix preparation. Station C which is used for the RT-qPCR run also contained two subsections to separate OT-2 and qPCR devices. Image taken from OpenCell.bio, Walker et al. (35).