Date: 15th September 2020
There has been a rapid, global rise in the demand for COVID-19 diagnostic testing, creating large bottlenecks in many countries, and leaving many people struggling to get a diagnosis. Now researchers at Imperial College London, have described a repurposing of existing high-throughput robotic platforms as a way to help diagnostic labs, such as the UK’s National Health Service (NHS), to increase testing capacity, and cutting reliance of high demand reagents which can be in limited supply.
Automated workflows are advantageous over manual ones, as they are able to achieve diagnostic precision, produce rapid results, exclude human error and require less labour which liberates valuable resources. However, one issue with diagnostic laboratory workflows can be an over-reliance on a small number of manufacturers for infrastructure and reagents.
Now scientists from Imperial College London, UK, present in the Nature Communications journal, a reagent-agnostic automated SARS-CoV-2 testing platform that can be quickly deployed and is easily scalable. Using an in-house-generated, open-source, virus-like particle (VLP) SARS-CoV-2 standard, they validate RNA extraction and RT-qPCR workflows as well as two detection assays based on CRISPR-Cas13a and RT-loop-mediated isothermal amplification (RT-LAMP).
Imperial College, as is the case for many research institutions around the world, have well established non-commercial biofoundries. These offer integrated infrastructure including automated high-throughput equipment to enable the design-build-test cycle for large-scale experimental designs in synthetic biology. The platforms, based at the London Biofoundry, were the starting point for Paul Freemont and his team, who wanted to help ease the burden of the NHS, and adapt the current robotic platforms to test for COVID-19.
To adapt the existing platforms the team started by creating and characterising a new testing standard which could be used in a Biosafety Level 1 laboratory and did not require specialist equipment. They engineered synthetic virus-like particles (VLPs) to contain the genomic RNA segment encoding one of viral proteins, the nucleocapsid (N) protein.
With the standard in place, the team wanted to increase patient sample turn around, and in order to achieve this automation seemed to be the obvious solution. This meant they had to create new software and hardware, and the resulting automated platform could average a sample processing rate of ~1000 samples per platform per day, however this could be easily modified and scaled to 4000 samples per day.
A detection assay also had to be designed, here they built on existing antigen platforms already in clinical use, and introduced two new detection methods for SARS-CoV-2, as an alternative to the standard RT-qPCR detection method. The first was based on the specific high-sensitivity enzymatic reporter unlocking (SHERLOCK) method – using CRISPR-Cas13a to detect the viral RNA. The second was a colourimetric RT-LAMP method whereby a pH change on detection of virus RNA was visually analysed using dyes and by measuring absorbance.
To scrutinise and validate the system the team used the platform on 173 patient samples obtained from North West London Pathology (NWLP) and compared the results to those obtained by NWLP. Of the 173 samples tested, the team matched 49 positive and 120 negative samples, only four samples showed lack of concordance but all of those were close to the limit of detection.
Finally, to highlight the flexibility of the system, the team processed patient samples using a range of commercial available reagent kits. They observed high correlation of the data showing that platform could function successfully with a diverse kits and supply chains.
Conclusion and future applications:
The team here have developed and validated a robust, scalable, automated diagnostic testing platform for COVID-19, and it is hoped this platform will alleviate the current bottlenecks in the diagnostic process. Indeed, the technology is now in use in NHS pathology labs for front line testing, where the system helped the diagnostic team to test 5,000 samples in just five days after a local COVID-19 outbreak.
However, it is hoped that this automated workflow will serve as a blueprint for others to implement across the world. It is particularly suited to low- and middle-income countries, and will support communities to repurpose similar systems using and adapting existing equipment.
The platform adds strength to our increasing use of digital and automated platforms for the fight against COVID-19. Furthermore, it supports the use of CRISPR as a powerful diagnostic reagent for detecting SARS-CoV-2, offering us crucial, rapid and alternative solutions for when traditional supply chains may struggle to meet demand.
For more information please see the press release from Imperial College London
Crone, M. A., M. Priestman, M. Ciechonska, K. Jensen, D. J. Sharp, A. Anand, P. Randell, M. Storch and P. S. Freemont (2020). “A role for Biofoundries in rapid development and validation of automated SARS-CoV-2 clinical diagnostics.” Nature Communications 11(1): 4464.