Setting a new standard for COVID-19 testing


 A new method of testing for the virus that causes COVID-19 is under development with support from WUN. Yangchao Chen and his colleagues at The Chinese University of Hong Kong are developing the technique and collaborating with partners at the University of Cape Town for clinical trials. Explained Chen: ‘our assay is much more sensitive than RT-qPCR, the current gold standard in COVID-19 testing, and also provides another advantage in that we will be able to detect multiple genes so as to reduce the false negative rate to a minimum.’ 


The current gold standard in testing for SARS-CoV-2, the virus that causes COVID-19, uses a technique known as RT-qPCR (reverse transcription quantitative polymerase chain reaction). As described in a recent review article, various testing methods entail different speeds, costs, and levels of accuracy (Xi et al 2020). The likelihood of detection is also affected by issues such as the timing of the test relative to the patient’s illness, sample types (from saliva, for instance, compared to nasal swabs), the treatment of the sample after collection, and which specific target the test is attempting to identify.

 To develop an assay that is both more sensitive and more accurate than the RT-qPCR technique, Yangchao Chen (The Chinese University of Hong Kong) and his team are applying their expertise in ribonucleic acid (RNA) viruses like SARS-Cov-2. The assay uses a technique called modified droplet digital PCR (ddPCR), an emerging method in RNA and DNA genetic analysis. While existing RT-qPCR assays can detect the virus at the level of four or five RNA copies per millilitre, Chen anticipates their new method will be able to detect its presence from just one viral copy. Moreover, as Chen explained, ‘not only is the assay itself more sensitive than RT-qPCR, but we also cover different genes.’ Most conventional assays can test for only one of the coronavirus genes, but the ddPCR assay will be able to detect multiple different genes.

The technique is currently being refined and clinical trials at the University of Cape Town and The University of Sydney are anticipated by late 2020. The more specialised facilities required for ddPCR explain why it was not initially preferred over other assays. However, the sensitivity and greater coverage of the ddPCR assay will help to reduce the risk of ‘false negatives,’ where tests have been unable to detect the presence of the virus. For instance, if the one gene that RT-qPCR assays can recognise has degraded in the sample, even though other genes are present, testing will return a false negative. This is a particular concern regarding COVID-19 due to the high proportion of asymptomatic cases.

 The rapid proliferation of SARS-CoV-2 assays has created a suite of assay options, but at present there is only limited evidence to compare performance across these options. Reliance on a single specimen type, sample timing, or diagnostic method can create a misleading result. The WUN-supported team, if successful in its goal of developing a more sensitive assay than those currently in use, will contribute to reducing oversights caused by the diagnostic method. While the assay technique is only one factor, the modified droplet digital assay could be used strategically, Chen argued, to identify potential oversights in other methods and increase confidence in the results: ‘RT-qPCR could be used as a routine assay, however, if an individual is at high risk and a suspected case, but their RT-qPCR result is negative, then ddPCR could help.’ The more specialised facilities required for ddPCR explain why it was not initially preferred over other assays. A more accurate and sensitive test will allow appropriate decision-making about patient care, risk to others such as health care professionals and close contacts, and the need for infection tracing and control.


Associate Professor Yangchao Chen is Principal Investigator of a WUN group developing testing methods for COVID-19. WUN partner institutions collaborating on this research project are University of Cape Town and The Chinese University of Hong Kong. For more information see their WUN page.