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As well as shedding SARS-CoV-2 virus through nose and throat secretions, infected people can, even when asymptomatic, excrete the virus in their stool. Because of this, wastewater sampling has been under investigation since the beginning of the pandemic as a way to monitor levels of the novel coronavirus in whole populations.
Accumulating evidence suggests that such analyses can detect spikes in case numbers earlier than diagnostic testing can, and may therefore lead to swifter implementation of public health measures. However, methods for detecting SARS-CoV-2 in wastewater are slow and laborious, says microbiologist Smruthi Karthikeyan, a postdoc in the laboratory of computational microbiologist and engineer Rob Knight at the University of California, San Diego. Karthikeyan had been performing such analyses on a small scale, using traditional filtration to concentrate the viral RNA from wastewater samples. But when her university announced, early in the pandemic, that it would like to start such surveillance on a campus-wide scale, she realized it wouldn’t be possible to increase output without employing many more people. Instead, her team developed a faster, cheaper automated alternative.
To samples of raw sewage, the team adds magnetic particles designed to bind to SARS-CoV-2 and other viruses. Then, a liquid handling robot fitted with a magnetic head concentrates and extracts the viral RNA. These RNA samples are then transferred to another robot that mixes aliquots of the RNA with PCR reagents and primers for known SARS-CoV-2 variants before the mixtures undergo amplification cycles to allow detection of the virus. While the magnetic beads and robots were not developed by the team, their use in the newly designed and optimized process enabled the researchers to speed throughput dramatically.
“With their process they can do a hundred [samples] in a day and without a lot of human effort,” explains microbiologist Katrine Whiteson of the University of California, Irvine, who tests wastewater for SARS-CoV-2 but was not involved in the technique’s development. Before this, the number of samples it was possible to process by filtration—a method she also uses—was “ten a day tops,” she says, so this automated system is an “enormous contribution” to the field.
Knight’s team used the robots to monitor the wastewater outflow of several University of California, San Diego, campus residential buildings and found it was sensitive enough to detect a single asymptomatic individual in a building of more than 400 occupants (confirmed by diagnostic testing). The researchers also analyzed samples collected every day for three months from the influent stream of San Diego’s municipal sewage treatment facility, which serves 2.3 million city residents. From this analysis, they were able to show that the level of virus detected could predict increases and decreases in diagnosed case numbers in the city a week in advance.
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With the method’s predictive power, high throughput, and low cost, “it would be wonderful if this [wastewater sampling] was a normal part of our public health monitoring,” says Whiteson.
S. Karthikeyan et al., “High-throughput wastewater SARS-CoV-2 detection enables forecasting of community infection dynamics in San Diego County,” mSystems, 6:e00045-21, 2021
