Smruthi Karthikeyan, University of California San Diego and Rob Knight, University of California San Diego
The Research Brief is a brief presentation of interesting academic work.
The big idea
With a wastewater treatment robot, our laboratory was able to detect coronaviruses in wastewater 30 times faster than large, non-automated systems. This advancement, published in the microbiology journal mSystems, gives communities that monitor their wastewater even more lead time to provide early warning of local cases of COVID-19.
When clinical studies surfaced showing that people who tested positive for SARS-CoV-2 shed the virus in their stools, the sewer seemed an obvious place to look for it. Wastewater monitoring can be used at the community level to identify potential outbreak clusters prior to clinical diagnosis, especially in areas where COVID-19 prevalence rates far exceed test rates.
The problem is that the virus is very diluted in the waste stream because so many bathrooms end up in the trash, not to mention all of the other junk they flush out. Monitoring depends on the concentration of virus particles in the wastewater to determine these low levels. This virus concentration step is usually the biggest bottleneck in wastewater analysis because it is tedious and time-consuming. Our robotic system takes a different, faster approach.
San Diego County, CC BY-ND
Why it matters
Cities, schools, and businesses across the country are using wastewater monitoring to find the coronavirus in their midst.
Wastewater monitoring is particularly useful as an early warning system for high risk areas, e.g. B. in communities where undocumented residents can be careful with individual tests.
The most common virus concentration technique uses filters and can take anywhere from six to eight hours to convert a few dozen wastewater samples into samples that can then be tested for the presence of SARS-CoV-2. Our new protocol concentrates 24 samples in a single 40 minute run.
We have repurposed equipment that would normally perform microbiological or cell biological tasks in the laboratory to deal with wastewater instead. By miniaturizing and automating our system, we are eliminating a number of labor-intensive steps, resources, and associated costs. And our hands-free process is much faster.
CH. Sheikhzadeh, CC BY-ND
How we do this work
We collect wastewater from autosamplers at the main San Diego sewage treatment plant, as well as those we have deployed in over 100 manholes on the University of California San Diego campus that collect wastewater samples every 30 minutes throughout the day.
Back in the laboratory, instead of several filtering steps, we use tiny magnetic beads to concentrate the virus particles. We buy these nanomagnetic beads that are said to bind to a wide variety of respiratory viruses. The wastewater treatment robot is equipped with a special magnetic head that holds the magnetic beads with viruses. It preferentially fishes out virus particles and leaves the rest of the garbage in the wastewater sample.
By using a robot to automate the wastewater concentration process, we can concentrate 24 samples in 40 minutes for each robot. Then the same robot can extract the viral RNA and process 96 samples in 36 minutes. Finally, we're using a polymerase chain reaction to look for the signature genes of SARS-CoV-2, much like a clinical diagnostic test a laboratory would do on a patient's nasal swab.
In total, our system can process 96 samples in 4.5 hours, which significantly shortens the time from sample to result.
So far, our study is the only coronavirus wastewater study that we know uses an automated process.
We use this technique as part of our large-scale wastewater monitoring on campus and sample over 100 locations daily. The school districts of San Diego also use it as an early warning system.
We are now using the viral genome sequencing part of our system to track the emergence of new SARS-CoV-2 variants.
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Smruthi Karthikeyan, Postdoctoral Fellow in Pediatrics, University of California, San Diego; and Rob Knight, Professor of Pediatrics and Computer Science and Engineering, University of California, San Diego
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