Creating better tests for viruses

UC College of Arts and Sciences Assistant Professor Pietro Strobbia is developing new at-home diagnostics for infectious diseases.

“During COVID we found some issues with home diagnostics infrastructure. Home tests aren’t sensitive enough to accurately detect many infectious diseases,” he said.

Biosensors are devices used in the rapid detection of substances at the molecular level.

During the pandemic, many people became familiar with diagnostics such as polymerase chain reaction, or PCR, tests, which are more accurate than antigen tests available at home. But no test offers both accuracy and deployability.

“We learned a lot from COVID. But we want to be ready for the next COVID, the next dangerous virus,” he said.

To that end, Strobbia and his research partners, UC Professors George Stan and Ruxandra Dima, received a $1.4 million grant from the National Institutes of Health to develop new surface-enhanced Raman scattering biosensors using artificial intelligence.

“What we want to do is build a library of biosensors detecting many targets. Then we can use AI to read the library in relation to the biosensors' sequences and understand which sequences will work best for certain targets,” Strobbia said. “Then we can ask our AI to design a powerful sensor for us to detect an emerging virus.” 

The long-term goal of this project is to create a sensing platform capable of detecting multiple genetic biomarkers in an at-home test. Surface-enhanced Raman scattering is already used in lots of applications to detect contaminants in food or in environmental analyses.

Unlike at-home tests today, the sensors not only can detect the presence of virus but the amount or viral load.

As part of the project, Strobbia’s lab invested in a new robot that can test the project’s many hundreds of sensors, each of which contains a precise mix of chemicals. This will help researchers create the vast library needed by the AI.

UC doctoral student Steven Quarin said using pipettes to prepare and test the sensors by hand is tedious and time-consuming.

“I know how long it takes me to prepare a single row in a 96-well plate,” he said. “When you’re pipetting, it gets very repetitive. It’s monotonous work. I’m confident I’m not making mistakes, but there are times when I ask, ‘Did I do this right?’”

Strobbia said the robot is reliably accurate and can prepare sensors with less risk of contamination.

Students can spend time required to perform these monotonous tasks in thinking or working on less mechanical tasks. An additional benefit is that students in the lab will learn how to interact with robots and program automation into their workflows, he said.

“Robots and automation are the likely future of most chemistry labs. The students that learn this could be the future leaders of this revolution. I believe at UC we should prepare such leaders,” Strobbia said.

Strobbia’s research partners, computational chemists Dima and Stan, said they were excited to join the project to develop efficient and cost-effective biosensors.

Stan said biosensor development is hindered by our limited understanding of the optimal biophysical and biochemical properties required for functional DNA sequences.

“Our collaboration aims to develop an AI-based approach that autonomously generates functional DNA sequences optimized for the viral target,” he said. “This machine learning approach uses a large library of DNA sequences to learn the ‘language’ based on the DNA alphabet of four nucleotides.”

Stan said since artificial intelligence gets more accurate when it has access to larger data sets, a big part of the project will be to provide a large library of functional DNA sequences.

“In turn, this large DNA library will be used to train the AI-based model,” Stan said. “Thus, the availability of this automated pipeline in the Department of Chemistry opens up exciting avenues for biosensor design that combine state-of-the-art experimental and AI-based tools.”

Featured image at top: UC chemistry students are helping to develop new biosensors for eventual use in at-home disease testing. Photo/Andrew Higley/UC Marketing + Brand