With an eye on COVID-19 variants, U of T, Sinai Health researchers design next-gen sequencing platform

An automated robotics platform designed by U of T and Sinai Health researchers is capable of analyzing thousands of COVID-19 patient samples in a single run with a high accuracy rate (photo courtesy of Sinai Health)

A robotics platform designed by Sinai Health and University of Toronto researchers to screen COVID-19 samples could revolutionize how labs track the spread of viruses and other pathogens.

The next-generation, ultra-high-throughput sequencing system, called C19-SPAR-Seq, has a sensitivity rate greater than 95 per cent during peak virus onset and transmissibility, and over 90 per cent in samples with a low viral load.

The researchers’ approach was detailed in a study published this week in the journal Nature Communications.

“Identifying positive samples quickly and accurately is critical in beating this pandemic,” said Jeff Wrana, a professor of molecular genetics in U of T’s Temerty Faculty of Medicine and senior investigator at Sinai Health’s Lunenfeld-Tanenbaum Research Institute (LTRI).

“With new and potentially dangerous variants now circulating, this platform is scalable, automated and capable of analyzing thousands of COVID-19 patient samples in a single instrument run.”

Wrana and colleagues are now using the system to screen all positive samples identified in the shared clinical diagnostics lab at Sinai Health and University Health Network. The goal is to identify known and novel variants that emerge in the population.

“By systematically screening all positives using C19-SPAR-Seq, we can provide rapid feedback on the frequency of known variants in the population and quantitate their expansion – and most importantly, enable early discovery of emergent variants,” Wrana said, adding that, as SARS-CoV-2 continues to evolve, it will be critical to catalogue variants that might evade immune responses in previously infected or vaccinated people.

A team of trainees shifted from other areas of research to help develop and validate the platform, allowing the group to go from concept to published paper in under 12 months.

“The system is extremely reliable and readily adaptable,” said Javier Hernandez, a doctoral student in molecular genetics at U of T who was a co-lead author on the study with Marie-Ming Aynaud and Seda Barutcu.

“The turnaround is approximately 24 hours. It’s very simple as we’ve automated practically every step in the process,” Hernandez said. “It’s been very exciting to see my work make a difference.”

Laurence Pelletier is a professor of molecular genetics at U of T and a senior investigator at the Lunenfeld-Tanenbaum Research Institute who was co-corresponding author on the paper with Wrana. He said the shared clinical diagnostic lab provided access to thousands of samples and was key to the study’s success.

The diagnostic lab is led by Tony Mazzulli, a professor of both medicine and laboratory medicine and pathobiology at U of T, who is microbiologist-in-chief for Sinai Health and University Health Network.

“It has been an absolute pleasure to work with Jeff and his team at the LTRI,” said Mazzulli. “His novel SPAR-Seq system is cutting-edge technology and his team’s ability to sequence COVID-19 samples in real time has tremendous potential for impacting our understanding of the epidemiology and spread of novel mutants in the province.”

The platform is also cost-effective. The study notes it only costs about US$8 per test when running thousands of samples at once, as the cost per sample decreases due to economies of scale.

This study was supported by the university’s Toronto COVID-19 Action Fund and a donation from the Krembil Foundation.