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Highly multiplexed oligonucleotide probe-ligation testing enables efficient extraction-free SARS-CoV-2 detection and viral genotyping

Cited 9 time in Web of Science Cited 9 time in Scopus
Authors

Credle, Joel J.; Robinson, Matthew L.; Gunn, Jonathan; Monaco, Daniel; Sie, Brandon; Tchir, Alexandra; Hardick, Justin; Zheng, Xuwen; Shaw-Saliba, Kathryn; Rothman, Richard E.; Eshleman, Susan H.; Pekosz, Andrew; Hansen, Kasper; Mostafa, Heba; Steinegger, Martin; Larman, H. Benjamin

Issue Date
2021-06
Publisher
Nature Publishing Group
Citation
Modern Pathology, Vol.34 No.6, pp.1093-1103
Abstract
There is an urgent and unprecedented need for sensitive and high-throughput molecular diagnostic tests to combat the SARS-CoV-2 pandemic. Here we present a generalized version of the RNA-mediated oligonucleotide Annealing Selection and Ligation with next generation DNA sequencing (RASL-seq) assay, called "capture RASL-seq" (cRASL-seq), which enables highly sensitive (down to similar to 1-100 pfu/ml or cfu/ml) and highly multiplexed (up to similar to 10,000 target sequences) detection of pathogens. Importantly, cRASL-seq analysis of COVID-19 patient nasopharyngeal (NP) swab specimens does not involve nucleic acid purification or reverse transcription, steps that have introduced supply bottlenecks into standard assay workflows. Our simplified protocol additionally enables the direct and efficient genotyping of selected, informative SARS-CoV-2 polymorphisms across the entire genome, which can be used for enhanced characterization of transmission chains at population scale and detection of viral clades with higher or lower virulence. Given its extremely low per-sample cost, simple and automatable protocol and analytics, probe panel modularity, and massive scalability, we propose that cRASL-seq testing is a powerful new technology with the potential to help mitigate the current pandemic and prevent similar public health crises.
ISSN
0893-3952
URI
https://hdl.handle.net/10371/202560
DOI
https://doi.org/10.1038/s41379-020-00730-5
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  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data

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