PropertyValue
?:abstract
  • A major bottleneck in scaling-up COVID-19 testing is the need for sophisticated instruments and well-trained healthcare professionals, which are already overwhelmed due to the pandemic. Moreover, the high-sensitive SARS-CoV-2 diagnostics are contingent on an RNA extraction step, which, in turn, is restricted by constraints in the supply chain. Here, we present CASSPIT (Cas13 Assisted Saliva-based & Smartphone Integrated Testing), which will allow direct use of saliva samples without the need for RNA extraction for SARS-CoV-2 detection. CASSPIT utilizes CRISPR-Cas13a based SARS-CoV-2 RNA detection, and lateral-flow assay (LFA) readout of the test results. The sample preparation workflow includes an optimized chemical treatment and heat inactivation method, which, when applied to 94 COVID-19 clinical samples, showed a 97% positive agreement with the RNA extraction method. With CASSPIT, LFA based visual limit of detection (LoD) for a given SARS-CoV-2 RNA spiked into the saliva samples was ~200 copies; image analysis-based quantification further improved the analytical sensitivity to ~100 copies. Upon validation of clinical sensitivity on RNA extraction-free saliva samples (n=76), a 98% agreement between the lateral-flow readout and RT-qPCR data was found. To enable user-friendly test results with provision for data storage and online consultation, we subsequently integrated lateral-flow strips with a smartphone application. We believe CASSPIT will eliminate our reliance on RT-qPCR by providing comparable sensitivity and will be a step toward establishing nucleic acid-based point-of-care (POC) testing for COVID-19.
is ?:annotates of
?:creator
?:doi
?:doi
  • 10.1101/2020.11.07.20227082
?:license
  • medrxiv
?:pdf_json_files
  • document_parses/pdf_json/ca1d463a5da15f21c06d7ba8e3adacdc0074d148.json
?:publication_isRelatedTo_Disease
is ?:relation_isRelatedTo_publication of
?:sha_id
?:source
  • MedRxiv; WHO
?:title
  • A saliva-based RNA extraction-free workflow integrated with Cas13a for SARS-CoV-2 detection
?:type
?:year
  • 2020-11-10

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