A LAMP sequencing approach for high-throughput co-detection of SARS-CoV-2 and influenza virus in human saliva

  • Robert Warneford-Thomson
  • , Parisha P. Shah
  • , Patrick Lundgren
  • , Jonathan Lerner
  • , Jason Morgan
  • , Antonio Davila
  • , Benjamin S. Abella
  • , Kenneth Zaret
  • , Jonathan Schug
  • , Rajan Jain
  • , Christoph A. Thaiss
  • , Roberto Bonasio

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The COVID-19 pandemic has created an urgent need for rapid, effective, and low-cost SARS-CoV-2 diagnostic testing. Here, we describe COV-ID, an approach that combines RT-LAMP with deep sequencing to detect SARS-CoV-2 in unprocessed human saliva with a low limit of detection (5–10 virions). Based on a multi-dimensional barcoding strategy, COV-ID can be used to test thousands of samples overnight in a single sequencing run with limited labor and laboratory equip-ment. The sequencing-based readout allows COV-ID to detect multiple amplicons simultaneously, including key controls such as host transcripts and artificial spike-ins, as well as multiple pathogens. Here, we demonstrate this flexibility by simultaneous detection of 4 amplicons in contrived saliva samples: SARS-CoV-2, influenza A, human STATHERIN, and an artificial SARS calibration standard. The approach was validated on clinical saliva samples, where it showed excellent agreement with RT-qPCR. COV-ID can also be performed directly on saliva absorbed on filter paper, simplifying collection logistics and sample handling.

Original languageEnglish
Article numbere69949
JournaleLife
Volume11
DOIs
StatePublished - 2022
Externally publishedYes

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