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Artifact-Free Quantification and Sequencing of Rare Recombinant Viruses by Using Drop-Based Microfluidics

  • Ye Tao
  • , Assaf Rotem
  • , Huidan Zhang
  • , Shelley K. Cockrell
  • , Stephan A. Koehler
  • , Connie B. Chang
  • , Lloyd W. Ung
  • , Paul G. Cantalupo
  • , Yukun Ren
  • , Jeffrey S. Lin
  • , Andrew B. Feldman
  • , Christiane E. Wobus
  • , James M. Pipas
  • , David A. Weitz*
  • *Corresponding author for this work
  • Harvard University
  • School of Mechatronics Engineering, Harbin Institute of Technology
  • China Medical University
  • University of Pittsburgh
  • Montana State University
  • Johns Hopkins University Applied Physics Laboratory
  • Johns Hopkins University
  • University of Michigan, Ann Arbor

Research output: Contribution to journalArticlepeer-review

Abstract

Recombination is an important driver in the evolution of viruses and thus is key to understanding viral epidemics and improving strategies to prevent future outbreaks. Characterization of rare recombinant subpopulations remains technically challenging because of artifacts such as artificial recombinants, known as chimeras, and amplification bias. To overcome this, we have developed a high-throughput microfluidic technique with a second verification step in order to amplify and sequence single recombinant viruses with high fidelity in picoliter drops. We obtained the first artifact-free estimate of in vitro recombination rate between murine norovirus strains MNV-1 and WU20 co-infecting a cell (Prec=3.3×10-4±2×10-5) for a 1205nt region. Our approach represents a time- and cost-effective improvement over current methods, and can be adapted for genomic studies requiring artifact- and bias-free selective amplification, such as microbial pathogens, or rare cancer cells.

Original languageEnglish
Pages (from-to)2167-2171
Number of pages5
JournalChemBioChem
Volume16
Issue number15
DOIs
StatePublished - 1 Oct 2015
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • RT-PCR
  • drop-based microfluidics
  • error-free genomic amplification
  • sequence determination
  • viruses

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