Publication

Droplet digital PCR: A novel method for detection of influenza virus defective interfering particles

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Last modified
  • 03/05/2025
Type of Material
Authors
    Samantha L. Schwartz, Emory UniversityAnice Lowen, Emory University
Language
  • English
Date
  • 2016-11
Publisher
  • Elsevier
Publication Version
Copyright Statement
  • © 2016 Elsevier B.V.
Final Published Version (URL)
Title of Journal or Parent Work
ISSN
  • 0166-0934
Volume
  • 237
Start Page
  • 159
End Page
  • 165
Grant/Funding Information
  • In addition, the Georgia Research Alliance funded the purchase of droplet digital PCR instrumentation used for the work reported herein.
  • This work was funded by NIH/NIAID through R01 AI099000 and Centers of Excellence for Influenza Research and Surveillance (CEIRS) contract no. HHSN272201400004C.
Abstract
  • Defective interfering (DI) particles are viruses that carry one or more large, internal deletions in the viral genome. These deletions occur commonly in RNA viruses due to polymerase error and yield incomplete genomes that typically lack essential coding regions. The presence of DI particles in a virus population can have a major impact on the efficiency of viral growth and is an important variable to consider in interpreting experimental results. Herein, we sought to develop a robust methodology for the quantification of DI particles within influenza A virus stocks. We took advantage of reverse transcription followed by droplet digital PCR (RT ddPCR), a highly sensitive and precise technology for determination of template concentrations without the use of a standard curve. Results were compared to those generated using standard RT qPCR. Both assays relied on the use of primers binding to terminal regions conserved in DI gene segments described to date, and internal primers targeting regions typically missing from DI particles. As it has been reported previously, we observed a lower coefficient of variation among technical replicates for ddPCR compared to qPCR. Results furthermore established RT ddPCR as a sensitive and quantitative method for detecting DI gene segments within influenza A virus stocks.
Author Notes
Keywords
Research Categories
  • Health Sciences, Immunology
  • Biology, Microbiology
  • Biology, Virology

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