• Scientific reports · Dec 2020

    Development of immunohistochemistry and in situ hybridisation for the detection of SARS-CoV and SARS-CoV-2 in formalin-fixed paraffin-embedded specimens.

    • Fabian Z X Lean, Mart M Lamers, Samuel P Smith, Rebecca Shipley, Debby Schipper, Nigel Temperton, Bart L Haagmans, Ashley C Banyard, Kevin R Bewley, Miles W Carroll, Sharon M Brookes, Ian Brown, and Alejandro Nuñez.
    • Pathology Department, Animal and Plant Health Agency (APHA), Woodham Lane, New Haw, Addlestone, KT15 3NB, UK. Fabian.lean@apha.gov.uk.
    • Sci Rep. 2020 Dec 14; 10 (1): 21894.

    AbstractThe rapid emergence of SARS-CoV-2, the causative agent of COVID-19, and its dissemination globally has caused an unprecedented strain on public health. Animal models are urgently being developed for SARS-CoV-2 to aid rational design of vaccines and therapeutics. Immunohistochemistry and in situ hybridisation techniques that facilitate reliable and reproducible detection of SARS-CoV and SARS-CoV-2 viral products in formalin-fixed paraffin-embedded (FFPE) specimens would be of great utility. A selection of commercial antibodies generated against SARS-CoV spike protein and nucleoprotein, double stranded RNA, and RNA probe for spike genes were evaluated for the ability to detect FFPE infected cells. We also tested both heat- and enzymatic-mediated virus antigen retrieval methods to determine the optimal virus antigen recovery as well as identifying alternative retrieval methods to enable flexibility of IHC methods. In addition to using native virus infected cells as positive control material, the evaluation of non-infected cells expressing coronavirus (SARS, MERS) spike as a biosecure alternative to assays involving live virus was undertaken. Optimized protocols were successfully applied to experimental animal-derived tissues. The diverse techniques for virus detection and control material generation demonstrated in this study can be applied to investigations of coronavirus pathogenesis and therapeutic research in animal models.

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