Selected article for: "highly target and immune response"

Author: Caceres-Martell, Y.; Fernandez-Soto, D.; Campos-Silva, C.; Garcia-Cuesta, E. M.; Casasnovas, J. M.; Navas-Herrera, D.; Beneitez-Martinez, A.; Martinez-Fleta, P.; Alfranca, A.; Sanchez-Madrid, F.; Escudero-Lopez, G.; Vilches, C.; Jara-Acevedo, R.; Reyburn, H. T.; Rodriguez-Frade, J. M.; Vales-Gomez, M.
Title: Bead-assisted SARS-CoV-2 multi-antigen serological test allows effective identification of patients
  • Cord-id: kdwwdp6v
  • Document date: 2021_4_10
  • ID: kdwwdp6v
    Snippet: Many new aspects of COVID-19 disease, including different clinical manifestations, have been identified during the pandemic. The wide array of symptoms and variation in disease severity after SARS-CoV-2 infection might be related to heterogeneity in the immune responses of different patients. Here we describe a new method for a simple multi-antigen serological test that generates a full picture of seroconversion in a single reaction. The assay is based on the detection by flow cytometry of multi
    Document: Many new aspects of COVID-19 disease, including different clinical manifestations, have been identified during the pandemic. The wide array of symptoms and variation in disease severity after SARS-CoV-2 infection might be related to heterogeneity in the immune responses of different patients. Here we describe a new method for a simple multi-antigen serological test that generates a full picture of seroconversion in a single reaction. The assay is based on the detection by flow cytometry of multiple immunoglobulin classes (isotypes) specific for four SARS-CoV-2 antigens: the Spike glycoprotein (one of the highly immunogenic proteins), its RBD fragment (the major target for neutralising antibodies), the nucleocapsid protein and the main cysteine-like protease. Until now, most diagnostic serological tests measured antibodies to only one antigen and some patients seemed to not make any antibody response. Our data reveal that while most patients respond against all the viral antigens tested, others show a marked bias to make antibodies against either proteins exposed on the viral particle or those released after cellular infection. Combining all the four antigens and using machine learning techniques, it was possible to clearly discriminate between patients and healthy controls with 100% confidence. Further, combination of antigens and different immunoglobulin isotypes in this multi-antigen assay improved the classification of patients with mild and severe disease. Introduction of this method will facilitate massive screenings of patients to evaluate their immune response. It could also support vaccination campaigns both to select non-immune individuals and to distinguish infected patients from vaccine responders.

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