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A high content microscopy-based platform for detecting antibodies to the nucleocapsid, spike and membrane proteins of SARS-CoV-2
Preprint
en En
| PREPRINT-MEDRXIV
| ID: ppmedrxiv-21264873
ABSTRACT
The strong humoral immune response produced against the SARS-CoV-2 nucleocapsid (N) and spike (S) proteins has underpinned serological testing but the prevalence of antibody responses to other SARS-CoV-2 proteins, which may be of use as further serological markers, is still unclear. Cell-based serological screening platforms can fulfil a crucial niche in the identification of antibodies which recognise more complex folded epitopes or those incorporating post-translation modifications which may be undetectable by other methods used to investigate the antigenicity of the SARS-CoV-2 proteome. Here, we employed automated high content immunofluorescence microscopy (AHCIM) to assess the viability of such an approach as a method capable of assaying humoral immune responses against full length SARS-CoV-2 proteins in their native cellular state. We first demonstrate that AHCIM provides high sensitivity and specificity in the detection of SARS-CoV-2 N and S IgG. Assessing the prevalence of antibody responses to the SARS-CoV-2 structural membrane protein (M), we further find that 85% of COVID-19 patients within our sample set developed detectable M IgG responses (M sensitivity 85%, N sensitivity 93%, combined N + M sensitivity 95%). The identification of M as a serological marker of high prevalence may be of value in detecting additional COVID-19 cases during the era of mass SARS-CoV-2 vaccinations, where serological screening for SARS CoV-2 infections in vaccinated individuals is dependent on detection of antibodies against N. These findings highlight the advantages of using cell-based systems as serological screening platforms and raise the possibility of using M as a widespread serological marker alongside N and S.
cc_by_nc_nd
Texto completo:
1
Colección:
09-preprints
Base de datos:
PREPRINT-MEDRXIV
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
Idioma:
En
Año:
2021
Tipo del documento:
Preprint