Analysis of SARS-CoV-2 Antibodies in COVID-19 Convalescent Plasma using a Coronavirus Antigen Microarray

Rafael R. de Assis; Aarti Jain; Rie Nakajima; Algis Jasinskas; Jiin Felgner; Joshua M. Obiero; Oluwasanmi Adenaiye; Sheldon Tai; Filbert Hong; Philip Norris; Mars Stone; Graham Simmons; Anil Bagri; Martin Schreiber; Andreas Buser; Andreas Holbro; Manuel Battegay; Donald K. Milton; Huw Davies; Laurence M. Corash; Michael P. Busch; Philip L. Felgner; Saahir Khan

The mean fluorescence intensity (MFI) of each antigen was determined by the average of the median fluorescence signal of four replicate spots. The fluorescence signal for each spot was determined by its signal intensity subtracted by the background fluorescence. Antigens containing a human Fc tag were removed from the analysis, as the secondary antibodies used for quantification are known to bind to human Fc; non-human Fc tag did not interfere with the assay. All statistical analyses were conducted using R version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria). MFI was normalized using the normalize.quantiles.use.target function from the proprocessCore package (version 1.48.0). As a target for normalization, a vector containing the median MFI for IgG or IgA was constructed. Descriptive statistics were used to summarize the IgA and IgG reactivity measured as MFI. Wilcoxon Rank Sum tests with p < 0.05 corrected for multiple comparisons were used to compare the mean differences between groups. Receiver Operating Characteristic Area Under the Curve (ROC AUC) values for each antigen were calculated by comparing positive and negative specimens using the pROC package (version 1.16.2). Antigens were ranked based on their ROC AUC values, and high performing antigens with ROC AUC > 0.95 were identified. Data visualization was performed using the ggplot2 package (version 3.3.0).