concordance between the 3 assays. Three CCP specimens that were negative by the VITROS CoV2T immunoassay were also negative by both COVAM and the RVPNT assay. Concordance between VITROS CoV2T and COVAM was 96%, VITROS CoV2T and RVPNT 93%, and RVPNT and COVAM 95%. The discordances were all weakly reactive samples near the cutoff threshold of the VITROS CoV2T immunoassay. The multiplex COVAM allows CCP to be grouped according to antibody reactivity patterns against 11 SARS-CoV-2 antigens. Unsupervised K-means analysis, via the gap statistics, as well as hierarchical clustering analysis revealed 3 main clusters with distinct reactivity intensities and patterns. These patterns were not recapitulated by adjusting the VITROS CoV2T or RVPNT assay thresholds. Plasma classified according to these reactivity patterns may be better associated with CCP treatment efficacy than antibody levels alone. The use of a SARS-CoV-2 antigen array may be useful to qualify CCP for administration as a treatment for acute COVID-19 and to interrogate vaccine immunogenicity and performance in preclinical and clinical studies to understand and recapitulate antibody responses associated with protection from infection and disease.
Following exposure and recovery from SARS-CoV-2 infection, convalescent patients develop antigen specific adaptive T-and B-cell immune responses including binding and neutralizing antibodies (Ab). These immune responses may prevent reinfection or blunt the clinical consequences of future infectious exposures to the virus. Administration of COVID-19 convalescent plasma (CCP) from recovered patients is being employed for therapeutic use based on the belief that factors, including neutralizing Ab against SARS-CoV-2, present in the plasma may inhibit virus replication and improve patient outcomes.
Numerous clinical trials are underway aimed at understanding how best to administer CCP and to test the hypothesis that it is an effective treatment. A significant clinical trial involving more than 35,000 CCP treated COVID-19 patients has shown an efficacy signal from plasma with elevated Ab levels in patients treated within a few days after symptom onset [1] . Previous efforts to use convalescent plasma for treatment of infectious diseases have produced mixed results, possibly associated with differences in the antibody profile from different donors. Differences in the breadth and level of SARS-CoV-2-specific Ab in CCP may correlate with therapeutic efficacy. Consequently, investigated the performance of a Coronavirus Antigen Microarray (COVAM) in order to characterize and classify Ab reactivity in CCP before it is administered to patients.
COVAM is a multiplex assay platform for high-throughput serological studies. The microarrays are produced by printing validated and purified recombinant antigens on nitrocellulose-coated slides.
The COVAM has 11 SARS-CoV-2 antigens, 5 SARS-1, 5 MERS and 12 seasonal coronavirus antigens, as well as 35 antigens from 5 other viruses that cause acute respiratory infections [10, 11] . A complete list of the COVAM antigens can be found in Supplementary Table 1 . The arrays are designed to determine the Ab profile in serum or plasma samples to confirm prior exposure following suspected infection with the viruses and to monitor Ab changes over time. The arrays are designed for high throughput and low-cost testing and hence are suitable for epidemiology studies as a serologic surveillance tool to determine the prevalence and levels of Abs indicating viral exposure within individuals over the course of an epidemic.
Here we probed the COVAM with CCP collected from 99 donors between 4/18/2020 -5/6/2020 from 8 regions across the US. Although the donors were all recovered SARS-CoV-2 PCR+ COVID-19 cases, their Ab response profiles against 11 SARS-CoV-2 fell into distinct groups with different Ab levels and breadth of the responses particularly to 4 SARS-CoV-2 antigens. If these classification groups correlate with CCP efficacy, a SARS-CoV-2 antigen microarray may be useful to qualify CCP for treatment of acute COVID-19.
The COVAM slides were probed with human plasma and analyzed as described elsewhere (Jain et. al.,2016; Nakajima et. al., 2018 and Khan et al., 2019) . Briefly, the specimens were diluted 1:100 in 1X Protein Array Blocking Buffer (GVS Life Sciences, Sanford, ME), transferred to the slides and incubated overnight at 4°C. The slides were then washed 3 times for 5 minutes each with t-TBS buffer (20 mM Tris-HCl, 150 mM NaCl, 0.05% Tween-20 in ddH2O adjusted to pH 7.5 and filtered) at room temperature (RT). Then, anti-human IgG and anti-human IgA secondary Ab were added to each pad at a dilution of 1:100 (in Protein Array blocking Buffer) and incubated for 2h at RT under agitation. Pads were then washed with t-TBS 3 times for 5 minutes each and dried. The slides were imaged using ArrayCam imager (Grace Bio-Labs, Bend, OR). In order to measure non-specific binding of the secondary Ab, pads were incubated without the previous addition of human sera. The data acquisition and spot quantification were performed using the Scan ArrayExpress (V 3.0, PerkinElmer) software [2, [8] [9] .
In order to build the prediction models, a collection of samples with known exposure status to SARS-CoV-2 was used as a training set. This set was composed of 88 PreCoV specimens derived from frozen plasma components collected by Vitalant in July-September 2019, and 102 specimens from PCR confirmed cases was used for these analyses. Neutralization titers were measured as 50% neutralization (NT-50) by endpoint titration using a recombinant viral-particle neutralization test (RVPNT) and a cutoff equal to 1:40. The VITROS CoV2T chemiluminescent immunoassay assay was performed using either serum or plasma samples from Vitalant Research Institute (San Francisco, CA) according to the manufacturer instructions [3] . The test targets the spike protein and applies a predefined threshold value of 1.0 signal-to-cutoff (S/C) for IgG seropositivity but has a broad dynamic range with S/C values as high as 1000.
All data analysis was performed in the R programing environment (Version 4.0.2).
After data acquisition, data normalization was performed by the Quantile Normalization method using the "normalize.quantiles.use.target" function of the "preprocessCore" package (Version 1.50.0).
As reference for normalization, a collection of known positive and known negative samples (training set comprised of serial samples from recovered COVID-19 patients and plasma collected in the July-September 2019, respectively) was used.
To investigate the different reactivity profiles, the data were clustered and divided based on the reactivity to the 11 SARS-CoV-2 antigens. For the clustering, first the optimal number of clusters was estimated by the gap statistics on the K-means clustering analysis ("fviz_nbclust" function from the factoextra package (version 1.0.7). Then, a Hierarchical Clustering analysis ("hclust" function, method "ward.D2", from the "stats" package, version 4.0.2) was performed and the dendrogram cut in order to obtain a number of cluster defined by the previous calculated gap statistics. The assigned groups were obtained by cutting the dendrogram to obtain K = n groups, where n is the optimal number of clusters obtained from the gap statistics analysis.
Principal component Analysis (PCA) was also performed ("prcomp" function from the "stats" package version 4.0.0 as well as the "fviz_nbclust" function from the "factoextra" package, version
With goal of predicting exposure to SARS-CoV-2, the samples were first classified based on their overall reactivity profile. The prediction was performed using two main computational methods, a logistic regression model and a random forest prediction model.
For the logistic regression, a generalized linear model ("logit" family) was first generated ("glm" function from the "stats" package version 4.0.2) using the training set of. Then a receiver operating characteristic curve (ROC) was generated in order to obtain all the curve coordinates. This allows estimation of the specificity and sensitivity for each point of the curve and therefore defines an optimal cutoff point of the regression analysis fitted values. For the predictions described in this work, the model was built using four antigens: SARS-CoV-2 S1, SARS-CoV-2 S1.HisTag, SARS-CoV-2.S1.RBD, SARS.CoV.2.S1+S2. The test samples are then submitted to the logistic regression analysis and the fitted values compared to the defined cutoff for classification (positive or negative).
For the random forest analysis, like the logistic regression method, a model fit is generated (random forest version 4.6-14). For the random forest model, seven antigens were used: SARS-CoV-SARS.CoV.2.S1.RBD, SARS.CoV.2.S1+S2, SARS.CoV.2.S2.
The COVAM has 11 SARS-CoV-2 antigens, 5 SARS-1, 5 MERS and 12 seasonal coronavirus antigens. The complete list of the COVAM antigens, in the order displayed in the figures, is available The COVAM data are summarized in the heatmap in Figure 1 . There are 99 specimen IDs along the x-axis and 65 antigens along the y-axis (Supplementary Table 1 The COVAM reactivity against the nucleocapsid protein NP and the S2 domain of spike do not correlate with the VITROS S1 based CoV2T or the RVPNT assays, which is reasonable since Abmediated neutralization of virus entry is primarily based on blocking interactions between the RBD domain of S1 and ACE-2 receptors expressed on target cells in the assay.
Although there is concordance in the binary predictions of the 3 assays, the multiplex COVAM assay allows for a multivariant cluster analysis metric to classify CCP into different groups based on their reactivity patterns against several antigens. The VITROS CoV2T or RVPNT assays produce a single value for each specimen and a binary (reactive or non-reactive) result when that value is above or below a reactivity threshold, with quantitation of Ab intensity above the S/C = 1.0 and NT50 = 40 categorical thresholds. The COVAM interrogates IgG levels against 11 SARS-CoV-2 antigens and uses a computational algorithm to determine a binary outcome by comparing the results to the training set of COVID positive and negative controls.
Unsupervised cluster analysis can sort the specimens according COVAM antigen specific IgG reactivity patterns into at least 3 distinct groups based on the level of antibodies against a collection of NP, Spike, S1, S2 and RBD antigens (Figure 1) . Group 1 specimens have high Ab levels against many of the SARS-CoV-2 antigens, most markedly high reactivity to nucleocapsid protein (NP), S1
domain of spike protein, and the full spike S1+S2 protein. Group The results in Figure 3 show the IgG heatmap clustered (k-means), using the reactivity of the SARS-CoV-2 antigens, into these 3 groups. PCA analysis shows 3 distinct groups and the bar graphs confirm that Ab levels against each antigen differ significantly between each group. The heatmap and its dendrogram (Figure 3 ) indicate that the specimens in each group can be further split into subgroups. Figure 4 separates Group 1 specimens into 2 subgroups (1.1 and 1.2) evident from the heatmap (Figure 4a) and confirmed by the PCA (Figure 4c) . The bar graph (Figure 4b ) highlights a significant difference between subgroups 1.1 and 1.2 in reactivity against antigen S2 which is elevated in Group 1.2. Figure 5 separates Group 2 specimens into 2 subgroups (2.1 and 2.2) evident from the heatmap (Figure 5a ) and confirmed by PCA (Figure 5c ). The bar graph (Figure 5b) highlights a significant difference between subgroups 2.1 and 2.2 against the nucleocapsid protein and S2 which are both elevated in Group 2.2). Figure 6 separates Group 3 specimens into 2 groups (3.1 and 3.2) evident from the heatmap (Figure 6a ) and confirmed by PCA (Figure 6c) . From the bar graph and heat map it is evident that the reactivity of group 3 specimens is less than that of the other two groups, but the subgroup 3.2 is particularly lacking in antibodies to SARS-CoV-2 antigens.
The efficacy of CCP may vary depending on the donor, and there are different ways to classify the plasma before it is administered. One criterion is whether the donor was a PCR+ confirmed case.
Factors such as clinical disease severity and the time from symptom onset or recovery to collection of CCP units may contribute to the classification. Antibody level is a measurement that can be used to qualify CCP and antibodies against different antigen targets including the total spike S1/S2, S1, S2, RBD and NP proteins can be considered. Virus neutralization assay titers are another metric and pseudoviruses have been developed for this purpose to make it more convenient than live SARS-CoV-2 virus neutralization assays for routine use. In order to use these quantitative measurements to qualify CCP for clinical administration, a threshold can be established, to qualify a unit of plasma as acceptable for clinical use or rejected.
A multiplex antigen classification of CCP can also be considered as a criterion to qualify and accept or reject donor plasma for transfusion to COVID-19 patients. For example, all of the highly reactive COVAM Group 1 specimens (Figure 3 ) could be accepted and all of the low reactive Group 3 specimens rejected. Or subsets of any of the groups (Figure 4-6) could be accepted or rejected based on knowledge of the clinical efficacy of each major group or subgroup
The results plotted in Figure 7 show how multiplex antigen classification differs from criteria based on changing the binary threshold of a single assay. In Figure 7A the RVPN titer threshold was moved from the canonical standard 40, to 160, 320 and 640. Group 3 specimens increasingly fall below the cutoff as the threshold increases. Group 2 specimens are mostly above threshold at the 160 threshold and increasingly fall below the threshold as it is increased. Interestingly, all but one of the Group 1 specimens are below the highest RVPNT threshold of 640.
The VITROS CoV2T assay threshold can also be adjusted to select the specimens with higher signals (Figure 7B ). Moving the S/C threshold from 1 to 10 preferentially removes a high proportion of the low-level reactive Group 3 specimens. At the highest threshold of 150 all of the Group 3 specimens are removed while 40% and 30% of the Group 1 and 2 specimens, respectively, are above the threshold.
These assay-dependent and threshold-dependent differences in the classification of plasma specimens is further evident from the results in Figure 8 . Here the group 1, 2 and 3 specimens were separately sorted by the RVPNT titer ( Figure 8A) . The Titer Cutoff lines on Figure 8A shows how most of the Group 1 samples are above the threshold even at the highest 640 titer. The Group 2 and Group 3 specimen classifications are affected more by increasing the Cutoff titer. Similarly, the VITROS CoV2T data is plotted in Figure 8B and 3 different assay cutoff values are shown. It is evident that the VITROS CoV2T intensity is not well correlated to the RVPNT titer, and scatterplots (not show) give R 2 = 0.037. Increasing the VITROS CoV2T threshold cuts through all the groups.
These results indicate that RVPNT titer, VITROS CoV2T titer, and the COVAM multiplex cluster analysis patterns select different collections of plasma specimens.
Passive antibody transfer using convalescent plasma has previously been used to treat infectious diseases that involve the respiratory system including influenza [4] [5] [6] [7] . Methodologies like COVAM that quantitatively interrogate multiple specific signal intensity levels independently to classify specimens were called "chemometrics" in the pre-genomic period.
Modern multivariant statistical approaches that interrogate and classify a "fingerprint" of individual genomic measurements have been developed to classify genome sequence and expression data, and these computational methods can also be applied to clinical diagnostics. The test results from the COVAM assay described here take advantage of multivariant machine learning, pattern recognition, and clustering to classify plasma samples that have many antibodies at different levels against numerous SARS-CoV-2 antigens.
The COVAM was constructed to independently measure immunoglobulin isotype and subtype 