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A coronavirus antigen microarray (COVAM) was constructed containing 11 SARS-CoV-2, 5 SARS-1, 5 MERS, and 12 seasonal coronavirus recombinant proteins. The array is designed to measure immunoglobulin isotype and subtype levels in serum or plasma samples against each of the individual antigens printed on the array. We probed the COVAM with COVID-19 convalescent plasma (CCP) collected from 99 donors who recovered from a PCR+ confirmed SARS-CoV-2 infection. The results were analyzed using two computational approaches, a generalized linear model (glm) and random forest (RF) prediction model, to classify individual specimens as either Reactive or non-reactive against the SARS-CoV-2 antigens. A training set of 88 pre-COVID-19 specimens (PreCoV) collected in August 2019 and102 positive specimens from SARS-CoV-2 PCR+ confirmed COVID-19 cases was used for these analyses. Results compared with an FDA emergency use authorized (EUA) SARS-CoV2 S1-based total Ig chemiluminescence immunoassay (Ortho Clinical Diagnostics VITROS Anti-SARS-CoV-2 Total, CoV2T) and with a SARS-CoV-2 S1-S2 spike-based pseudovirus micro neutralization assay (SARS-CoV-2 reporter viral particle neutralization titration (RVPNT) showed high concordance between the three 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 91%. 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 three main clusters with distinct reactivity intensities and patterns. These patterns were not recapitulated by adjusting the VITROS CoV2T or RVPNT assay thresholds. Plasma classified by COVAM reactivity patterns offers potential to improve CCP therapeutic efficacy CoV2T alone. The use of a SARS-CoV-2 antigen array can qualify CCP for administration as a treatment for acute COVID-19, and interrogate vaccine immunogenicity and performance in preclinical, clinical studies, and routine vaccination to identify antibody responses predictive of protection from infection and disease.

Efficacy of donated COVID-19 convalescent plasma (dCCP) is uncertain and may depend on antibody titers, neutralizing capacity, timing of administration, and patient characteristics.In a single-center hypothesis-generating prospective case-control study with 1:2 matched dCCP recipients to controls according to disease severity at day 1, hospitalized adults with COVID-19 pneumonia received 2 × 200 ml pathogen-reduced treated dCCP from 2 different donors. We evaluated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in COVID-19 convalescent plasma donors and recipients using multiple antibody assays including a Coronavirus antigen microarray (COVAM), and binding and neutralizing antibody assays. Outcomes were dCCP characteristics, antibody responses, 28-day mortality, and dCCP -related adverse events in recipients.Eleven of 13 dCCPs (85%) contained neutralizing antibodies (nAb). PRT did not affect dCCP antibody activity. Fifteen CCP recipients and 30 controls (median age 64 and 65 years, respectively) were enrolled. dCCP recipients received 2 dCCPs from 2 different donors after a median of one hospital day and 11 days after symptom onset. One dCCP recipient (6.7%) and 6 controls (20%) died (p = 0.233). We observed no dCCP-related adverse events. Transfusion of unselected dCCP led to heterogeneous SARS CoV-2 antibody responses. COVAM clustered dCCPs in 4 distinct groups and showed endogenous immune responses to SARS-CoV-2 antigens over 14-21 days post dCCP in all except 4 immunosuppressed recipients.PRT did not impact dCCP anti-virus neutralizing activity. Transfusion of unselected dCCP did not impact survival and had no adverse effects. Variable dCCP antibodies and post-transfusion antibody responses indicate the need for controlled trials using well-characterized dCCP with informative assays.

COVID-19 hospitalizations are a key indicator of the SARS-CoV-2 pandemic. The US Centers for Disease Prevention and Control describes oxygen supplementation as a measure of severe disease. SARS-CoV-2 PCR positive patients hospitalized without hypoxia or requiring oxygen may have been identified incidentally due to routine screening practices. We describe the application of a revised case definition for COVID-19 hospitalization based on the case-patient oxygen requirement on admission. Using data collected from consecutive SARS-CoV-2 PCR positive hospital admissions in December 2021 and January 2022 at a large safety net hospital in Los Angeles County, we highlight differences between patients hospitalized with COVID-19 (i.e., no oxygen requirement on admission or `incidental` infection) and those hospitalized for COVID-19 (i.e., oxygen requirement on admission). We conducted multivariable modeling to determine the effect of age as a positive predictor of COVID-19 hospitalization and vaccination or prior infection as a negative predictor. The revised case-definition resulted in a substantial decrease in the number of COVID-19 hospitalizations during the study period: 67.5% of SARS-CoV-2 PCR positive hospital admissions were not for COVID-19 but with COVID-19. A revised case-definition for COVID-19 hospitalization that includes the oxygen requirement on admission is needed to more accurately monitor the pandemic and inform public health policy.

Recent studies provide conflicting evidence on the persistence of SARS-CoV-2 immunity induced by mRNA vaccines. Here, we aim to quantify the persistence of humoral immunity following vaccination using a coronavirus antigen microarray that includes 10 SARS-CoV-2 antigens. In a prospective longitudinal cohort of 240 healthcare workers, composite SARS-CoV-2 IgG antibody levels did not wane significantly over a 6-month study period. In the subset of the study population previously exposed to SARS-CoV-2 based on seropositivity for nucleocapsid antibodies, higher composite anti-spike IgG levels were measured before the vaccine but no significant difference from unexposed individuals was observed at 6 months. Age, vaccine type, or worker role did not significantly impact composite IgG levels, although non-significant trends towards lower antibody levels in older participants and higher antibody levels with Moderna vaccine were observed at 6 months. A small subset of our cohort were classified as having waning antibody titers at 6 months, and these individuals were less likely to work in patient care roles and more likely to have prior exposure to SARS-CoV-2.

BackgroundEarly evaluations of healthcare professional (HCP) COVID-19 risk occurred during insufficient personal protective equipment and disproportionate testing, contributing to perceptions of high patient-care related HCP risk. We evaluated HCP COVID-19 seropositivity after accounting for community factors and coworker outbreaks.MethodsPrior to universal masking, we conducted a single-center retrospective cohort plus cross-sectional study. All HCP (1) seen by Occupational Health for COVID-like symptoms (regardless of test result) or assigned to (2) dedicated COVID-19 units, (3) units with a COVID-19 HCP outbreak, or (4) control units from 01/01/2020 to 04/15/2020 were offered serologic testing by an FDA-authorized assay plus a research assay against 67 respiratory viruses, including 11 SARS-CoV-2 antigens. Multivariable models assessed the association of demographics, job role, comorbidities, care of a COVID-19 patient, and geocoded socioeconomic status with positive serology.ResultsOf 654 participants, 87 (13.3%) were seropositive; among these 60.8% (N = 52) had never cared for a COVID-19 patient. Being male (OR 1.79, CI 1.05–3.04,p = 0.03), working in a unit with a HCP-outbreak unit (OR 2.21, CI 1.28–3.81,p p = 0.05), and ethnically Latino (OR 2.10, CI 1.12–3.96,p = 0.02) were positively-associated with COVID-19 seropositivity, while working in dedicated COVID-19 units was negatively-associated (OR 0.53, CI = 0.30–0.94,p = 0.03). The research assay identified 25 additional seropositive individuals (78 [12%] vs. 53 [8%],p ConclusionsPrior to universal masking, HCP COVID-19 risk was dominated by workplace and community exposures while working in a dedicated COVID-19 unit was protective, suggesting that infection prevention protocols prevent patient-to-HCP transmission.Article summaryPrior to universal masking, HCP COVID-19 risk was dominated by workplace and community exposures while working in a dedicated COVID-19 unit was protective, suggesting that infection prevention protocols prevent patient-to-HCP transmission.

Background: Early evaluations of healthcare professional (HCP) COVID-19 risk occurred during insufficient personal protective equipment and disproportionate testing, contributing to perceptions of high patient-care related HCP risk. We evaluated HCP COVID-19 seropositivity after accounting for community factors and coworker outbreaks.Methods: Prior to universal masking, we conducted a single-center retrospective cohort plus cross-sectional study. All HCP 1) seen by Occupational Health for COVID-like symptoms (regardless of test result) or assigned to 2) dedicated COVID-19 units, 3) units with a COVID-19 HCP outbreak, or 4) control units from 01/01/2020-04/15/2020 were offered serologic testing by an FDA-authorized assay plus a research assay against 67 respiratory viruses, including 11 SARS-CoV-2 antigens. Multivariable models assessed the association of demographics, job role, comorbidities, care of a COVID-19 patient, and geocoded socioeconomic status with positive serology.Results: Of 654 participants, 87 (13.3%) were seropositive; among these 60.8% (N=52) had never cared for a COVID-19 patient. Being male (OR 1.79, CI 1.05-3.04, p=0.03), working in a HCP-outbreak unit (OR 2.21, CI 1.28-3.81, pConclusions: Prior to universal masking, HCP COVID-19 risk was dominated by workplace and community exposures while working in a dedicated COVID-19 unit was protective, suggesting that infection prevention protocols prevent patient-to-HCP transmission.

BackgroundProtecting health care workers (HCWs) during the coronavirus disease 2019 (COVID-19) pandemic is essential. Serologic testing can identify HCWs who had minimally symptomatic severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections that were missed by occupational screening based on daily symptom and temperature checks. Recent studies report conflicting results regarding the impact of occupational factors on SARS-CoV-2 seropositivity amongst HCWs.MethodsThe study population included all hospital workers at an academic medical center in Orange County, California. SARS-CoV-2 seropositivity was assessed from a fingerstick blood specimen using a coronavirus antigen microarray, which compares IgM and IgG antibodies against a panel of SARS-CoV-2 antigens with positive and negative controls to identify prior SARS-CoV-2 infection with 98% specificity and 93% sensitivity. Demographic, occupational, and clinical factors were surveyed and their effect on seropositivity estimated using multivariable logistic regression analysis.ResultsAmongst 1,557 HCWs with complete data, SARS-CoV-2 seropositivity was 10.8%. Risk factors for increased seropositivity included male gender, exposure to COVID-19 outside of work, working in food or environmental services, and working in COVID-19 units. Amongst the 1,103 HCW who were seropositive but missed by occupational screening, additional risk factors included younger age and working in administration.ConclusionsSARS-CoV-2 seropositivity is significantly higher than reported case counts even amongst HCWs who are meticulously screened. Seropositive HCWs missed by occupational screening were more likely to be younger, work roles without direct patient care, or have COVID-19 exposure outside of work.Key PointsSARS-CoV-2 seropositivity risk factors amongst health care workers included male gender, nonoccupational exposure, food or environmental services role, and COVID-19 unit location. Those missed by occupational screening were younger, in roles without direct patient care, or exposed outside of work.

A coronavirus antigen microarray (COVAM) was constructed containing 11 SARS-CoV-2, 5 SARS-1, 5 MERS, and 12 seasonal coronavirus recombinant proteins. The array is designed to measure immunoglobulin isotype and subtype levels in serum or plasma samples against each of the individual antigens printed on the array. We probed the COVAM with COVID-19 convalescent plasma (CCP) collected from 99 donors who recovered from a PCR+ confirmed SARS-CoV-2 infection. The results were analyzed using two computational approaches, a generalized linear model (glm) and Random Forest (RF) prediction model, to classify individual specimens as either Reactive or Non-Reactive against the SARS-CoV-2 antigens. A training set of 88 pre-COVID-19 specimens (PreCoV) collected in August 2019 and102 positive specimens from SARS-CoV-2 PCR+ confirmed COVID-19 cases was used for these analyses. Results compared with an FDA emergency use authorized (EUA) SARS-CoV2 S1-based total Ig chemiluminescence immunoassay (Ortho Clinical Diagnostics VITROS® Anti-SARS-CoV-2 Total, CoV2T) and with a SARS-CoV-2 S1-S2 spike-based pseudovirus micro neutralization assay (SARS-CoV-2 reporter viral particle neutralization titration (RVPNT) showed high 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.

Background The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care. Methods This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia. Results Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years [range, 21–88 years]; 64% [36/55] male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years [range, 27–88 years]; 55% [22/40] male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87–1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease. Conclusions and relevance We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.

BackgroundRecent epidemiological evidence has demonstrated a higher rate of COVID-19 hospitalizations and deaths among minorities. This pattern of race-ethnic disparities emerging throughout the United States raises the question of what social factors may influence spread of a highly transmissible novel coronavirus. The purpose of this study is to describe race-ethnic and socioeconomic disparities associated with COVID-19 in patients in our community in Orange County, California and understand the role of individual-level factors, neighborhood-level factors, and access to care on outcomes.MethodsThis is a case-series of COVID-19 patients from the University of California, Irvine (UCI) across six-weeks between 3/12/2020 and 4/22/2020. Note, California’s shelter-in-place order began on 3/19/2020. Individual-level factors included race-ethnicity status were recorded. Neighborhood-level factors from census tracts included median household income, mean household size, proportion without a college degree, proportion working from home, and proportion without health insurance were also recorded.ResultsA total of 210-patients tested were COVID-19 positive, of which 73.3% (154/210) resided in Orange County. Hispanic/Latinx patients residing in census tracts below the median income demonstrated exponential growth (rate = 55.9%, R2 = 0.9742) during the study period. In addition, there was a significant difference for both race-ethnic (p < 0.001) and income bracket (p = 0.001) distributions prior to and after California’s shelter-in-place. In addition, the percentage of individuals residing in neighborhoods with denser households (p = 0.046), lower levels of college graduation (p < 0.001), health insurance coverage (p = 0.01), and ability to work from home (p < 0.001) significantly increased over the same timeframe.Conclusions and RelevanceOur study examines the race-ethnic disparities in Orange County, CA, and highlights vulnerable populations that are at increased risk for contracting COVID-19. Our descriptive case series illustrates that we also need to consider socioeconomic factors, which ultimately set the stage for biological and social disparities.

The current practice for diagnosis of COVID-19, based on SARS-CoV-2 PCR testing of pharyngeal or respiratory specimens in a symptomatic patient at high epidemiologic risk, likely underestimates the true prevalence of infection. Serologic methods can more accurately estimate the disease burden by detecting infections missed by the limited testing performed to date. Here, we describe the validation of a coronavirus antigen microarray containing immunologically significant antigens from SARS-CoV-2, in addition to SARS-CoV, MERS-CoV, common human coronavirus strains, and other common respiratory viruses. A comparison of antibody profiles detected on the array from control sera collected prior to the SARS-CoV-2 pandemic versus convalescent blood specimens from virologically confirmed COVID-19 cases demonstrates near complete discrimination of these two groups, with improved performance from use of antigen combinations that include both spike protein and nucleoprotein. This array can be used as a diagnostic tool, as an epidemiologic tool to more accurately estimate the disease burden of COVID-19, and as a research tool to correlate antibody responses with clinical outcomes., COVID-19 diagnosis is commonly performed by PCR testing, however, serologic methods are more accurate and versatile for monitoring disease burden and epidemiology. Here the authors report a protein microarray with antigens from SARS-CoV-2, SARS-CoV, MERS-CoV as well as common human respiratory viruses.

The current practice for diagnosis of SARS-CoV-2 infection relies on PCR testing of nasopharyngeal or respiratory specimens in a symptomatic patient at high epidemiologic risk. This testing strategy likely underestimates the true prevalence of infection, creating the need for serologic methods to detect infections missed by the limited testing to date. Here, we describe the development of a coronavirus antigen microarray containing immunologically significant antigens from SARS-CoV-2, in addition to SARS-CoV, MERS-CoV, common human coronavirus strains, and other common respiratory viruses. A preliminary study of human sera collected prior to the SARS-CoV-2 pandemic demonstrates overall high IgG reactivity to common human coronaviruses and low IgG reactivity to epidemic coronaviruses including SARS-CoV-2, with some cross-reactivity of conserved antigenic domains including S2 domain of spike protein and nucleocapsid protein. This array can be used to answer outstanding questions regarding SARS-CoV-2 infection, including whether baseline serology for other coronaviruses impacts disease course, how the antibody response to infection develops over time, and what antigens would be optimal for vaccine development.

Background. COVID-19, caused by the SARS-CoV-2 virus, is a pandemic disease with high morbidity and mortality. Currently, available therapeutic options for COVID-19 are limited. Prior experience in epidemics with convalescent plasma (CP) containing antibodies to viruses has demonstrated variable indications of therapeutic efficacy for: Influenza, Argentine Hemorrhagic Fever, and SARS. Characterizing antibody titers to viruses has indicated correlation with therapeutic efficacy. Convalescent COVID-19 patients with potent SARS-CoV-2 antibody responses can serve as plasma donors for immune therapy. However, antibody responses are variable, many donors are first-time higher risk blood donors, and rapid assays to select optimal CP immune efficacy are limited. Pathogen inactivation (PI) of CP can reduce the risk of transfusion-transmitted infection by unrecognized pathogens. Objectives. This study characterized COVID-19 PI-CP activity; and evaluated efficacy and safety of PI CP transfusion in a case matched controlled cohort of acute COVID-19 patients. Methods. COVID-19 apheresis CP (650 - 1300 mL) was collected from nasopharyngeal PCR + outpatients following 2 PCR negative tests or 28 days after symptom resolution. Amotosalen-UVA PI of CP (INTERCEPT Blood System for Plasma) was performed, and antibody efficacy before and after PI was characterized by: VSV reporter pseudo-virus plaque neutralization (RVPN) NT-50 titer (Vitalant Research Institute, San Francisco), antibody to S and N virus proteins by agglutination-dependent antibody PCR (ADAP, Enable Biosciences, San Francisco), virus ACE-2 soluble receptor neutralization assay (Enable Biosciences), and SARS-CoV-2 antibody profile by coronavirus microarray (University of California, Irvine). Patient inclusion criteria were: confirmed SARS-CoV-2 infection, hospitalization, pulmonary infiltrates, availability of ABO compatible CP, and informed consent. CP patients were matched with control patients (CTRL) for disease severity at diagnosis by standardized clinical risk score (W. Liang et al JAMA Intern Med 2020) and concomitant Tocilizumab use. CP Patients received a total of 400 mL of PI CP from 2 donors over 48 hours and standard therapy. CTRL patients received standard COVID-19 therapy without CP. The primary outcome was in-hospital death to day 28. Secondary outcomes included: progression to intubation, admission to ICU, time to discharge, serious adverse events, NP viral clearance, plasma viral clearance, and humoral immune responses. Differences between CP and CTRL patients were assessed by the Mann-Whitney test for continuous variables, and by Fisher's exact test for categorical variables. Progression to ICU and intubation were analyzed as odds ratios calculated by conditional logistic regression. Results. 15 CP and 30 CTRL patients were enrolled. One CP patient was admitted in cardiogenic shock. Only 2 of 15 CP cohort patients had detectable IgG antibody to SARS CoV-2 S1 antigen at study entry. 3 of 15 PI CP donors had negligible SARS CoV-2 IgG antibodies to all antigens, and demonstrated poor neutralization efficacy. 12/15 CP had effective RVPN titers (> 1:80), RVPN titers were correlated with ACE-2 neutralization antibody titers (r2 = 0.83), and had significant activity specific for S and RBD antigens by microarray profiling (Figure 1). SARS CoV-2 antibody levels were variable between CP donors, but not impacted by PI (Figure 1). Baseline characteristics of CP and matched CTRL patients were similar (Table 1). Sensitivity analysis was performed assessing mortality after exclusion of one CTRL patient admitted in cardiogenic shock and the 2 respective controls. In-hospital 28-day mortality was lower in the CP cohort (0/14) compared to 5/28 CTRL, p = 0.151, 2-sided Fisher's exact test. Progression to intubation, ICU admission, and days in hospital were not significantly different (Table 1). There was a trend toward decreased inflammatory response (CRP normalization) in CP patients. Conclusions. In hospital mortality of COVID-19 patients was lower in the PI-CP cohort, but not statistically significant. 15% of CP had ineffective antibody by multiple assays. However, PI did not impact CP anti-SARS-CoV-2 activity. PI of plasma provides reduced risk of transfusion transmitted infection from COVID-19 CP donors. In this study, PI CP was safe, and may be effective for early treatment of hospitalized COVID-19 patients. Disclosures von Goetz: Cerus Corporation: Current Employment, Current equity holder in publicly-traded company. Khan:Nanommune Inc.: Current equity holder in private company. Tsai:Enable Biosciences: Current Employment. Robinson:Enable Biosciences: Current Employment. Seftel:Enable Biosciences: Current Employment, Current equity holder in private company. Bagri:Cerus Corporation: Current Employment, Current equity holder in publicly-traded company. Corash:Cerus Corporation: Current Employment, Current equity holder in publicly-traded company.

To detect the presence of antibodies in blood against SARS-CoV-2 in a highly sensitive and specific manner, here we describe a robust, inexpensive ($200), 3D-printable portable imaging platform (TinyArray imager) that can be deployed immediately in areas with minimal infrastructure to read coronavirus antigen microarrays (CoVAMs) that contain a panel of antigens from SARS-CoV-2, SARS-1, MERS, and other respiratory viruses. Application includes basic laboratories and makeshift field clinics where a few drops of blood from a finger prick could be rapidly tested in parallel for the presence of antibodies to SARS-CoV-2 with a test turnaround time of only 2–4 h. To evaluate our imaging device, we probed and imaged coronavirus microarrays with COVID-19-positive and negative sera and achieved a performance on par with a commercial microarray reader 100× more expensive than our imaging device. This work will enable large scale serosurveillance, which can play an important role in the months and years to come to implement efficient containment and mitigation measures, as well as help develop therapeutics and vaccines to treat and prevent the spread of COVID-19., The TinyArray imager, a robust inexpensive portable imaging device, can detect antibodies against SARS-CoV-2 based on coronavirus antigen microarrays.

The influenza virus remains a significant cause of mortality worldwide due to the limited effectiveness of currently available vaccines. A key challenge to the development of universal influenza vaccines is high antigenic diversity resulting from antigenic drift. Overcoming this challenge requires novel research tools to measure the breadth of serum antibodies directed against many virus strains across different antigenic subtypes. Here, we present a protocol for analyzing the breadth of serum antibodies against diverse influenza virus strains using a protein microarray of influenza antigens. This influenza antigen microarray is constructed by printing purified hemagglutinin and neuraminidase antigens onto a nitrocellulose-coated membrane using a microarray printer. Human sera are incubated on the microarray to bind antibodies against the influenza antigens. Quantum-dot-conjugated secondary antibodies are used to simultaneously detect IgG and IgA antibodies binding to each antigen on the microarray. Quantitative antibody binding is measured as fluorescence intensity using a portable imager. Representative results are shown to demonstrate assay reproducibility in measuring subtype-specific and cross-reactive influenza antibodies in human sera. Compared to traditional methods such as ELISA, the influenza antigen microarray provides a high throughput multiplexed approach capable of testing hundreds of sera for multiple antibody isotypes against hundreds of antigens in a short time frame, and thus has applications in sero-surveillance and vaccine development. A limitation is the inability to distinguish binding antibodies from neutralizing antibodies.