Detection of SARS-CoV-2 antibodies in serum and dried blood spot samples of vaccinated individuals using a sensitive homogeneous proximity extension assay.

A homogeneous PCR-based assay for sensitive and specific detection of antibodies in serum or dried blood spots (DBS) is presented and the method is used to monitor individuals infected with or vaccinated against SARS-CoV-2. Detection probes were prepared by conjugating the recombinant spike protein subunit 1 (S1), containing the receptor binding domain (RBD) of SARS-CoV-2, to each of a pair of specific oligonucleotides. The same was done for the nucleocapsid protein (NP). Upon incubation with serum or DBS samples, the bi- or multivalency of the antibodies (IgG, IgA or IgM) brings pairs of viral proteins with their conjugated oligonucleotides in proximity, allowing the antibodies to be detected by a modified proximity extension assay (PEA). Anti-S1 and anti-NP antibodies could be detected simultaneously from one incubation reaction. This Antibody PEA (AbPEA) test uses only 1 µl of neat or up to 100,000-fold diluted serum or one ø1.2 mm disc cut from a DBS. All 100 investigated sera and 21 DBS collected prior to the COVID-19 outbreak were negative, demonstrating a 100% specificity. The area under the curve, as evaluated by Receiver Operating Characteristic (ROC) analysis reached 0.998 (95%CI: 0.993-1) for samples taken from 11 days after symptoms onset. The kinetics of antibody responses were monitored after a first and second vaccination using serially collected DBS from 14 individuals. AbPEA offers highly specific and sensitive solution-phase antibody detection without requirement for secondary antibodies, no elution step when using DBS sample in a simple procedure that lends itself to multiplex survey of antibody responses.

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery.

The persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a "non-severe" and 80 had a "severe" outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases. The main clinical and hematochemical data associated with disease outcome were grouped with serological data to form a dataset for the supervised machine learning techniques. We identified nine proteins (i.e., CD200R1, MCP1, MCP3, IL6, LTBP2, MATN3, TRANCE, α2-MRAP, and KIT) that contributed to the correct classification of COVID-19 disease severity when combined with relative neutrophil and lymphocyte counts. By analyzing PEA, clinical and hematochemical data with statistical methods that were able to handle many variables in the presence of a relatively small sample size, we identified nine potential serum biomarkers of a "severe" outcome. Most of these were confirmed by literature data. Importantly, we found three biomarkers associated with central nervous system pathologies and protective factors, which were downregulated in the most severe cases.

Plasma Proteome Fingerprints Reveal Distinctiveness and Clinical Outcome of SARS-CoV-2 Infection.

BACKGROUND: We evaluated how plasma proteomic signatures in patients with suspected COVID-19 can unravel the pathophysiology, and determine kinetics and clinical outcome of the infection. METHODS: Plasma samples from patients presenting to the emergency department (ED) with symptoms of COVID-19 were stratified into: (1) patients with suspected COVID-19 that was not confirmed (n = 44); (2) non-hospitalized patients with confirmed COVID-19 (n = 44); (3) hospitalized patients with confirmed COVID-19 (n = 53) with variable outcome; and (4) patients presenting to the ED with minor diseases unrelated to SARS-CoV-2 infection (n = 20). Besides standard of care diagnostics, 177 circulating proteins related to inflammation and cardiovascular disease were analyzed using proximity extension assay (PEA, Olink) technology. RESULTS: Comparative proteome analysis revealed 14 distinct proteins as highly associated with SARS-CoV-2 infection and 12 proteins with subsequent hospitalization (p < 0.001). ADM, IL-6, MCP-3, TRAIL-R2, and PD-L1 were each predictive for death (AUROC curve 0.80-0.87). The consistent increase of these markers, from hospital admission to intensive care and fatality, supported the concept that these proteins are of major clinical relevance. CONCLUSIONS: We identified distinct plasma proteins linked to the presence and course of COVID-19. These plasma proteomic findings may translate to a protein fingerprint, helping to assist clinical management decisions.