The 2019 SARS CoV-2 (COVID-19) pandemic has illustrated the need for rapid and accurate diagnostic tests. In this work, a multiplexed grating-coupled fluorescent plasmonics (GC-FP) biosensor platform was used to rapidly and accurately measure antibodies against COVID-19 in human blood serum and dried blood spot samples. The GC-FP platform measures antibody-antigen binding interactions for multiple targets in a single sample, and has 100% selectivity and sensitivity (n = 23) when measuring serum IgG levels against three COVID-19 antigens (spike S1, spike S1S2, and the nucleocapsid protein). The GC-FP platform yielded a quantitative, linear response for serum samples diluted to as low as 1:1600 dilution. Test results were highly correlated with two commercial COVID-19 antibody tests, including an enzyme linked immunosorbent assay (ELISA) and a Luminex-based microsphere immunoassay. To demonstrate test efficacy with other sample matrices, dried blood spot samples (n = 63) were obtained and evaluated with GC-FP, yielding 100% selectivity and 86.7% sensitivity for diagnosing prior COVID-19 infection. The test was also evaluated for detection of multiple immunoglobulin isotypes, with successful detection of IgM, IgG and IgA antibody-antigen interactions. Last, a machine learning approach was developed to accurately score patient samples for prior COVID-19 infection, using antibody binding data for all three COVID-19 antigens used in the test.

The 2019 SARS CoV-2 (COVID-19) pandemic has highlighted the need for rapid and accurate tests to diagnose acute infection and immune response to infection. A multiplexed assay built on grating-coupled fluorescent plasmonics (GC-FP) was shown to have 100% selectivity and sensitivity (n = 23) when measuring serum IgG levels against three COVID-19 antigens (spike S1, spike S1S2, and the nucleocapsid protein). The entire assay takes less than 30 min, making it highly competitive with well-established ELISA and immunofluorescence assays. GC-FP is quantitative over a large dynamic range, providing a linear response for serum titers ranging from 1:25 to 1:1,600, and shows high correlation with both ELISA and a Luminex-based microsphere immunoassay (MIA) (Pearson r > 0.9). Compatibility testing with dried blood spot samples (n = 63) demonstrated 100% selectivity and 86.7% sensitivity. A machine learning (ML) model was trained to classify dried blood spot samples for prior COVID-19 infection status, based on the combined antibody response to S1, S1S2, and Nuc antigens. The ML model yielded 100% selectivity and 80% sensitivity and demonstrated a higher stringency than diagnosis with a single antibody-antigen response. The platform is flexible and will readily accommodate IgG, IgM, and IgA. Further, the assay uses sub-nanogram quantities of capture ligand and is thus readily modified to include additional antigens, which is shown by the addition of RBD in later iterations of the test. The combination of rapid, multiplexed, and quantitative detection for both blood serum and dried blood spot samples makes GC-FP an attractive approach for COVID-19 antibody testing.

Lyme disease, which is primarily caused by infection with the bacterium Borrelia burgdorferi in the United States or other Borrelia species internationally, presents an ongoing challenge for diagnostics. Serological testing is the primary means of diagnosis but testing approaches differ widely, with varying degrees of sensitivity and specificity. Moreover, there is currently no reliable test to determine disease resolution following treatment. A distinct challenge in Lyme disease diagnostics is the variable patterns of human immune response to a plurality of antigens presented by Borrelia spp. during the infection. Thus, multiplexed testing approaches that capture these patterns and detect serological response against multiple antigens may be the key to prompt, accurate Lyme disease diagnosis. In this review, current state-of-the-art multiplexed diagnostic approaches are presented and compared with respect to their diagnostic accuracy and their potential for monitoring response to treatment.