The rapid rise of label-free biosensing technologies has led to multiple creative strategies for the detection of macromolecules in complex biological solutions for disease state monitoring, drug discovery, and basic science research. A challenge with these techniques is that assays conducted in complex media such as serum suffer from nonspecific binding of matrix constituents. In label-free biosensors, it is virtually impossible to distinguish these nonspecific interactions without the use of a reference (negative control) probe. Only with reference subtraction can the specific binding signal be faithfully reported. To date, this has been a sparsely studied area in the biosensing field. Here, we report an FDA-inspired framework for optimum control probe selection and a systematic analysis for determining the optimal negative control probe given two monoclonal antibody capture probes on photonic ring resonator sensors. Briefly, while the differences in assay performance for IL-17A and CRP were found to be subtle, the best-scoring reference control based on the bioanalytical parameters of linearity, accuracy, and selectivity differed for each analyte. In the IL-17A assay, BSA scored the highest at 83%, while mouse IgG1 isotype control antibody placed a close second with 75%. With respect to the CRP assay, the rat IgG1 isotype control antibody scored the highest at 95%, while anti-FITC scored the second highest at 89%. These results suggest that although isotype-matching to the capture antibody may be tempting, the best on-chip reference control must be optimized on a case-by-case basis using the framework we report.

Wearable, mobile, and point-of-care (POC) sensors comprise a rapidly expanding field of devices aimed at improving human health by relaying real-time biometric data such as heart rate and glucose levels. The current scope of what these devices can offer healthcare is limited by their inability to measure biomarkers associated with inflammation, well-being, and disease. Photonic biosensors that integrate sensing elements directly with spectrometers, lasers, and detectors are an attractive approach to enabling POC sensors, with distinct advantages in terms of size, weight, power consumption, and cost. Here, we have demonstrated for the first time the integration of photonic microring resonator biosensors with an on-chip microring filter bank spectrometer for the controlled detection of inflammatory biomarker C-reactive protein (CRP) in serum. We demonstrate that sensor and spectrometer performance is tolerant of temperature variation, as temperature dependence moves in parallel. Finally, we assess the impact of manufacturing variability on the 300 mm wafer scale on the performance of the spectrometer. Taken together, these results suggest that integration of on-chip ring filter bank spectrometers with ring resonator-based biosensors constitutes an attractive approach toward cost-effective integrated sensor development.

Detection of antibodies to upper respiratory pathogens is critical to surveillance, assessment of the immune status of individuals, vaccine development, and basic biology. The urgent need for antibody detection tools has proven particularly acute in the COVID-19 era. Array-based tools are desirable as methods for assessing broader patterns of antigen-specific responses, as well as providing information on SARS-CoV-2 immunity in the context of pre-existing immunity to other viruses. Also, methods that rapidly and quantitatively detect antibody responses to SARS-CoV-2 antigens using small (fingerstick) quantities of blood are essential for monitoring immunity at a global scale. This talk will describe the development of two optical sensor platforms (Arrayed Imaging Reflectometry, and an integrated photonics platform fabricated at AIM Photonics) for quantifying antibodies to SARS-CoV-2 and other upper respiratory pathogens, and oriented towards the needs of multiplex detection and speed.

Detection of antibodies to upper respiratory pathogens is critical to surveillance, assessment of the immune status of individuals, vaccine development, and basic biology. The urgent need for antibody detection tools has proven particularly acute in the COVID-19 era. We report a multiplex label-free antigen microarray on the Arrayed Imaging Reflectometry (AIR) platform for detection of antibodies to SARS-CoV-2, SARS-CoV-1, MERS, three circulating coronavirus strains (HKU1, 229E, OC43) and three strains of influenza. We find that the array is readily able to distinguish uninfected from convalescent COVID-19 subjects, and provides quantitative information about total Ig, as well as IgG- and IgM-specific responses.

Multiplex assays for autoantibodies have shown utility both in research towards understanding the basic biology of autoimmune disease, and as tools for clinical diagnosis. New label-free multiplex analysis methods have the potential to streamline both the process of assay development and assay workflow. We report fabrication and testing of a 5-plex autoantigen microarray using the Arrayed Imaging Reflectometry (AIR) platform. This label-free technology provides rapid, sensitive, and quantitative detection of an arbitrary number of analytes in a standard multiwell format. In this work, we demonstrate that AIR is able to detect antibodies to Ro60, La/SSB, Scl-70, BicD2, and Ro52 in single-donor human serum samples with multiplex results comparable to singleplex ELISA or Luminex assays.

Rapid changes in influenza A virus (IAV) antigenicity create challenges in surveillance, disease diagnosis, and vaccine development. Further, serological methods for studying antigenic properties of influenza viruses often rely on animal models and therefore may not fully reflect the dynamics of human immunity. We hypothesized that arrays of human monoclonal antibodies (hmAbs) to influenza could be employed in a pattern-recognition approach to expedite IAV serology and to study the antigenic evolution of newly emerging viruses. Using the multiplex, label-free Arrayed Imaging Reflectometry (AIR) platform, we have demonstrated that such arrays readily discriminated among various subtypes of IAVs, including H1, H3 seasonal strains, and avian-sourced human H7 viruses. Array responses also allowed the first determination of antigenic relationships among IAV strains directly from hmAb responses. Finally, correlation analysis of antibody binding to all tested IAV subtypes allowed efficient identification of broadly reactive clones. In addition to specific applications in the context of understanding influenza biology with potential utility in "universal" flu vaccine development, these studies validate AIR as a platform technology for studying antigenic properties of viruses and also antibody properties in a high-throughput manner. We further anticipate that this approach will facilitate advances in the study of other viral pathogens.

Influenza serology has traditionally relied on techniques such as hemagglutination inhibition, microneutralization, and ELISA. These assays are complex, challenging to implement in a format allowing detection of several types of antibody-analyte interactions at once (multiplex), and troublesome to implement in the field. As an alternative, we have developed a hemagglutinin microarray on the Arrayed Imaging Reflectometry (AIR) platform. AIR provides sensitive, rapid, and label-free multiplex detection of targets in complex analyte samples such as serum. In preliminary work, we demonstrated the application of this array to the testing of human samples from a vaccine trial. Here, we report the application of an expanded label-free hemagglutinin microarray to the analysis of avian serum samples. Samples from influenza virus challenge experiments in mallards yielded strong, selective detection of antibodies to the challenge antigen in most cases. Samples acquired in the field from mallards were also analyzed, and compared with viral hemagglutinin inhibition and microneutralization assays. We find that the AIR hemagglutinin microarray can provide a simple and robust alternative to standard methods, offering substantially greater information density from a simple workflow.