Microarray Strategies for Multiplexed Immunochemical Analysis

Abstract

[eng] The current doctoral thesis titled “Microarray Strategies for Multiplexed Immunochemical Analysis” was carried out in the context of two research projects, expecting to develop multiplexed ligand-binding assays under microarray format for universal applications. On one side, framed by the FoodSmartphone project (ITN-Marie Curie Actions Horizon 2020) the PART I of the thesis was dedicated to generate a multiplexed and high-throughput screening platform based on DNA-directed immobilization (DDI) strategies for the detection of antibiotic residues in cow’s milk according to EU regulations. For this, three class selective monoclonal antibodies, in-house produced, targeting fluoroquinolones, sulfonamides and tylosin were utilized in an indirect competitive immunoassay format, achieving limits of detection in the order of ppb’s. The use of oligonucleotide strains conjugated to hapten molecules allowed site encoding the signal of each hapten-antibody pair, using a fluorescently labelled secondary antibodies for detection. After assay characterization a pre-validation study was performed with the idea to evaluate the application of the novel platform in blind samples, allowing the identification of at least 18 veterinary residues according to regulatory limits and with a sample processing capacity of 180 samples per run in less than 90 minutes. As part of the same project, the implementation of the immunoreagents utilized in the DDI microarray was evaluated in a portable and miniaturized imaging surface plasmon resonance (iSPR) sensor combining smartphone readout system as a proof of concept. It was possible to evidence the binding between one of the monoclonal antibodies to the respective hapten molecule in a label free configuration, for a promising transition towards more user-friendly and cost-effective bioanalytical tools coupled to smartphone systems. On the other hand, the PART II of the thesis was carried out in the context of the PoC4CoV project, attempting to produce point-of-care tests for the rapid detection of SARS-CoV-2 at different levels. Through this work the manufacture of a microarray chip based on SARS-CoV-2 peptides and proteins for personalized immunodiagnostic of COVID-19 was conducted. Initially, a rational design of potential immunogenic linear peptides (15-mer) derived from S and N proteins of the virus was proposed. A total of 28 peptide sequences were selected through computational models and immunogenicity studies and then synthesized also including peptides derived from variants of concern. After platform optimization studies, a multiplexed and high- throughput hybrid microarray chip constituted by full length viral proteins and the selected sequences defined the so called Immuno-μSARS2 chip. This microarray allowed simultaneous IgG and IgM personalized profiling against the selected panel of epitopes with minimum sample requirements, offering an affordable and easy to implement immunodiagnostic tool. The Immuno-μSARS2 chip was successfully implemented in more than 750 human serum samples, achieving a 98 % of clinical sensitivity and 91 % of clinical specificity respecting to the RT-PCR. To extend the potential use of the chip, the integration of artificial intelligence was combined with sample analysis to define immunological signatures based on IgG response disease severity prediction using samples classified according to COVID-19 clinical outcomes. Our platform was able to discriminate with an 81 % of accuracy patients progressing towards irreversible disease condition (Exitus), followed by 70 % accuracy those patients admitted to ICU and with a discriminate in a 66 % those requiring hospitalization, from asymptomatic disease course. At the same time, a few peptide sequences were found as promising immunodiagnostic elements that could potentially replace the use of full-length proteins to generate affordable tests in the future. The methodology applied over this experimental section can be easily translated into other viruses or diseases expecting to improve diagnostic and prognostic performance.