Immunodetection of Lung IgG and IgM Antibodies against SARS-CoV-2 via Enzymatic Liquefaction of Respiratory Samples from COVID-19 Patients

Lung-secreted IgG and IgM antibodies are valuable biomarkers for monitoring the local immune response against respiratory infections. These biomarkers are found in lower airway secretions that need to be liquefied prior to analysis. Traditional methods for sample liquefaction rely on reducing disulfide bonds, which may damage the structure of the biomarkers and hamper their immunodetection. Here, we propose an alternative enzymatic method that uses O-2 bubbles generated by endogenous catalase enzymes in order to liquefy respiratory samples. The proposed method is more efficient for liquefying medium- and high-viscosity samples and does not fragment the antibodies. This prevents damage to antigen recognition domains and recognition sites for secondary antibodies that can decrease the signal of immunodetection techniques. The suitability of the enzymatic method for detecting antibodies in respiratory samples is demonstrated by detecting anti-SARS-CoV-2 IgG and IgM to viral N-protein with gold standard ELISA in bronchial aspirate specimens from a multicenter cohort of 44 COVID-19 patients. The enzymatic detection sharply increases the sensitivity toward IgG and IgM detection compared to the traditional approach based on liquefying samples with dithiothreitol. This improved performance could reveal new mechanisms of the early local immune response against respiratory infections that may have gone unnoticed with current sample treatment methods.

Automated summary

This study introduces an enzymatic method that uses O-2 bubbles generated by endogenous catalase enzymes to liquefy respiratory samples more efficiently, without damaging antibody structures. By detecting antibodies in bronchial aspirate specimens from COVID-19 patients, it was found that the enzymatic detection significantly improves the sensitivity for IgG and IgM detection compared to traditional methods.