This article was originally published here
Chem Sci. 2022 Feb 14;13(11):3216-3226. doi: 10.1039/d1sc05852e. eCollection 2022 Mar 16.
The current COVID-19 pandemic caused by SARS-CoV-2 highlights the urgent need to develop sensitive diagnostic and prognostic methods. To achieve this, multidimensional sensing of SARS-CoV-2-related parameters, including viral loads, immune response, and inflammatory factors, is crucial. Here, using metal-labeled antibodies as reporting probes, we developed a metal detection-based multiplex assay (MMDA) method as a general multiplex assay strategy for biofluids. This strategy offers extremely high multiplexing capacity (theoretically greater than 100) compared to other reported biofluid assay methods. As a proof of concept, MMDA was used for serological profiling of anti-SARS-CoV-2 antibodies. MMDA has significantly higher sensitivity and specificity than ELISA for detecting anti-SARS-CoV-2 antibodies. By integrating high-dimensional data exploration/visualization (tSNE) tool and machine learning algorithms with in-depth multiplex data analysis, we categorized COVID-19 patients into different subgroups based on their distinct antibody landscape. We unbiasedly identified anti-SARS-CoV-2-nucleocapsid IgG and IgA as the most potently induced antibody types for the diagnosis of COVID-19, and anti-SARS-CoV-2-spike IgA as biomarker for disease severity stratification. MMDA represents a more accurate method for the diagnosis and stratification of disease severity of the ongoing COVID-19 pandemic, as well as for the discovery of biomarkers of other diseases.
PMID:35414865 | PMC: PMC8926254 | DOI:10.1039/d1sc05852e