MALDI‐TOF MS analysis of nasal swabs for the characterization of patients infected with SARS‐CoV‐2 Omicron

Abstract

Abstract With the ongoing mutation of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) leading to various variants, there is an urgent need for new diagnostic methods for SARS‐CoV‐2 infection. The existing nucleic acid test and antigen test suffer from long assay time and low sensitivity, respectively. Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS)‐based nasal swabs analysis have been demonstrated as a promising technique in SARS‐CoV‐2 infection screening. However, the applicability of the technique in the different variants of SARS‐CoV‐2 is uncertain. Given the prevalence of the Omicron variant since 2022, we developed a MALDI‐TOF‐based diagnosis method with nasal swab samples to detect the infection by this variant. We collected 325 SARS‐CoV‐2‐positive and 221 SARS‐CoV‐2‐negative nasal swab samples, and the molecular mass fingerprints were acquired from the samples by MALDI‐TOF MS. Using a random forest machine learning classification model to analyze the molecular mass fingerprints MALDI‐TOF mass spectra, the accuracy of 97%, false negative rate of 0%, and false positive rate of 7.6% were achieved for the diagnosis of SARS‐CoV‐2 infection. Combining the MALDI‐TOF analysis with top‐down proteomics, we identified four potential protein biomarkers, that is, humanin‐like 4, thymosin beta‐10, thymosin beta‐4 and statherin, in the nasal swab for the diagnosis of coronavirus disease 2019. It was further found that the four protein biomarkers can also differentiate the SARS‐CoV‐2 original strains infection and Omicron strains infection. These results suggest that the MALDI‐TOF MS‐based nasal swab analysis holds effective diagnostic capabilities of SARS‐CoV‐2 infection, and shows promising potential for global application and extension to other infectious diseases.

Keywords

• diagnosis
• machine learning
• MALDI‐TOF
• SARS‐CoV‐2