Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19
Lucas C. Lázari, Fábio Ghilardi, Lívia Rosa-Fernandes, Diego M. Assis, José Carlos Nicolau, Verônica Feijoli Santiago, Talia Falcão Dalçóquio, Cláudia Blanes Angeli, Adriadne Justi Bertolin, Cláudio Romero Farias Marinho, Carsten Wrenger, Edison Luíz Durigon, Rinaldo Focaccia Siciliano, Giuseppe Palmisano
Abstract
SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.