Use of matrix-assisted laser desorption ionization time-of-flight mass spectrometry analysis of serum peptidome to classify and predict COVID-19 severity

Gomila, Rosa M; Martorell, Gabriel; Fraile-Ribot, Pablo A; Doménech-Sánchez, Antonio; Albertí, Miguel; Oliver, Antonio; García-Gasalla, Mercedes; Albertí, Sebastián

BACKGROUND: Classification and early detection of severe COVID-19 patients is required to establish an effective treatment. We tested the utility of matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to classify and predict the severity of COVID-19. METHODS: We used MALDI-TOF MS to analyse the serum peptidome from 72 COVID-19 patients (training cohort), clinically classified as mild (28), severe (23) and critical (21), and 20 healthy controls. The resulting matrix of peak intensities was used for Machine Learning (ML) approaches to classify and predict COVID-19 severity of 22 independent patients (validation cohort). Finally, we analysed all sera by liquid chromatography mass spectrometry (LC MS/MS) to identify the most relevant proteins associated to disease severity. RESULTS: We found a clear variability of the serum peptidome profile depending on COVID-19 severity. Forty-two peaks exhibited a log fold change ≥ 1 and 17 were significantly different and at least four-fold more intense in the set of critical patients than in the mild ones. ML approach classified clinical stable patients according to their severity with a 100% of accuracy and predicted correctly the evolution of the non-stable patients in all cases. LC MS/MS identified five proteins that were significantly upregulated in the critical patients. They included the serum amyloid protein A2, which probably yielded the most intense peak detected by MALDI-TOF MS. CONCLUSION: We demonstrated the potential of the MALDI-TOF MS as a bench to bedside technology to aid clinicians in their decisions on COVID-19 patients.