MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19
Camila Medeiros de Almeida, Larissa C. Motta, Gabriely S. Folli, Wena Dantas Marcarini, Camila Alves Costa, Ana Carolina Serafim Vilela, Valério Garrone Baraúna, Francis Martin, Maneesh N. Singh, Luciene Cristina Gastalho Campos, Nádia Lago Costa, Paula Frizera Vassallo, Andréa Rodrigues Chaves, Denise Coutinho Endringer, José Geraldo Mill, Paulo R. Filgueiras, Wanderson Romão
Abstract
Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 μL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.