MALDI(+) FT-ICR mass spectrometry (MS) combined with machine learning toward saliva-based diagnostic screening for COVID-19

Resumo

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 Fouriertransform 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 SARSCoV-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.

Descrição

Palavras-chave

COVID-19, MALDI FT-ICR MS, Machine learning, Saliva, SARS-CoV-2, Screening

Citação

ALMEIDA, Camila M. de et al. MALDI(+) FT-ICR mass spectrometry (MS) combined with machine learning toward saliva-based diagnostic screening for COVID-19. Journal of Proteome Research, Washington, v. 21, n. 8, p. 1868-1875, 2022. DOI: 10.1021/acs.jproteome.2c00148. Disponível em: https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00148. Acesso em: 17 maio 2023.