Detection of SARS-CoV-2 in nasal swabs using MALDI-MS

• Published in: Nature biotechnology Vol. 38; no. 10; pp. 1168 - 1173
• Main Authors: Nachtigall, Fabiane M., Pereira, Alfredo, Trofymchuk, Oleksandra S., Santos, Leonardo S.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.10.2020; Nature Publishing Group
• Subjects: 639/638/11/296; 639/638/11/876; Accuracy; Agriculture; Algorithms; Analysis; Betacoronavirus - isolation & purification; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Chile; Clinical Laboratory Techniques - methods; Clinical Laboratory Techniques - statistics & numerical data; Coronavirus Infections - diagnosis; Coronavirus Infections - epidemiology; Coronavirus Infections - virology; COVID-19; COVID-19 Testing; Developing Countries; Epidemics; False Negative Reactions; False Positive Reactions; Health aspects; Humans; Ionization; Ions; Laboratories; LDCs; Learning algorithms; Letter; Life Sciences; Machine Learning; Mass spectra; Mass spectrometry; Mass spectroscopy; Medical laboratories; Model accuracy; Nasal Mucosa - virology; Pandemics; Pneumonia, Viral - diagnosis; Pneumonia, Viral - epidemiology; Pneumonia, Viral - virology; Sample preparation; SARS-CoV-2; Scientific imaging; Sensitivity and Specificity; Severe acute respiratory syndrome coronavirus 2; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - statistics & numerical data; Spectroscopy; Support Vector Machine; Support vector machines; Taiwan; Chile; Taiwan
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/s41587-020-0644-7

Detection of SARS-CoV-2 using RT–PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT–PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples. SARS-CoV-2 is reliably detected in nasal swab samples using mass spectrometry and machine learning analysis.