Detection of SARS-CoV-2 in nasal swabs using MALDI-MS.
Nat Biotechnol
; 38(10): 1168-1173, 2020 10.
Article
in English
| MEDLINE | ID: covidwho-690889
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Coronavirus Infections
/
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
/
Clinical Laboratory Techniques
/
Betacoronavirus
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Nat Biotechnol
Journal subject:
Biotechnology
Year:
2020
Document Type:
Article
Affiliation country:
S41587-020-0644-7
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