Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence

14 Apr, 2021

The high infectivity of SARS-CoV-2 makes it essential to develop a rapid and accurate diagnostic test so that carriers can be isolated at an early stage. Viral RNA in nasopharyngeal samples by RT-PCR is currently considered the reference method although it is not recognized as a strong gold standard due to certain drawbacks. Here we develop a methodology combining the analysis of from human nasopharyngeal (NP) samples by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with the use of machine learning (ML). A total of 236 NP samples collected in two different viral transport media were analyzed with minimal sample preparation and the subsequent mass spectra data was used to build different ML models with two different techniques. The best model showed high performance in terms of accuracy, sensitivity and specificity, in all cases reaching values higher than 90%. Our results suggest that the analysis of NP samples by MALDI-TOF MS and ML is a simple, safe, fast and economic diagnostic test for COVID-19.


The COVID-19 pandemic not only represents a major health crisis, but has also had unprecedented economic repercussions. According to the most recent available report of the World Health Organization, the cumulative number of reported cases of SARS-CoV-2 infections worldwide has now reached over 98 million people and over 2 million people have died of the disease since the start of the COVID-19 pandemic in December 2019. Moreover, it has been estimated that the gross domestic product may drop by more than 10-15% in some countries. Taking into consideration how rapidly the COVID-19 pandemic has spread, often through the transmission of SARS-CoV-2 by asymptomatic individuals, fast and economic diagnostic tools are essential for the control of this devastating pandemic.

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