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

Meritxell Deulofeu,José Elías Conde,Esteban García-Cuesta,María Teresa Serrando,Enrique Verdú,Orlando Jiménez-Romero,Victoria Salvadó,Pere Boadas-Vaello,Eladia María Peña-Méndez

doi:10.3389/fmed.2021.661358

Abstract

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.

Highlights

The COVID-19 pandemic represents a major health crisis, but has had unprecedented economic repercussions
Taking into consideration how rapidly the COVID-19 pandemic has spread, often through the transmission of SARS-CoV-2 by asymptomatic individuals [3], fast and economic diagnostic tools are essential for the control of this devastating pandemic
Different experiments were designed to test the ability of MALDI-TOF MS to extract these spectral patterns from the data obtained from human nasopharyngeal (NP) and enable it to detect patients infected by SARS-CoV-2

Summary

INTRODUCTION

The COVID-19 pandemic represents a major health crisis, but has had unprecedented economic repercussions. PCR-based strategies are costly, require a lot of technical personnel and laboratories, and the analysis time is relatively long, limiting the number of samples that can be processed daily Some of these problems can be overcome by using saliva for COVID-19 diagnosis and a dual RT-qPCR test, the time needed to obtain results remains high [9, 10]. The large amount of data obtained using MS spectra fingerprints requires the combination of powerful statistical strategies [13] and artificial intelligence methods in order to be able to identify the diagnostic pattern These strategies have been successfully applied in medicine and biomedicine [15, 16]. In light of the above, the present work has aimed to develop a new methodology based on MALDI-TOF MS analyses of nasopharyngeal samples coupled to methods of artificial intelligence, allowing COVID-19 to be identified. The application of a machine learning (ML) approach to the fingerprint mass spectra of positive and negative samples of SARS-CoV-2 infection will allow the development of a fast and efficient approach to support clinical decisions

MATERIALS AND METHODS

RESULTS AND DISCUSSION

ETHICS STATEMENT