Differentiation of Cystic Fibrosis-Related Pathogens by Volatile Organic Compound Analysis with Secondary Electrospray Ionization Mass Spectrometry.

Nathan Perkins,Simona Müller,Renato Zenobi,Ronja Weber,Srdjan Micic,Jérôme Kaeslin,Tobias Bruderer,Christoph Berger,Alexander Moeller

doi:10.3390/metabo11110773

Abstract

Identifying and differentiating bacteria based on their emitted volatile organic compounds (VOCs) opens vast opportunities for rapid diagnostics. Secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) is an ideal technique for VOC-biomarker discovery because of its speed, sensitivity towards polar molecules and compound characterization possibilities. Here, an in vitro SESI-HRMS workflow to find biomarkers for cystic fibrosis (CF)-related pathogens P. aeruginosa, S. pneumoniae, S. aureus, H. influenzae, E. coli and S. maltophilia is described. From 180 headspace samples, the six pathogens are distinguishable in the first three principal components and predictive analysis with a support vector machine algorithm using leave-one-out cross-validation exhibited perfect accuracy scores for the differentiation between the groups. Additionally, 94 distinctive features were found by recursive feature elimination and further characterized by SESI-MS/MS, which yielded 33 putatively identified biomarkers. In conclusion, the six pathogens can be distinguished in vitro based on their VOC profiles as well as the herein reported putative biomarkers. In the future, these putative biomarkers might be helpful for pathogen detection in vivo based on breath samples from patients with CF.

Highlights

If some volatile organic compounds (VOCs) are characteristic for one particular bacterium, they can be used as markers to indicate the presence of the species in the sample under investigation, e.g., in vitro cultures, blood, urine, saliva, sputum or breath
This study has demonstrated that the full VOC profiles of the six pathogens allowed a qualitative differentiation of bacterial species by simple principal components analysis (PCA)
We have demonstrated by principal component analysis and with supervised machine learning that the six investigated CFassociated pathogens can be differentiated by secondary electrospray ionization (SESI)-high resolution MS (HRMS)

Summary

Introduction

Confirming the presence and identity of pathogenic bacteria is of key importance for the diagnosis of bacterial infections. Classical diagnostic methods are slow because they involve time-consuming steps such as cultivation followed by biochemical, serological or genetic analysis [1]. Bacteria can be identified and specified indirectly by detecting emitted metabolic volatile organic compounds (VOCs) [2]. If some VOCs are characteristic for one particular bacterium, they can be used as markers to indicate the presence of the species in the sample under investigation, e.g., in vitro cultures, blood, urine, saliva, sputum or breath. Using more rapid diagnostic methods to detect pathogen-specific biomarkers allows more timely treatment decisions, allows to monitor the progression of a treatment and might be less invasive [3,4,5]

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