Kaeslin Jérôme, Micic Srdjan, Weber Ronja, Müller Simona, Perkins Nathan, Berger Christoph, Zenobi Renato, Bruderer Tobias, Moeller Alexander
Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology, Vladimir-Prelog Weg 1-5/10, 8093 Zurich, Switzerland.
Division of Respiratory Medicine and Childhood Research Center, University Children's Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland.
Metabolites. 2021 Nov 11;11(11):773. doi: 10.3390/metabo11110773.
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 , , , , and 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.
基于细菌释放的挥发性有机化合物(VOCs)来识别和区分细菌,为快速诊断带来了广阔机遇。二次电喷雾电离高分辨率质谱(SESI-HRMS)是发现VOC生物标志物的理想技术,因其速度快、对极性分子敏感且具备化合物表征能力。本文描述了一种体外SESI-HRMS工作流程,用于寻找与囊性纤维化(CF)相关病原体、、、、和的生物标志物。在前三个主成分中,可从180个顶空样品中区分出这六种病原体,使用留一法交叉验证的支持向量机算法进行预测分析,在区分不同组时显示出完美的准确率得分。此外,通过递归特征消除发现了94个独特特征,并通过SESI-MS/MS进一步表征,从而得到33个推定鉴定的生物标志物。总之,这六种病原体可根据其VOC谱以及本文报道的推定生物标志物在体外进行区分。未来,这些推定的生物标志物可能有助于基于CF患者的呼吸样本在体内检测病原体。