Ronja Weber

Environmental volatile organic compounds (VOCs) from the ambient air potentially influence on-line breath analysis measurements by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS). The aim of this study was to investigate how inhaling through a VOC filter affects the detected breath profiles and whether it is feasible to integrate such filters into routine measurements. A total of 24 adult participants performed paired breath analysis measurements with and without the use of an activated carbon filter for inspiration. Concordance correlation coefficients (CCCs) and the Bland–Altman analysis were used to assess the agreement between the two methods. Additionally, the effect on a selection of known metabolites and contaminants was analyzed. Out of all the detected features, 78.3% showed at least a moderate agreement before and after filter usage (CCC > 0.9). The decrease in agreement of the remaining m/z features was mostly associated with reduced signal intensities after filter usage. Although a moderate-to-substantial concordance was found for almost 80% of the m/z features, the filter still had an effect by decreasing signal intensities, not only for contaminants, but also for some of the studied metabolites. Operationally, the use of the filter complicated and slowed down the conductance of measurements, limiting its applicability in clinical studies.

The early detection of inflammation and infection is important to prevent irreversible lung damage in cystic fibrosis. Novel and non-invasive monitoring tools would be of high benefit for the quality of life of patients. Our group previously detected over 100 exhaled mass-to-charge (m/z) features, using on-line secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), which distinguish children with cystic fibrosis from healthy controls. The aim of this study was to annotate as many m/z features as possible with putative chemical structures. Compound identification was performed by applying a rigorous workflow, which included the analysis of on-line MS² spectra and a literature comparison. A total of 49 discriminatory exhaled compounds were putatively identified. A group of compounds including glycolic acid, glyceric acid and xanthine were elevated in the cystic fibrosis group. A large group of acylcarnitines and aldehydes were found to be decreased in cystic fibrosis. The proposed compound identification workflow was used to identify signatures of volatile organic compounds that discriminate children with cystic fibrosis from healthy controls, which is the first step for future non-invasive and personalized applications.

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.

The diagnosis of asthma in children is still a clinical challenge. Breath-analysis has the potential to overcome this challenge. Our goal is to show that secondary electrospray ionization high-resolution mass-spectrometry (SESI-HRMS) can be used to detect asthma-specific metabolites in exhaled breath. We are conducting an exploratory observational study comparing the molecular composition of exhaled breath from school children (5-18 years) with allergic asthma (confirmed by objective tests) to healthy controls. Patients are taken off their asthma medication two weeks prior to breath measurements. Breath analysis is performed on an AB SCIEX TripleTOF 5600+ HRMS coupled to a Super SESI ion source, detecting m/z features between 50 and 500 Da (mass accuracy <1ppm). A combination of data extraction and machine learning models is used to isolate the most discriminative features and assess the predictive power of breath profiles. We acquired data from 47 children (21 with allergic asthma, 26 healthy controls). In our preliminary data analysis, we identified 193 m/z features which differed significantly between the two groups (adjusted p value < 0.05), which showed an average predictive power (asthma vs. healthy) of 78.7% (leave-one-out cross-validation with Random Forests algorithm). We could allocate molecular formulas to most significant m/z peaks. Compound identification is currently ongoing but some of the compounds have previously been reported in a biological context. For the first time we identified exhaled molecules that differ between children with allergic asthma and healthy controls by real-time SESI-HRMS. Such a discovery has the potential to improve the early diagnosis of asthma.