Online breath analysis with SESI/HRMS for metabolic signatures in children with allergic asthma

Practical Applications of Secondary/Extractive Electrospray Ionization (SESI): A Versatile Tool for Real-Time Chemical Analysis

In the 1980s, researchers discovered the remarkable ability of electrospray plumes to effectively ionize gas-phase molecules via secondary ionization. Around 20 years later-coinciding with the ambient mass spectrometry revolution-secondary electrospray ionization (SESI) and extractive electrospray ionization (EESI) coupled to mass spectrometry were revisited and further developed to analyze complex mixtures of gas and aerosol samples in real-time yet with high sensitivity. During the past two decades, these mass spectrometric techniques have been applied across a broad range of applications, such as the detection of illicit drugs, environmental aerosol analysis, and a series of metabolomic studies through the analysis of volatiles emitted from living organisms. This review offers a comprehensive overview of the progress of SESI and EESI applications since their emergence. Finally, we discuss the opportunities, challenges, along with future directions of SESI and EESI techniques.

A mass spectrometry-based strategy for investigating volatile molecular interactions in microbial consortia: unveiling a Fusarium-specific induction of an antifungal compound

Co-cultivation of microorganisms has emerged as a promising methodology for deciphering the intricate molecular interactions between species. This approach facilitates the replication of natural niches of ecological or clinical relevance where microbes consistently interact. In this context, increasing attention has been addressed toward elucidating the molecular crosstalk within fungal co-cultures. However, a major challenge in this area of research is determining the fungal origin of metabolites induced in co-cultivation systems. Molecules elicited in co-cultures may not be detectable in the individual cultures, making it challenging to establish which microorganism is responsible for their induction. For agar-diffused metabolites, imaging mass spectrometry can help overcome this obstacle by localizing the induced molecules during fungal confrontations. For volatile metabolites, however, this remains an open problem. To address this issue, in this study, a three-head-to-head co-culture strategy was developed, specifically focusing on the exploration of volatile interactions between fungi via headspace solid-phase microextraction combined with gas chromatography mass spectrometry. This methodology was applied to study the volatile molecular interactions of three fungal species: Fusarium culmorum, Aspergillus amstelodami, and Cladosporium cladosporioides. The adopted strategy revealed a Fusarium-specific induction of three volatile molecules: γ-terpinene and two unidentified sesquiterpene compounds. Interestingly, γ-terpinene showed antifungal activity in a bioassay against the other two fungal species: Aspergillus amstelodami and Cladosporium cladosporioides. The proposed methodology could help to investigate volatile molecular interactions and highlight metabolite induction specific to a particular fungus involved in in vitro fungal confrontations. This is relevant for a better understanding of the complex biosynthetic responses of fungi in consortia and for identifying volatile molecules with antifungal activity.

Pharmacometabolomics via real-time breath analysis captures metabotypes of asthmatic children associated with salbutamol responsiveness

Asthma is a widespread respiratory disease affecting millions of children. Salbutamol is a well-established bronchodilator available to treat asthma. However, response to bronchodilators is very heterogeneous, particularly in children. Pharmacometabolomics via exhaled breath analysis holds promise for patient stratification. Here, we integrate a real-time breath analysis platform in the workflow of an outpatient clinic to provide a detailed metabolic snapshot of patients with asthma undergoing standard clinical evaluations. We observed significant metabolic changes associated with salbutamol inhalation within ∼1 h. Our data supports the hypothesis that sphingolipid metabolism and arginine biosynthesis mediate the bronchodilator effect of salbutamol. Clustering analysis of 30 metabolites associated with these pathways revealed characteristic metabotypes related to clinical phenotypes of poor bronchodilator responsiveness. We propose that such a metabolic fingerprinting approach may be of utility in clinical practice to quantify response to inhaled medications or asthma outcomes.

Molecular breath profile of acute COPD exacerbations

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) show high variability in individual susceptibility and promote disease progression; thus, accurate diagnosis and treatment is essential. Unravelling the molecular metabolic changes during AECOPD in breath could promote understanding of AECOPD and its treatment. Our objective was to investigate the metabolic breath profiles during AECOPD for biomarker detection. We conducted real-time breath analysis in patients with COPD during AECOPD and during subsequent stable phase. Molecular breath patterns were compared between AECOPD and stable phase by dimension reduction techniques and paired t-tests. Pathway enrichment analyses were performed to investigate underlying metabolic pathways. Partial least-squares discriminant analysis and XGboost were utilised to build a prediction model to differentiate AECOPD from stable state. 35 patients (60% male) with a mean age of 65 (10.2) yr with AECOPD were included. AECOPD could be predicted with a high sensitivity of 82.5% (95% confidence interval of 68.8%-93.8%) and an excellent discriminative power (AUC = 0.86). Metabolic changes in the linoleate, tyrosine, and tryptophan pathways during AECOPD were predominant. Significant metabolic changes occur during COPD exacerbations, predominantly in the linoleate, tyrosine, and tryptophan pathways, which are all linked to inflammation. Real-time exhaled breath analysis enables a good prediction of AECOPD compared to stable state and thus could enhance precision of AECOPD diagnosis and efficacy in clinical practice.

A matrix-centered view of mass spectrometry platform innovation for volatilome research

Volatile organic compounds (VOCs) are carbon-containing molecules with high vapor pressure and low water solubility that are released from biotic and abiotic matrices. Because they are in the gaseous phase, these compounds tend to remain undetected when using conventional metabolomic profiling methods. Despite this omission, efforts to profile VOCs can provide useful information related to metabolic status and identify potential signaling pathways or toxicological impacts in natural or engineered environments. Over the past several decades mass spectrometry (MS) platform innovation has instigated new opportunities for VOC detection from previously intractable matrices. In parallel, volatilome research linking VOC profiles to other forms of multi-omic information (DNA, RNA, protein, and other metabolites) has gained prominence in resolving genotype/phenotype relationships at different levels of biological organization. This review explores both on-line and off-line methods used in VOC profiling with MS from different matrices. On-line methods involve direct sample injection into the MS platform without any prior compound separation, while off-line methods involve chromatographic separation prior to sample injection and analyte detection. Attention is given to the technical evolution of platforms needed for increasingly resolved VOC profiles, tracing technical progress over time with particular emphasis on emerging microbiome and diagnostic applications.

Advancing Breathomics through Accurate Discrimination of Endogenous from Exogenous Volatiles in Breath

Breathomics, a growing field in exposure monitoring and clinical diagnostics, has faced accuracy challenges due to unclear contributing factors. This study aims to enhance the potential of breathomics in various frontiers by categorizing exhaled volatile organic compounds (VOCs) as endogenous or exogenous. Analyzing ambient air and breath samples from 271 volunteers via TD-GC × GC-TOF MS/FID, we identify and quantify 50 common VOCs in exhaled breath. Advanced quantitative structure-property relationships and compartment models are employed to obtain VOCs kinetic parameters. This in-depth approach allows us to accurately determine the alveolar concentration of VOCs and further discern their origins, facilitating personalized application of breathomics in exposure assessment and disease diagnosis. Our findings demonstrate that prolonged external exposure turns humans into secondary pollutant sources. Analysis of endogenous VOCs reveals that internal exposure poses more significant health risks than external. Moreover, by correcting environmental backgrounds, we improve the accuracy of gastrointestinal disease diagnostic models by 15-25%. This advancement in identifying VOC origins via compartmental models promises to elevate the clinical relevance of breathomics, marking a leap forward in exposure assessment and precision medicine.

BreathXplorer: Processing Online Breathomics Data Generated from Direct Analysis Using High-Resolution Mass Spectrometry

Nontargeted breath analysis in real time using high-resolution mass spectrometry (HRMS) is a promising approach for high coverage profiling of metabolites in human exhaled breath. However, the information-rich and unique non-Gaussian metabolic signal shapes of real-time HRMS-based data pose a significant challenge for efficient data processing. This work takes a typical real-time HRMS technique as an example, i.e. secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), and presents BreathXplorer, an open-source Python package designed for the processing of real-time exhaled breath data comprising multiple exhalations. BreathXplorer is composed of four main modules. The first module applies either a topological algorithm or a Gaussian mixture model (GMM) to determine the start and end points of each exhalation. Next, density-based spatial clustering of applications with noise (DBSCAN) is employed to cluster m/z values belonging to the same metabolic feature, followed by applying an intensity relative standard deviation (RSD) filter to extract real breath metabolic features. BreathXplorer also offers functions of (1) feature alignment across the samples and (2) associating MS/MS spectra with their corresponding metabolic features for downstream compound annotation. Manual inspection of the metabolic features extracted from SESI-HRMS breath data suggests that BreathXplorer can achieve 100% accuracy in identifying the start and end points of each exhalation and acquire accurate quantitative measurements of each breath feature. In a proof-of-concept study on exercise breathomics using SESI-HRMS, BreathXplorer successfully reveals the significantly changed metabolites that are pertinent to exercise. BreathXplorer is publicly available on GitHub (https://github.com/HuanLab/breathXplorer). It provides a powerful and convenient-to-use tool for the researchers to process breathomics data obtained by directly analysis using HRMS.

Internal Standard Addition System for Online Breath Analysis

Breath analysis with secondary electrospray ionization (SESI) coupled to mass spectrometry (MS) is a sensitive method for breath metabolomics. To enable quantitative assessments using SESI-MS, a system was developed to introduce controlled amounts of gases into breath samples and carry out standard addition experiments. The system combines gas standard generation through controlled evaporation, humidification, breath dilution, and standard injection with the help of mass-flow controllers. The system can also dilute breath, which affects the signal of the detected components. This response can be used to filter out contaminating compounds in an untargeted metabolomics workflow. The system's quantitative capabilities have been shown through standard addition of pyridine and butyric acid into breath in real time. This system can improve the quality and robustness of breath data.

Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform

BACKGROUND

The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review.

AIM OF REVIEW

This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.

Alternative electrolyte solutions for untargeted breath metabolomics using secondary-electrospray ionization high-resolution mass spectrometry

RATIONALE

Secondary-electrospray ionization (SESI) coupled with high-resolution mass spectrometry is a powerful tool for the discovery of biomarkers in exhaled breath. A primary electrospray consisting of aqueous formic acid (FA) is currently used to charge the volatile organic compounds in breath. To investigate whether alternate electrospray compositions could enable different metabolite coverage and sensitivities, the electrospray dopants NaI and AgNO3 were tested.

METHODS

In a proof-of-principle manner, the exhaled breath of one subject was analyzed repeatedly with different electrospray solutions and with the help of a spectral stitching technique. Capillary diameter and position were optimized to achieve proper detection of exhaled breath. The detected features were then compared using formula annotation. Using an evaporation-based gas standard system, the signal response of the different solutions was probed.

RESULTS

Principal component analysis revealed a substantial difference in features detected with AgNO3 . With silver, more sulfur-containing features and more unsaturated hydrocarbon compounds were detected. Furthermore, more primary amines were potentially ionized, as indicated by van Krewelen diagrams. In total, twice as many features were unique to AgNO3 than for other electrospray dopants. Using gas standards at known concentrations, the high sensitivity of FA as a dopant was demonstrated but also indicated alternate sensitivities of the other electrospray solutions.

CONCLUSIONS

This work demonstrated the potential of AgNO3 as a complementary dopant for further biomarker discovery in SESI-based breath analysis.

Optimization of volatile organic compounds sampling from dairy cow exhaled breath using polymer-based solid-phase extraction cartridges for gas chromatographic analysis

We explored appropriate technical setups for the detection of volatile organic compounds (VOCs) from exhaled cow breath by comparing six different polymer-based solid-phase extraction (SPE) cartridges currently on the market for gas chromatography/mass spectrometry (GC-MS) screening. Exhaled breath was sampled at a single timepoint from five lactating dairy cows using six different SPE cartridges (Bond Elut ENV (ENV); Chromabond HRX (HRX); Chromabond HRP (HRP); Chromabond HLB (HLB); Chromabond HR-XCW (XCW) and Chromabond HR-XAW (XAW)). The trapped VOCs were analyzed by dynamic headspace vacuum in-tube extraction GC-MS (DHS-V-ITEX-GC-MS). Depending on the SPE cartridge, we detected 1174-1312 VOCs per cartridge. Most VOCs were alkenes, alkanes, esters, ketones, alcohols, aldehydes, amines, nitriles, ethers, amides, carboxylic acids, alkynes, azoles, terpenes, pyridines, or sulfur-containing compounds. The six SPE cartridges differed in their specificity for the chemical compounds, with the XAW cartridge showing the best specificity for ketones. The greatest differences between the tested SPE cartridges appeared in the detection of specific VOCs. In total, 176 different VOCs were detected with a match factor >80%. The greatest number of specific VOCs was captured by XAW (149), followed by ENV (118), HLB (117), HRP (115), HRX (114), and XCW (114). We conclude that the tested SPE cartridges are suitable for VOC sampling from exhaled cow breath, but the SPE cartridge choice enormously affects the detected chemical groups and the number of detected VOCs. Therefore, an appropriate SPE adsorbent cartridge should be selected according to our proposed inclusion criteria. For targeted metabolomics approaches, the SPE cartridge choice depends on the VOCs or chemical compound groups of interest based on our provided VOC list. For untargeted approaches without information on the animals' metabolic condition, we suggest using multi-sorbent SPE cartridges or multiple cartridges per animal.