Breath Metabolites Research Articles

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.

Baseline correction for the infrared spectra of exhaled breath

Infrared spectroscopy appears to be a promising analytical method for the metabolic analysis of breath. However, due to the presence of trace amounts in exhaled breath, the absorption strength of the metabolites remains extremely low. In such low detection limits, the nonlinear detection sensitivity of the infrared detector and electronic noise strongly modify the baseline of the acquired infrared spectra of breath. Fitting the reference molecular spectra with the baseline-modified spectral features of breath metabolites does not provide accurate identification. Therefore, baseline correction of the acquired infrared spectra of breath is the primary requirement for the success of breath-based infrared diagnosis. A selective spectral region-based, simple baseline correction method is proposed for the infrared spectroscopy of breath.

Exhalation metabolomics: A new force in revealing the impact of ozone pollution on respiratory health

Near-surface ozone pollution poses a significant threat to respiratory health, with the lung micro-environment playing a crucial role in its effects. This article highlights the potential of exhalation metabolomics as a novel approach to investigate the respiratory health impacts of ozone pollution. By analyzing exhaled breath metabolites, we can gain insights into the biological responses triggered by ozone exposure. This strategy offers a non-invasive, real-time monitoring tool to assess individual susceptibility and health outcomes. Furthermore, exhalation metabolomics has the potential to inform the development of concentration limits for ozone pollution control, providing a scientific basis for effective policy-making and public health interventions.

The potential of volatile organic compounds to diagnose primary sclerosing cholangitis

IntroductionPrimary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by progressive inflammation and fibrosis of the bile ducts. PSC is a complex disease of largely unknown aetiology with a strong association with inflammatory bowel disease (IBD). Diagnosis, especially at early stage, is difficult and to date there is no diagnostic biomarker. The present study aimed to assess the diagnostic potential of Volatile Organic Compounds (VOCs) in exhaled breath to detect (early) PSC in an IBD population. MethodsBreath samples were obtained from 16 PSC, 47 PSC with IBD, and 53 IBD patients during outpatient clinic visit. Breath sampling was performed using the ReCIVA breath sampler and subsequently analysed by gas chromatography mass spectrometry. Random Forest modelling was performed to find discriminatory VOCs and create a predictive model that was tested using an independent test set. ResultsThe final model to discriminate PSC patients with or without IBD versus IBD only patients included twenty VOCs and achieved a sensitivity, specificity, and area under the receiver operating curve on the test set of 77%, 83%, and 0.84 respectively. Three VOCs (isoprene, 2-octanone and undecane) together correlated significantly to the Amsterdam-Oxford score for PSC disease prognosis. A sensitivity analysis showed stable results across early stage PSC including those with normal alkaline phosphatase levels, as well as further progressed PSC. ConclusionThe present study demonstrates that exhaled breath can distinguish PSC cases from IBD and has potential as a non-invasive clinical breath test for (early) PSC. Impact and ImplicationsPrimary Sclerosing Cholangitis (PSC) is a complex chronic liver disease, which ultimately results in cirrhosis, liver failure, and death. Detection especially in early disease can be challenging, and therefore therapy typically starts when there is already some irreversible damage. The current study shows that metabolites in exhaled breath, so called Volatile Organic Compounds, hold promise to non-invasively detect PSC also at early stages.

Breath Gas Markers in Depression and Their Relationship with Brain Metabolism

IntroductionDysfunctional changes in the glutamatergic system play an important role in the pathophysiology of depression. Glutamate regulates various neuronal function, such as nerve migration, excitability, plasticity, as well as long-term potentiation and long-term synaptic depression. Failures in this process might cause emotional/cognitive changes associated with stress-induced depressive symptoms, a part of our current understanding of the pathophysiology of depression. These changes might be related to deviations in biochemical blood parameters, but also to volatile organic compounds (VOCs) measured in breath.Objectives1) To replicate our previous finding that concentration of volatile organic compounds in expiratory breath gas and metabolites derived from MR spectroscopy distinguish unmedicated depressed patients from healthy participants, (2) to determine whether the amount of these VOCs is associated with severity of depression and anxiety, and (3) to correlate breath-VOC-content with glutamatergic neurotransmission and energy metabolism derived from MR spectroscopy.Methods25 antidepressant-free patients with major depression according to DSM V (18-65 years of age) are recruited from our out- and inpatient clinics. The controls will consist of 25 healthy age-and-sex-matched participants. Breath gas analyses will be carried out at awakening, and 30 and 60 minutes thereafter, and at 5pm using PTR-TOF-MS with direct on time measurement through a special sampler. A 7 Tesla Siemens Terra MRI scanner will be used to undertake spectroscopic measurements. Concentrations of glutamate and β-hydroxybutyrate levels in the pregenual and dorsal anterior cingulate gyrus will subsequently be assessed.ResultsStatistical analysis for differences between groups corrected for multiple measurements will be carried out. Concentration of VOCs will be correlated with brain metabolism and severity of symptoms.ConclusionsVOCs in breath are proposed to be an efficient and non-invasive marker for depression-related biochemical changes related to disease severity, and eventually useful for personalized treatment planning.Disclosure of InterestNone Declared

Breath metabolomics for diagnosis of acute respiratory distress syndrome

BackgroundAcute respiratory distress syndrome (ARDS) poses challenges in early identification. Exhaled breath contains metabolites reflective of pulmonary inflammation.AimTo evaluate the diagnostic accuracy of breath metabolites for ARDS in invasively ventilated intensive care unit (ICU) patients.MethodsThis two-center observational study included critically ill patients receiving invasive ventilation. Gas chromatography and mass spectrometry (GC–MS) was used to quantify the exhaled metabolites. The Berlin definition of ARDS was assessed by three experts to categorize all patients into “certain ARDS”, “certain no ARDS” and “uncertain ARDS” groups. The patients with “certain” labels from one hospital formed the derivation cohort used to train a classifier built based on the five most significant breath metabolites. The diagnostic accuracy of the classifier was assessed in all patients from the second hospital and combined with the lung injury prediction score (LIPS).ResultsA total of 499 patients were included in this study. Three hundred fifty-seven patients were included in the derivation cohort (60 with certain ARDS; 17%), and 142 patients in the validation cohort (47 with certain ARDS; 33%). The metabolites 1-methylpyrrole, 1,3,5-trifluorobenzene, methoxyacetic acid, 2-methylfuran and 2-methyl-1-propanol were included in the classifier. The classifier had an area under the receiver operating characteristics curve (AUROCC) of 0.71 (CI 0.63–0.78) in the derivation cohort and 0.63 (CI 0.52–0.74) in the validation cohort. Combining the breath test with the LIPS does not significantly enhance the diagnostic performance.ConclusionAn exhaled breath metabolomics-based classifier has moderate diagnostic accuracy for ARDS but was not sufficiently accurate for clinical use, even after combination with a clinical prediction score.

Real-time breath analysis towards a healthy human breath profile.

The direct analysis of molecules contained within human breath has had significant implications for clinical and diagnostic applications in recent decades. However, attempts to compare one study to another or to reproduce previous work are hampered by: variability between sampling methodologies, human phenotypic variability, complex interactions between compounds within breath, and confounding signals from comorbidities. Towards this end, we have endeavored to create an averaged healthy human 'profile' against which follow-on studies might be compared. Through the use of direct secondary electrospray ionization combined with a high-resolution mass spectrometry and in-house bioinformatics pipeline, we seek to curate an average healthy human profile for breath and use this model to distinguish differences inter- and intra-day for human volunteers. Breath samples were significantly different in PERMANOVA analysis and ANOSIM analysis based on Time of Day, Participant ID, Date of Sample, Sex of Participant, and Age of Participant (p< 0.001). Optimal binning analysis identify strong associations between specific features and variables. These include 227 breath features identified as unique identifiers for 28 of the 31 participants. Four signals were identified to be strongly associated with female participants and one with male participants. A total of 37 signals were identified to be strongly associated with the time-of-day samples were taken. Threshold indicator taxa analysis indicated a shift in significant breath features across the age gradient of participants with peak disruption of breath metabolites occurring at around age 32. Forty-eight features were identified after filtering from which a healthy human breath profile for all participants was created.

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data

BackgroundBreath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported.ObjectiveThe objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm.MethodsWe conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard.ResultsWe enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84–0.91), the summary specificity was 1.00 (95% CI 0.85–1.00), the AUC was 0.96 (95% CI 0.94–0.98), the pAUC was 0.92 (95% CI 0.89–0.96), and the DOR was 207.62 (95% CI 24.62–924.64).ConclusionElectronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.

Analysis of a Broad Range of Carbonyl Metabolites in Exhaled Breath by UHPLC-MS.

Analysis of volatile organic compounds (VOCs) in exhaled breath (EB) has shown great potential for disease detection including lung cancer, infectious respiratory diseases, and chronic obstructive pulmonary disease. Although many breath sample collection and analytical methods have been developed for breath analysis, analysis of metabolic VOCs in exhaled breath is still a challenge for clinical application. Many carbonyl compounds in exhaled breath are related to the metabolic processes of diseases. This work reports a method of ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-MS) for the analysis of a broad range of carbonyl metabolites in exhaled breath. Carbonyl compounds in the exhaled breath were captured by a fabricated silicon microreactor with a micropillar array coated with 2-(aminooxy)ethyl-N,N,N-trimethylammonium (ATM) triflate. A total of six subgroups consisting of saturated aldehydes and ketones, hydroxy-aldehydes, and hydroxy-ketones, unsaturated 2-alkenals, and 4-hydroxy-2-alkenals were identified in the exhaled breath. The combination of a silicon microreactor for the selective capture of carbonyl compounds with UHPLC-MS analysis may provide a quantitative method for the analysis of carbonyls to identify disease markers in exhaled breath.

Non-Invasive Lung Cancer Diagnostics through Metabolites in Exhaled Breath: Influence of the Disease Variability and Comorbidities

Non-invasive, simple, and fast tests for lung cancer diagnostics are one of the urgent needs for clinical practice. The work describes the results of exhaled breath analysis of 112 lung cancer patients and 120 healthy individuals using gas chromatography-mass spectrometry (GC-MS). Volatile organic compound (VOC) peak areas and their ratios were considered for data analysis. VOC profiles of patients with various histological types, tumor localization, TNM stage, and treatment status were considered. The effect of non-pulmonary comorbidities (chronic heart failure, hypertension, anemia, acute cerebrovascular accident, obesity, diabetes) on exhaled breath composition of lung cancer patients was studied for the first time. Significant correlations between some VOC peak areas and their ratios and these factors were found. Diagnostic models were created using gradient boosted decision trees (GBDT) and artificial neural network (ANN). The performance of developed models was compared. ANN model was the most accurate: 82-88% sensitivity and 80-86% specificity on the test data.

Breathomics profiling of metabolic pathways affected by major depression: Possibilities and limitations.

Major depressive disorder (MDD) is one of the most common psychiatric disorders with multifactorial etiologies. Metabolomics has recently emerged as a particularly potential quantitative tool that provides a multi-parametric signature specific to several mechanisms underlying the heterogeneous pathophysiology of MDD. The main purpose of the present study was to investigate possibilities and limitations of breath-based metabolomics, breathomics patterns to discriminate MDD patients from healthy controls (HCs) and identify the altered metabolic pathways in MDD. Breath samples were collected in Tedlar bags at awakening, 30 and 60 min after awakening from 26 patients with MDD and 25 HCs. The non-targeted breathomics analysis was carried out by proton transfer reaction mass spectrometry. The univariate analysis was first performed by T-test to rank potential biomarkers. The metabolomic pathway analysis and hierarchical clustering analysis (HCA) were performed to group the significant metabolites involved in the same metabolic pathways or networks. Moreover, a support vector machine (SVM) predictive model was built to identify the potential metabolites in the altered pathways and clusters. The accuracy of the SVM model was evaluated by receiver operating characteristics (ROC) analysis. A total of 23 differential exhaled breath metabolites were significantly altered in patients with MDD compared with HCs and mapped in five significant metabolic pathways including aminoacyl-tRNA biosynthesis (p = 0.0055), branched chain amino acids valine, leucine and isoleucine biosynthesis (p = 0.0060), glycolysis and gluconeogenesis (p = 0.0067), nicotinate and nicotinamide metabolism (p = 0.0213) and pyruvate metabolism (p = 0.0440). Moreover, the SVM predictive model showed that butylamine (p = 0.0005, pFDR=0.0006), 3-methylpyridine (p = 0.0002, pFDR = 0.0012), endogenous aliphatic ethanol isotope (p = 0.0073, pFDR = 0.0174), valeric acid (p = 0.005, pFDR = 0.0162) and isoprene (p = 0.038, pFDR = 0.045) were potential metabolites within identified clusters with HCA and altered pathways, and discriminated between patients with MDD and non-depressed ones with high sensitivity (0.88), specificity (0.96) and area under curve of ROC (0.96). According to the results of this study, the non-targeted breathomics analysis with high-throughput sensitive analytical technologies coupled to advanced computational tools approaches offer completely new insights into peripheral biochemical changes in MDD.

Visualization of exhaled breath metabolites reveals distinct diagnostic signatures for acute cardiorespiratory breathlessness.

Acute cardiorespiratory breathlessness accounts for one in eight of all emergency hospitalizations. Early, noninvasive diagnostic testing is a clinical priority that allows rapid triage and treatment. Here, we sought to find and replicate diagnostic breath volatile organic compound (VOC) biomarkers of acute cardiorespiratory disease and understand breath metabolite network enrichment in acute disease, with a view to gaining mechanistic insight of breath biochemical derangements. We collected and analyzed exhaled breath samples from 277 participants presenting acute cardiorespiratory exacerbations and aged-matched healthy volunteers. Topological data analysis phenotypes differentiated acute disease from health and acute cardiorespiratory exacerbation subtypes (acute heart failure, acute asthma, acute chronic obstructive pulmonary disease, and community-acquired pneumonia). A multibiomarker score (101 breath biomarkers) demonstrated good diagnostic sensitivity and specificity (≥80%) in both discovery and replication sets and was associated with all-cause mortality at 2 years. In addition, VOC biomarker scores differentiated metabolic subgroups of cardiorespiratory exacerbation. Louvain clustering of VOCs coupled with metabolite enrichment and similarity assessment revealed highly specific enrichment patterns in all acute disease subgroups, for example, selective enrichment of correlated C5-7 hydrocarbons and C3-5 carbonyls in heart failure and selective depletion of correlated aldehydes in acute asthma. This study identified breath VOCs that differentiate acute cardiorespiratory exacerbations and associated subtypes and metabolic clusters of disease-associated VOCs.

Severe Obstructive Sleep Apnea Disrupts Vigilance-State-Dependent Metabolism.

The direct pathophysiological effects of obstructive sleep apnea (OSA) have been well described. However, the systemic and metabolic consequences of OSA are less well understood. The aim of this secondary analysis was to translate recent findings in healthy subjects on vigilance-state-dependent metabolism into the context of OSA patients and answer the question of how symptomatic OSA influences metabolism and whether these changes might explain metabolic and cardiovascular consequences of OSA. Patients with suspected OSA were assigned according to their oxygen desaturation index (ODI) and Epworth Sleepiness Scale (ESS) score into symptomatic OSA and controls. Vigilance-state-dependent breath metabolites assessed by high-resolution mass spectrometry were used to test for a difference in both groups. In total, 44 patients were eligible, of whom 18 (40.9%) were assigned to the symptomatic OSA group. Symptomatic OSA patients with a median [25%, 75% quartiles] ODI of 40.5 [35.0, 58.8] events/h and an ESS of 14.0 [11.2, 15.8] showed moderate to strong evidence for differences in 18 vigilance-state-dependent breath compounds compared to controls. These identified metabolites are part of major metabolic pathways in carbohydrate, amino acid, and lipid metabolism. Thus, beyond hypoxia per se, we hypothesize that disturbed sleep in OSA patients persists as disturbed sleep-dependent metabolite levels during daytime.

High-resolution mass spectrometry exhalome profiling with a modified direct analysis in real time ion source.

Exhaled breath contains many substances that are closely related to human metabolism. Analysis of its composition is important for human health, but it is difficult. Since the volatile molecules in breath samples are exhaled instantaneously, easily diffused and modified, and at low level of presence, they are difficult to identify and quantify. A modified direct analysis in real time ion source was used for high-resolution mass spectrometry measurement of human metabolites in exhaled breath through online monitoring and offline analysis, in both positive and negative ion modes. The improved system enabled the breath volatiles as well as condensates to be directly sampled, rapidly transmitted and efficiently ionized in a confined region, and then detected using an Orbitrap mass analyzer. The molecular features with online and offline analysis of exhaled breath were demonstrated with obvious differences. A total ofabout 65 metabolites in positive ion mode and about 55 metabolites in negative ion mode were identified based on accurate m/z values. Exhalome profile and the composition proportion of different classes of compounds were obtained. The relative contents of metabolites from breath varied during different time periods throughout a day. A more complete picture of the human breath metabolome was provided combining the results obtained from both online and offline analysis. The developed method allows analysis of breath samples with different status rapidly and directly, and it features simple operation and metabolite identification, requiring little or no sample preparation.

Affinity Assays for Cannabinoids Detection: Are They Amenable to On-Site Screening?

Roadside testing of illicit drugs such as tetrahydrocannabinol (THC) requires simple, rapid, and cost-effective methods. The need for non-invasive detection tools has led to the development of selective and sensitive platforms, able to detect phyto- and synthetic cannabinoids by means of their main metabolites in breath, saliva, and urine samples. One may estimate the time passed from drug exposure and the frequency of use by corroborating the detection results with pharmacokinetic data. In this review, we report on the current detection methods of cannabinoids in biofluids. Fluorescent, electrochemical, colorimetric, and magnetoresistive biosensors will be briefly overviewed, putting emphasis on the affinity formats amenable to on-site screening, with possible applications in roadside testing and anti-doping control.

Comprehensive two-dimensional gas chromatographic platforms comparison for exhaled breath metabolites analysis.

The high potential of exhaled breath for disease diagnosis has been highlighted in numerous studies. However, exhaled breath analysis is suffering from a lack of standardized sampling and analysis procedures, impacting the robustness of inter-laboratory results, and thus hampering proper external validation. The aim of this work was to verify compliance and validate the performance of two different comprehensive two-dimensional gas chromatography coupled to mass spectrometry platforms in different laboratories by monitoring probe metabolites in exhaled breath following the Peppermint Initiative guidelines. An initial assessment of the exhaled breath sampling conditions was performed, selecting the most suitable sampling bag material and volume. Then, a single sampling was performed using Tedlar bags, followed by the trapping of the volatile organic compounds into thermal desorption tubes for the subsequent analysis using two different analytical platforms. The thermal desorption tubes were first analyzed by a (cryogenically modulated) comprehensive two-dimensional gas chromatography system coupled to high-resolution time-of-flight mass spectrometry. The desorption was performed in split mode and the split part was recollected in the same tube and further analyzed by a different (flow modulated) comprehensive two-dimensional gas chromatography system with a parallel detection, specifically using a quadrupole mass spectrometer and a vacuum ultraviolet detector. Both the comprehensive two-dimensional gas chromatography platforms enabled the longitudinal tracking of the peppermint oil metabolites in exhaled breath. The increased sensitivity of comprehensive two-dimensional gas chromatography enabled to successfully monitor over a 6.5h period a total of 10 target compounds, namely α-pinene, camphene, β-pinene, limonene, cymene, eucalyptol, menthofuran, menthone, isomenthone, and neomenthol.

Mask assistance to colorimetric sniffers for detection of Covid-19 disease using exhaled breath metabolites

According to World Health Organization reports, large numbers of people around the globe have been infected or died for Covid-19 due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers are still trying to find a rapid and accurate diagnostic method for revealing infected people by low viral load with the overriding goal of effective diagnostic management. Monitoring the body metabolic changes is known as an effective and inexpensive approach for the evaluation of the infected people. Here, an optical sniffer is introduced to detect exhaled breath metabolites of patients with Covid-19 (60 samples), healthy humans (55 samples), and cured people (15 samples), providing a unique color pattern for differentiation between the studied samples. The sniffer device is installed on a thin face mask, and directly exposed to the exhaled breath stream. The interactions occurring between the volatile compounds and sensing components such as porphyrazines, modified organic dyes, porphyrins, inorganic complexes, and gold nanoparticles allowing for the change of the color, thus being tracked as the sensor responses. The assay accuracy for the differentiation between patient, healthy and cured samples is calculated to be in the range of 80%84%. The changes in the color of the sensor have a linear correlation with the disease severity and viral load evaluated by rRT-PCR method. Interestingly, comorbidities such as kidney, lung, and diabetes diseases as well as being a smoker may be diagnosed by the proposed method. As a powerful detection device, the breath sniffer can replace the conventional rapid test kits for medical applications.

Quantitative Analysis of Exhaled Breath Collected on Filter Substrates via Low-Temperature Plasma Desorption/Ionization Mass Spectrometry.

Breath analysis has attracted increasing attention in recent years due to its great potential for disease diagnostics at early stages and for clinical drug monitoring. There are several recent examples of successful development of real-time, in vivo quantitative analysis of exhaled breath metabolites via mass spectrometry. On the other hand, current mass spectrometer accessibility limitations restrict point-of-care applications. Here now, an offline method is developed for quantitative analysis of exhaled breath collected on inexpensive filter substrates for direct desorption and ionization by using low-temperature plasma-mass spectrometry (LTP-MS). In particular, different operating conditions of the ionization source were systematically studied to optimize desorption/ionization by using glycerol, a low volatility compound. Applications with respect to propofol, γ-valprolactone, and nicotine analysis in exhaled breath are demonstrated in this study. The effects of several filter substrate properties, including filter material and pore size, on the analyte signal were characterized. Cellulose filter papers performed best with the present analytes. In addition, filters with smaller pores enabled a more efficient sample collection. Furthermore, sample-collection flow rate was determined to have a very significant effect, with slower flow rates yielding the best results. It was also found that filters loaded with sample can be successfully stored in glass vials with no observable sample loss even after 3 days. Limits of detection under optimized conditions are shown to be competitive or significantly better compared with relevant techniques and with additional benefits of cost-efficiency and sample storage capabilities.

Sampling and Analysis of Low-Molecular-Weight Volatile Metabolites in Cellular Headspace and Mouse Breath.

Volatile compounds, abundant in breath, can be used to accurately diagnose and monitor a range of medical conditions. This offers a noninvasive, low-cost approach with screening applications; however, the uptake of this diagnostic approach has been limited by conflicting published outcomes. Most published reports rely on large scale screening of the public, at single time points and without reference to ambient air. Here, we present a novel approach to volatile sampling from cellular headspace and mouse breath that incorporates multi-time-point analysis and ambient air subtraction revealing compound flux as an effective proxy of active metabolism. This approach to investigating breath volatiles offers a new avenue for disease biomarker discovery and diagnosis. Using gas chromatography mass spectrometry (GC/MS), we focus on low molecular weight, metabolic substrate/by-product compounds and demonstrate that this noninvasive technique is sensitive (reproducible at ~1 µg cellular protein, or ~500,000 cells) and capable of precisely determining cell type, status and treatment. Isolated cellular models represent components of larger mammalian systems, and we show that stress- and pathology-indicative compounds are detectable in mice, supporting further investigation using this methodology as a tool to identify volatile targets in human patients.

Case Report: The effect of intravenous and oral antibiotics on the gut microbiome and breath volatile organic compounds over one year

Antimicrobial resistance (AMR) is a global concern and better understanding of the gut microbiome, a known 'amplifier' of AMR, may allow future clinicians to tailor therapy to minimise this risk and offer a personalised medicine approach. To examine the gut microbiome, patients are required to provide faecal samples; more convenient and cheaper solutions need to be found. As part of a pilot study looking at how routes of administration affect the gut microbiome in NHS patients undergoing routine clinical management for infections, we hypothesised that effects on the gut microbiome varied with the route and metabolism of antibiotic used, and these changes may be reflected in breath metabolites. We present a case report of a patient with an unusual clinical history, alongside breath metabolite and gut microbiome data taken before, during and after antibiotic therapy over a period of one year. We noted a shift in the dominant Bacteroides strain in the patient's gut microbiome between pre- and post-therapy samples, along with an alteration in the composition of breath metabolites. This study provides a framework for similar future work and highlights the need for further research on the relationships between changes in microbial gut communities and antimicrobial exposure, patient clinical status, and the metabolites of human breath.