Characterizing bacterial volatiles using secondary electrospray ionization mass spectrometry (SESI-MS)

Database-assisted, globally optimized targeted secondary electrospray ionization high resolution mass spectrometry (dGOT-SESI-HRMS) and spectral stitching enhanced volatilomics analysis of bacterial metabolites

Secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) is an innovative analytical technique for the rapid and non-invasive analysis of volatile organic compounds (VOCs). However, compound annotation and ion suppression in the SESI source has hindered feature detection, stability and reproducibility of SESI-HRMS in untargeted volatilomics. To address this, we have developed and optimized a novel pseudo-targeted approach, database-assisted globally optimized targeted (dGOT)-SESI-HRMS using the microbial-VOC (mVOC) database, and spectral stitching methods to enhance metabolite detection in headspace of anaerobic bacterial cultures. Headspace volatiles from representative bacteria strains were assessed using full scan with data dependent acquisition (DDA), conventional globally optimized targeted (GOT) method, and spectral stitching supported dGOT experiments based on a MS peaks list derived from mVOC. Our results indicate that spectral stitching supported dGOT-SESI-HRMS can proportionally fragment peaks with respect to different analysis windows, with a total of 109 VOCs fragmented from 306 targeted compounds. Of the collected spectra, 88 features were confirmed as culture derived volatiles with respect to media blanks. Annotation was also achieved with a total of 25 unique volatiles referenced to standard databases allowing for biological interpretation. Principal component analysis (PCA) summarizing the headspace volatile demonstrated improved separation of clusters when data was acquired using the dGOT method. Collectively, our dGOT-SESI-HRMS method afforded robust capability of capturing unique VOC profiles from different bacterial strains and culture conditions when compared to conventional GOT and DDA modes, suggesting the newly developed approach can serve as a more reliable analytical method for the sensitive monitoring of gut microbial metabolism.

The potential of volatile organic compound analysis for pathogen detection and disease monitoring in patients with cystic fibrosis

INTRODUCTION

Airway infection with pathogens and its associated pulmonary exacerbations (PEX) are the major causes of morbidity and premature death in cystic fibrosis (CF). Preventing or postponing chronic infections requires early diagnosis. However, limitations of conventional microbiology-based methods can hamper identification of exacerbations and specific pathogen detection. Analyzing volatile organic compounds (VOCs) in breath samples may be an interesting tool in this regard, as VOC-biomarkers can characterize specific airway infections in CF.

AREAS COVERED

We address the current achievements in VOC-analysis and discuss studies assessing VOC-biomarkers and fingerprints, i.e. a combination of multiple VOCs, in breath samples aiming at pathogen and PEX detection in people with CF (pwCF). We aim to provide bases for further research in this interesting field.

EXPERT OPINION

Overall, VOC-based analysis is a promising tool for diagnosis of infection and inflammation with potential to monitor disease progression in pwCF. Advantages over conventional diagnostic methods, including easy and non-invasive sampling procedures, may help to drive prompt, suitable therapeutic approaches in the future. Our review shall encourage further research, including validation of VOC-based methods. Specifically, longitudinal validation under standardized conditions is of interest in order to ensure repeatability and enable inclusion in CF diagnostic routine.

Hyphenated Mass Spectrometry versus Real-Time Mass Spectrometry Techniques for the Detection of Volatile Compounds from the Human Body

Mass spectrometry (MS) is an analytical technique that can be used for various applications in a number of scientific areas including environmental, security, forensic science, space exploration, agri-food, and numerous others. MS is also continuing to offer new insights into the proteomic and metabolomic fields. MS techniques are frequently used for the analysis of volatile compounds (VCs). The detection of VCs from human samples has the potential to aid in the diagnosis of diseases, in monitoring drug metabolites, and in providing insight into metabolic processes. The broad usage of MS has resulted in numerous variations of the technique being developed over the years, which can be divided into hyphenated and real-time MS techniques. Hyphenated chromatographic techniques coupled with MS offer unparalleled qualitative analysis and high accuracy and sensitivity, even when analysing complex matrices (breath, urine, stool, etc.). However, these benefits are traded for a significantly longer analysis time and a greater need for sample preparation and method development. On the other hand, real-time MS techniques offer highly sensitive quantitative data. Additionally, real-time techniques can provide results in a matter of minutes or even seconds, without altering the sample in any way. However, real-time MS can only offer tentative qualitative data and suffers from molecular weight overlap in complex matrices. This review compares hyphenated and real-time MS methods and provides examples of applications for each technique for the detection of VCs from humans.

Circadian Metabolomics from Breath

Metabolites like melatonin are essential in determining circadian phase. In the recent years, comprehensive metabolome analyses have unveiled entire panels of small biomolecules fluctuating in a circadian fashion, thus enabling a more precise determination of inner time and understanding of how circadian clock operates at the molecular level. Emerging analytical techniques allowing for the determination of exhaled metabolites in breath show promise to gain further insights noninvasively and in vivo into circadian metabolism.

Standardization procedures for real-time breath analysis by secondary electrospray ionization high-resolution mass spectrometry

Despite the attractiveness of breath analysis as a non-invasive means to retrieve relevant metabolic information, its introduction into routine clinical practice remains a challenge. Among all the different analytical techniques available to interrogate exhaled breath, secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) offers a number of advantages (e.g., real-time, yet wide, metabolome coverage) that makes it ideal for untargeted and targeted studies. However, so far, SESI-HRMS has relied mostly on lab-built prototypes, making it difficult to standardize breath sampling and subsequent analysis, hence preventing further developments such as multi-center clinical studies. To address this issue, we present here a number of new developments. In particular, we have characterized a new SESI interface featuring real-time readout of critical exhalation parameters such as CO₂, exhalation flow rate, and exhaled volume. Four healthy subjects provided breath specimens over a period of 1 month to characterize the stability of the SESI-HRMS system. A first assessment of the repeatability of the system using a gas standard revealed a coefficient of variation (CV) of 2.9%. Three classes of aldehydes, namely 4-hydroxy-2-alkenals, 2-alkenals and 4-hydroxy-2,6-alkedienals―hypothesized to be markers of oxidative stress―were chosen as representative metabolites of interest to evaluate the repeatability and reproducibility of this breath analysis analytical platform. Median and interquartile ranges (IQRs) of CVs for CO₂, exhalation flow rate, and exhaled volume were 3.2% (1.5%), 3.1% (1.9%), and 5.0% (4.6%), respectively. Despite the high repeatability observed for these parameters, we observed a systematic decay in the signal during repeated measurements for the shorter fatty aldehydes, which eventually reached a steady state after three/four repeated exhalations. In contrast, longer fatty aldehydes showed a steady behavior, independent of the number of repeated exhalation maneuvers. We hypothesize that this highly molecule-specific and individual-independent behavior may be explained by the fact that shorter aldehydes (with higher estimated blood-to-air partition coefficients; approaching 100) mainly get exchanged in the airways of the respiratory system, whereas the longer aldehydes (with smaller estimated blood-to-air partition coefficients; approaching 10) are thought to exchange mostly in the alveoli. Exclusion of the first three exhalations from the analysis led to a median CV (IQR) of 6.7 % (5.5 %) for the said classes of aldehydes. We found that such intra-subject variability is in general much lower than inter-subject variability (median relative differences between subjects 48.2%), suggesting that the system is suitable to capture such differences. No batch effect due to sampling date was observed, overall suggesting that the intra-subject variability measured for these series of aldehydes was biological rather than technical. High correlations found among the series of aldehydes support this notion. Finally, recommendations for breath sampling and analysis for SESI-HRMS users are provided with the aim of harmonizing procedures and improving future inter-laboratory comparisons.

Metabolic changes during periodontitis therapy assessed by real-time ambient mass spectrometry

It has been shown that bacteria in periodontally diseased patients can be recognized by the detection of volatile metabolites in the headspace of saliva by real-time ambient mass spectrometry. The aim of this study was to use this detection method to analyze the oral metabolome in diseased periodontitis patients before and after therapy to monitor disease evolution and healing events. Twelve patients with advanced chronic periodontal disease and 12 periodontally healthy controls served as test and control groups, respectively. Clinical data, subgingival plaque samples and saliva samples were collected at baseline (BL) and 3 months after treatment. The test group received non-surgical scaling and root planing using systemic antibiotics and the control group received one session of supragingival cleaning. Saliva samples from all subjects were analyzed with ambient mass spectrometry. Significant metabolic alterations were found in the headspace of saliva of periodontitis patients 3 months after the non-surgical periodontal treatment. Furthermore, the diseased group showed metabolic features after the treatment that were similar to the healthy control group. In addition, 29 metabolic features correlated with A. actinomycetemcomitans, 17 features correlated with P. gingivalis and one feature correlated with T. denticola. It was shown that headspace secondary electrospray ionization - mass spectrometry allows the detection of different volatile metabolites in healthy and diseased patients. It can be concluded that this rapid and minimally invasive method could have the potential to routinely diagnose and monitor periodontal diseases in the headspace of saliva samples and, eventually, in exhaled breath.

Evolution of clinical and environmental health applications of exhaled breath research: Review of methods and instrumentation for gas-phase, condensate, and aerosols

Human breath, along with urine and blood, has long been one of the three major biological media for assessing human health and environmental exposure. In fact, the detection of odor on human breath, as described by Hippocrates in 400 BC, is considered the first analytical health assessment tool. Although less common in comparison to contemporary bio-fluids analyses, breath has become an attractive diagnostic medium as sampling is non-invasive, unlimited in timing and volume, and does not require clinical personnel. Exhaled breath, exhaled breath condensate (EBC), and exhaled breath aerosol (EBA) are different types of breath matrices used to assess human health and disease state. Over the past 20 years, breath research has made many advances in assessing health state, overcoming many of its initial challenges related to sampling and analysis. The wide variety of sampling techniques and collection devices that have been developed for these media are discussed herein. The different types of sensors and mass spectrometry instruments currently available for breath analysis are evaluated as well as emerging breath research topics, such as cytokines, security and airport surveillance, cellular respiration, and canine olfaction.

Towards enhanced metabolomic data analysis of mass spectrometry image: Multivariate Curve Resolution and Machine Learning

Large amounts of data are generally produced from mass spectrometry imaging (MSI) experiments in obtaining the molecular and spatial information of biological samples. Traditionally, MS images are constructed using manually selected ions, and it is very challenging to comprehensively analyze MSI results due to their large data sizes and highly complex data structures. To overcome these barriers, it is obligatory to develop advanced data analysis approaches to handle the increasingly large MSI data. In the current study, we focused on the method development of using Multivariate Curve Resolution (MCR) and Machine Learning (ML) approaches. We aimed to effectively extract the essential information present in the large and complex MSI data and enhance the metabolomic data analysis of biological tissues. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) algorithm was used to obtain major patterns of spatial distribution and grouped metabolites with the same spatial distribution patterns. In addition, both supervised and unsupervised ML methods were established to analyze the MSI data. In the supervised ML approach, Random Forest method was selected, and the model was trained using the selected datasets based on the distribution pattern obtained from MCR-ALS analyses. In the unsupervised ML approach, both DBSCAN (Density-based Spatial Clustering of Applications with Noise) and CLARA (Clustering Large Applications) were applied to cluster the MSI datasets. It is worth noting that similar patterns of spatial distribution were discovered through MSI data analysis using MCR-ALS, supervised ML, and unsupervised ML. Our protocols of data analysis can be applied to process the data acquired using many other types of MSI techniques, and to extract the overall features present in MSI results that are intractable using traditional data analysis approaches.

Translating secondary electrospray ionization-high-resolution mass spectrometry to the clinical environment

While there has been progress in making use of breath tests to guide clinical decision making, the full potential of exhaled breath analysis still remains to be exploited. Here we summarize some of the reasons why this is the case, what we have done so far to overcome some of the existing obstacles, and our vision of how we think breath analysis will play a more prominent role in the coming years. In particular, we envision that real-time high-resolution mass spectrometry will provide valuable information in biomarker discovery studies. However, this can only be achieved by a coordinated effort, using standardized equipment and methods in multi-center studies to eventually deliver tangible advances in the field of breath analysis in a clinical setting. Concrete aspects such as sample integrity, compound identification, quantification and standardization are discussed. Novel secondary electrospray ionization developments with the aim of facilitating inter-groups comparisons and biomarker validation studies are also presented.

The potential role of exhaled breath analysis in the diagnostic process of pneumonia-a systematic review

Diagnostic strategies currently used for pneumonia are time-consuming, lack accuracy and suffer from large inter-observer variability. Exhaled breath contains thousands of volatile organic compounds (VOCs), which include products of host and pathogen metabolism. In this systematic review we investigated the use of so-called 'breathomics' for diagnosing pneumonia. A Medline search yielded 18 manuscripts reporting on animal and human studies using organic and inorganic molecules in exhaled breath, that all could be used to answer whether analysis of VOC profiles could potentially improve the diagnostic process of pneumonia. Papers were categorised based on their specific aims; the exclusion of pneumonia; the detection of specific respiratory pathogens; and whether targeted or untargeted VOC analysis was used. Ten studies reported on the association between VOCs and presence of pneumonia. Eight studies demonstrated a difference in exhaled VOCs between pneumonia and controls; in the individual studies this discrimination was based on unique sets of VOCs. Eight studies reported on the accuracy of a breath test for a specific respiratory pathogen: five of these concerned pre-clinical studies in animals. All studies were valued as having a high risk of bias, except for one study that used an external validation cohort. The findings in the identified studies are promising. However, as yet no breath test has been shown to have sufficient diagnostic accuracy for pneumonia. We are in need of studies that further translate the knowledge from discovery studies to clinical practice.

Initial study of three different pathogenic microorganisms by gas chromatography-mass spectrometry

Background: Diagnoses  of  respiratory  tract  infections  usually happen  in  the  late  phase  of  the  disease  and  usually  result  in  reduction  of  the  pathogen  load after broad-spectrum  antibiotic  therapy,  but  not  in eradication of the pathogen.  The  development  of a  non-invasive,  fast,  and  accurate  method  to  detect  pathogens  has  always  been  of  interest  to  researchers  and  clinicians  alike.  Previous studies have shown that bacteria produce organic gases.  The  current  study  aimed  to  identify  the  volatile  organic  compounds  (VOCs)  produced  by three  respiratory  tract  pathogens,  including  Staphylococcus  aureus,  Escherichia  coli  and  Candida  albicans.Methods: The  VOCs  produced  were identified by gas chromatography-mass spectrometry (GC-MS), with  prior  collection  of  microbial  volatile  compounds  using  solid  phase  microextraction  (SPME)  fiber.  The volatile compounds were collected by obtaining bacterial headspace samples. Results: Results  showed  that  these  three  organisms  have  various  VOCs,  which  were  analyzed  under  different  conditions.  By ignoring common VOCs, some species-specific VOCs could be detected.  The most important VOC of E. coli was indole, also some important VOCs produced by S. aureus  were 2,3-pentandione,  cis-dihydro-α-terpinyl  acetate,  1-decyne,  1,3-heptadiene,  2,5-dimethyl  pyrazine,  ethyl  butanoate  and  cyclohexene,4-ethenyl. Furthermore,  most  of the identified  compounds  by  C.  albicans are  alcohols. Conclusions: The  detection  of  VOCs  produced  by  infectious  agents  maybe  the  key  to  make   a  rapid  and  precise  diagnosis  of  infection,  but  more  comprehensive  studies  must  be  conducted  in this  regard.

Initial study of three different pathogenic microorganisms by gas chromatography-mass spectrometry

Background: Diagnoses  of  respiratory  tract  infections  usually happen  in  the  late  phase  of  the  disease  and  usually  result  in  reduction  of  the  pathogen  load after broad-spectrum  antibiotic  therapy,  but  not  in eradication of the pathogen.  The  development  of a  non-invasive,  fast,  and  accurate  method  to  detect  pathogens  has  always  been  of  interest  to  researchers  and  clinicians  alike.  Previous studies have shown that bacteria produce organic gases.  The  current  study  aimed  to  identify  the  volatile  organic  compounds  (VOCs)  produced  by three  respiratory  tract  pathogens,  including  Staphylococcus  aureus,  Escherichia  coli  and  Candida  albicans.Methods: The  VOCs  produced  were identified by gas chromatography-mass spectrometry (GC-MS), with  prior  collection  of  microbial  volatile  compounds  using  solid  phase  microextraction  (SPME)  fiber.  The volatile compounds were collected by obtaining bacterial headspace samples. Results: Results  showed  that  these  three  organisms  have  various  VOCs,  which  were  analyzed  under  different  conditions.  By ignoring common VOCs, some species-specific VOCs could be detected.  The most important VOC of E. coli was indole, also some important VOCs produced by S. aureus  were 2,3-pentandione,  cis-dihydro-α-terpinyl  acetate,  1-decyne,  1,3-heptadiene,  2,5-dimethyl  pyrazine,  ethyl  butanoate  and  cyclohexene,4-ethenyl. Furthermore,  most  of the identified  compounds  by  C.  albicans are  alcohols. Conclusions: The  detection  of  VOCs  produced  by  infectious  agents  maybe  the  key  to  make   a  rapid  and  precise  diagnosis  of  infection,  but  more  comprehensive  studies  must  be  conducted  in this  regard.

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Proton Transfer Reaction (PTR), combined with a Time-of-Flight (ToF) Mass Spectrometer (MS) is an analytical approach based on chemical ionization that belongs to the Direct-Injection Mass Spectrometric (DIMS) technologies. These techniques allow the rapid determination of volatile organic compounds (VOCs), assuring high sensitivity and accuracy. In general, PTR-MS requires neither sample preparation nor sample destruction, allowing real time and non-invasive analysis of samples. PTR-MS are exploited in many fields, from environmental and atmospheric chemistry to medical and biological sciences. More recently, we developed a methodology based on coupling PTR-ToF-MS with an automated sampler and tailored data analysis tools, to increase the degree of automation and, consequently, to enhance the potential of the technique. This approach allowed us to monitor bioprocesses (e.g. enzymatic oxidation, alcoholic fermentation), to screen large sample sets (e.g. different origins, entire germoplasms) and to analyze several experimental modes (e.g. different concentrations of a given ingredient, different intensities of a specific technological parameter) in terms of VOC content. Here, we report the experimental protocols exemplifying different possible applications of our methodology: i.e. the detection of VOCs released during lactic acid fermentation of yogurt (on-line bioprocess monitoring), the monitoring of VOCs associated with different apple cultivars (large-scale screening), and the in vivo study of retronasal VOC release during coffee drinking (nosespace analysis).

Profiling aged artisanal Cheddar cheese using secondary electrospray ionization mass spectrometry

A number of direct injection mass spectrometry methods that can sample foods nondestructively and without sample preparation are being developed with applications ranging from the rapid assessment of food safety to the verification of protected designations of origin. In this pilot study, secondary electrospray ionization mass spectrometry (SESI-MS) in positive- and negative-ion modes was used to collect volatile fingerprints of artisanal Cheddar cheeses aged for one to three years. SESI-MS fingerprints were found to change in an aging-dependent manner and can be used to descriptively and predictively categorize Cheddars by their aging period, identify volatile components that increase or decrease with aging, and robustly discriminate individual batches of artisanal cheese. From these results, it was concluded that SESI-MS volatile fingerprinting could be used by artisanal food producers to characterize their products during production and aging, providing useful data to help them maximize the value of each batch.

Microextraction techniques in breath biomarker analysis

Breath volatile organic compound analysis may open a non-invasive window onto (patho)physiological and metabolic processes in the body. Breath tests require controlled sampling with respect to different breath phases and on-site and point-of-care applicability. Microextraction techniques such as solid phase microextraction (SPME) or needle-trap microextraction (NTME) meet these requirements. Small sample volumes and fast and controlled sample preparation combine on-site sampling and pre-concentration in one step. Detection limits in the low ppbV range and fast and simple processing facilitate the application of distribution-based SPME for screening and targeted analysis. Exhaustive NTME has shown further advantages such as fast and automated sampling, improved stability and reproducibility with improved detection limits. Combinations of different sorbents and thermal expansion desorption have shown most promising properties when applied to water saturated breath samples. This article addresses major challenges and advantages of microextraction techniques in breath analysis. Important progress, current applications and future trends are discussed.

Circadian variation of the human metabolome captured by real-time breath analysis

Circadian clocks play a significant role in the correct timing of physiological metabolism, and clock disruption might lead to pathological changes of metabolism. One interesting method to assess the current state of metabolism is metabolomics. Metabolomics tries to capture the entirety of small molecules, i.e. the building blocks of metabolism, in a given matrix, such as blood, saliva or urine. Using mass spectrometric approaches we and others have shown that a significant portion of the human metabolome in saliva and blood exhibits circadian modulation; independent of food intake or sleep/wake rhythms. Recent advances in mass spectrometry techniques have introduced completely non-invasive breathprinting; a method to instantaneously assess small metabolites in human breath. In this proof-of-principle study, we extend these findings about the impact of circadian clocks on metabolomics to exhaled breath. As previously established, our method allows for real-time analysis of a rich matrix during frequent non-invasive sampling. We sampled the breath of three healthy, non-smoking human volunteers in hourly intervals for 24 hours during total sleep deprivation, and found 111 features in the breath of all individuals, 36–49% of which showed significant circadian variation in at least one individual. Our data suggest that real-time mass spectrometric "breathprinting" has high potential to become a useful tool to understand circadian metabolism, and develop new biomarkers to easily and in real-time assess circadian clock phase and function in experimental and clinical settings.

Breathprints of model murine bacterial lung infections are linked with immune response

In this model study, we explored the host's contribution of breath volatiles to diagnostic secondary electrospray ionisation-mass spectrometry (SESI-MS) breathprints for acute bacterial lung infections, their correlation with the host's immune response, and their use in identifying the lung pathogen. Murine airways were exposed to Pseudomonas aeruginosa and Staphylococcus aureus bacterial cell lysates or to PBS (controls), and their breath and bronchoalveolar lavage fluid (BALF) were collected at six time points (from 6 to 120 h) after exposure. Five to six mice per treatment group and four to six mice per control group were sampled at each time. Breath volatiles were analysed using SESI-MS and the BALF total leukocytes, polymorphonuclear neutrophils, lactate dehydrogenase activity, and cytokine concentrations were quantified. Lysate exposure breathprints contain host volatiles that persist for up to 120 h; are pathogen specific; are unique from breathprints of controls, active infections and cleared infections; and are correlated with the host's immune response. Bacterial lung infections induce changes to the host's breath volatiles that are selective and specific predictors of the source of infection. Harnessing the pathogen-specific volatiles in the host's breath may provide useful information for detecting latent bacterial lung infections and managing the spread of respiratory diseases.

Fingerprinting breast cancer vs. normal mammary cells by mass spectrometric analysis of volatiles

There is increasing interest in the development of noninvasive diagnostic methods for early cancer detection, to improve the survival rate and quality of life of cancer patients. Identification of volatile metabolic compounds may provide an approach for noninvasive early diagnosis of malignant diseases. Here we analyzed the volatile metabolic signature of human breast cancer cell lines versus normal human mammary cells. Volatile compounds in the headspace of conditioned culture medium were directly fingerprinted by secondary electrospray ionization-mass spectrometry. The mass spectra were subsequently treated statistically to identify discriminating features between normal vs. cancerous cell types. We were able to classify different samples by using feature selection followed by principal component analysis (PCA). Additionally, high-resolution mass spectrometry allowed us to propose their chemical structures for some of the most discriminating molecules. We conclude that cancerous cells can release a characteristic odor whose constituents may be used as disease markers.

Breath analysis in real time by mass spectrometry in chronic obstructive pulmonary disease

BACKGROUND

It has been suggested that exhaled breath contains relevant information on health status.

OBJECTIVES

We hypothesized that a novel mass spectrometry (MS) technique to analyze breath in real time could be useful to differentiate breathprints from chronic obstructive pulmonary disease (COPD) patients and controls (smokers and nonsmokers).

METHODS

We studied 61 participants including 25 COPD patients [Global Initiative for Obstructive Lung Disease (GOLD) stages I-IV], 25 nonsmoking controls and 11 smoking controls. We analyzed their breath by MS in real time. Raw mass spectra were then processed and statistically analyzed.

RESULTS

A panel of discriminating mass-spectral features was identified for COPD (all stages; n = 25) versus healthy nonsmokers (n = 25), COPD (all stages; n = 25) versus healthy smokers (n = 11) and mild COPD (GOLD stages I/II; n = 13) versus severe COPD (GOLD stages III/IV; n = 12). A blind classification (i.e. leave-one-out cross validation) resulted in 96% sensitivity and 72.7% specificity (COPD vs. smoking controls), 88% sensitivity and 92% specificity (COPD vs. nonsmoking controls) and 92.3% sensitivity and 83.3% specificity (GOLD I/II vs. GOLD III/IV). Acetone and indole were identified as two of the discriminating exhaled molecules.

CONCLUSIONS

We conclude that real-time MS may be a useful technique to analyze and characterize the metabolome of exhaled breath. The acquisition of breathprints in a rapid manner may be valuable to support COPD diagnosis and to gain insight into the disease.

Robust detection of P. aeruginosa and S. aureus acute lung infections by secondary electrospray ionization-mass spectrometry (SESI-MS) breathprinting: from initial infection to clearance

Before breath-based diagnostics for lung infections can be implemented in the clinic, it is necessary to understand how the breath volatiles change during the course of infection, and ideally, to identify a core set of breath markers that can be used to diagnose the pathogen at any point during the infection. In the study presented here, we use secondary electrospray ionization-mass spectrometry (SESI-MS) to characterize the breathprint of Pseudomonas aeruginosa and Staphylococcus aureus lung infections in a murine model over a period of 120 h, with a total of 86 mice in the study. Using partial least squares-discriminant analysis (PLS-DA) to evaluate the time-course data, we were able to show that SESI-MS breathprinting can be used to robustly classify acute P. aeruginosa and S. aureus mouse lung infections at any time during the 120 h infection/clearance process. The variable importance plot from PLS indicates that multiple peaks from the SESI-MS breathprints are required for discriminating the bacterial infections. Therefore, by utilizing the entire breathprint rather than single biomarkers, infectious agents can be diagnosed by SESI-MS independent of when during the infection breath is tested.

Secondary electrospray ionization-mass spectrometry (SESI-MS) breathprinting of multiple bacterial lung pathogens, a mouse model study

Bacterial pneumonia is one of the leading causes of disease-related morbidity and mortality in the world, in part because the diagnostic tools for pneumonia are slow and ineffective. To improve the diagnosis success rates and treatment outcomes for bacterial lung infections, we are exploring the use of secondary electrospray ionization-mass spectrometry (SESI-MS) breath analysis as a rapid, noninvasive method for determining the etiology of lung infections in situ. Using a murine lung infection model, we demonstrate that SESI-MS breathprints can be used to distinguish mice that are infected with one of seven lung pathogens: Haemophilus influenzae, Klebsiella pneumoniae, Legionella pneumophila, Moraxella catarrhalis, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae, representing the primary causes of bacterial pneumonia worldwide. After applying principal components analysis, we observed that with the first three principal components (primarily comprised of data from 14 peaks), all infections were separable via SESI-MS breathprinting (P < 0.0001). Therefore, we have shown the potential of this SESI-MS approach for rapidly detecting and identifying acute bacterial lung infections in situ via breath analysis.

Detecting bacterial lung infections: in vivo evaluation of in vitro volatile fingerprints

The identification of bacteria by their volatilomes is of interest to many scientists and clinicians as it holds the promise of diagnosing infections in situ, particularly lung infections via breath analysis. While there are many studies reporting various bacterial volatile biomarkers or fingerprints using in vitro experiments, it has proven difficult to translate these data to in vivo breath analyses. Therefore, we aimed to create secondary electrospray ionization-mass spectrometry (SESI-MS) pathogen fingerprints directly from the breath of mice with lung infections. In this study we demonstrated that SESI-MS is capable of differentiating infected versus uninfected mice, P. aeruginosa-infected versus S. aureus-infected mice, as well as distinguish between infections caused by P. aeruginosa strains PAO1 versus FRD1, with statistical significance (p < 0.05). In addition, we compared in vitro and in vivo volatiles and observed that only 25-34% of peaks are shared between the in vitro and in vivo SESI-MS fingerprints. To the best of our knowledge, these are the first breath volatiles measured for P. aeruginosa PAO1, FRD1, and S. aureus RN450, and the first comparison of in vivo and in vitro volatile profiles from the same strains using the murine infection model.