We propose a novel application of secondary electrospray ionization-mass spectrometry (SESI-MS) as a real-time clinical diagnostic tool for bacterial infection. It is known that volatile organic compounds (VOCs), produced in different combinations and quantities by bacteria as metabolites, generate characteristic odors for certain bacteria. These VOCs comprise a specific metabolic profile that can be used for species or serovar identification, but rapid and sensitive analytical methods are required for broad utility. In this study, the VOC profiles of five bacterial groups from four genera, Pseudomonas aeruginosa , Staphylococcus aureus , Escherichia coli , Salmonella enterica serovar Typhimurium, and Salmonella enterica serovar Pullorum, were characterized by SESI-MS. Thirteen compounds were identified from these bacterial cultures, and the combination of these VOCs creates a unique pattern for each genus. In addition, principal component analysis (PCA) was applied for the purpose of species or serovar discrimination. The first three principal components exhibit a clear separation between the metabolic volatile profiles of these five bacterial groups that is independent of the growth medium. As a first step toward addressing the complexity of clinical application, in vitro tests for mixed cultures were conducted. The results show that individual species or serovars in a mixed culture are identifiable among a biological VOC background, and the ratios of the detected volatiles reflect the proportion of each bacterium in the mixture. Our data confirm the utility of SESI-MS in real-time identification of bacterial species or serovars in vitro , which, in the future, may play a promising clinical role in diagnosing infections.

Due to excellent separation capacity for complex mixtures of chemicals, comprehensive two-dimensional gas chromatography (GC × GC) is being utilized with increasing frequency for metabolomics analyses. This review describes recent advances in GC × GC method development for metabolomics, organismal sampling techniques compatible with GC × GC, metabolomic discoveries made using GC × GC, and recommendations and best practices for collecting and reporting GC × GC metabolomics data.

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.

Pseudomonas aeruginosa is a nearly ubiquitous Gram-negative organism, well known to occupy a multitude of environmental niches and cause human infections at a variety of bodily sites, due to its metabolic flexibility, secondary to extensive genetic heterogeneity at the species level. Because of its dynamic metabolism and clinical importance, we sought to perform a comparative analysis on the volatile metabolome (the 'volatilome') produced by P. aeruginosa clinical isolates. In this study, we analyzed the headspace volatile molecules of 24 P. aeruginosa clinical isolates grown in vitro, using 2D gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS). We identified 391 non-redundant compounds that we associate with the growth and metabolism of P. aeruginosa (the 'pan-volatilome'). Of these, 70 were produced by all 24 isolates (the 'core volatilome'), 52 by only a single isolate, and the remaining 269 volatile molecules by a subset. Sixty-five of the detected compounds could be assigned putative compound identifications, of which 43 had not previously been associated with P. aeruginosa. Using the accessory volatile molecules, we determined the inter-strain variation in the metabolomes of these isolates, clustering strains by their metabotypes. Assessing the extent of metabolomic diversity in P. aeruginosa through an analysis of the volatile molecules that it produces is a critical next step in the identification of novel diagnostic or prognostic biomarkers.