Detection of volatile metabolites of Escherichia coli by multi capillary column coupled ion mobility spectrometry

Patterns of volatile organic compounds in excrements of preterm neonates

BACKGROUND

As neonates are susceptible for many diseases, establishing noninvasive diagnostic methods is desirable. We hypothesized that volatile organic compounds (VOCs) could be successfully measured in diaper samples.

METHODS

We performed a feasibility study to investigate whether ambient air-independent headspace measurements of the VOC profiles of diapers from premature infants can be conducted using ion mobility spectrometer coupled with multi-capillary columns (B & S Analytik GmbH).

RESULTS

We analysed 39 diapers filled with stool (n = 10) or urine (n = 20) respectively, using empty diapers as a control (n = 9). A total of 158 different VOCs were identified, and we classified the content of the diapers (urine or stool) according to their VOC profiles with a significance level of p < 0.05.

CONCLUSIONS

We have developed a novel method to study headspace VOC profiles of biosamples using ion mobility spectrometry coupled with multi-capillary columns. Using this method, we have characterized the VOC profiles of stool and urine of preterm neonates. Future studies are warranted to characterize specific VOC profiles in infections and other diseases of the preterm neonate, thus establishing quick and noninvasive diagnostics in the routine care of the highly vulnerable preterm and term neonates.

Smelling the difference: separation of healthy and infected button mushrooms via microbial volatile organic compounds

In the literature there is a lack of consensus regarding mushroom volatiles; most of the studies identify only a few volatiles. This study deals with button mushrooms, their emitted volatiles, and the main changes during infections (green mould and cobweb disease) in a time series experiment. Emitted volatile profiles were determined using HS-SPME-GC-MS coupled analytical technique. The separation of healthy and infected mushroom samples was done using different multivariate statistical methods (PCA, PLS-DA, HeatMap). The main volatile compounds were also determined. As a result, several compounds were found to successfully distinguish healthy (bisabolene, cymene, myrtenol, d-limonene, etc.) and infected (thujopsene, cedr-8-ene, chamigrene, patchulane, longifolene, etc.), mushroom samples, and an early disease detection was achieved. Results can be used for further investigation of infected mushroom identification in an early stage in packaged mushroom products. Furthermore, these results could help to identify infections in commercially available mushrooms, thus increasing shelf-life in super/hypermarkets.

Blood Culture Headspace Gas Analysis Enables Early Detection of Escherichia coli Bacteremia in an Animal Model of Sepsis

(1) Background: Automated blood culture headspace analysis for the detection of volatile organic compounds of microbial origin (mVOC) could be a non-invasive method for bedside rapid pathogen identification. We investigated whether analyzing the gaseous headspace of blood culture (BC) bottles through gas chromatography-ion mobility spectrometry (GC-IMS) enables differentiation of infected and non-infected; (2) Methods: BC were gained out of a rabbit model, with sepsis induced by intravenous administration of E. coli (EC group; n = 6) and control group (n = 6) receiving sterile LB medium intravenously. After 10 h, a pair of blood cultures was obtained and incubated for 36 h. The headspace from aerobic and anaerobic BC was sampled every two hours using an autosampler and analyzed using a GC-IMS device. MALDI-TOF MS was performed to confirm or exclude microbial growth in BCs; (3) Results: Signal intensities (SI) of 113 mVOC peak regions were statistically analyzed. In 24 regions, the SI trends differed between the groups and were considered to be useful for differentiation. The principal component analysis showed differentiation between EC and control group after 6 h, with 62.2% of the data variance described by the principal components 1 and 2. Single peak regions, for example peak region P_15, show significant SI differences after 6 h in the anaerobic environment (p < 0.001) and after 8 h in the aerobic environment (p < 0.001); (4) Conclusions: The results are promising and warrant further evaluation in studies with an extended microbial panel and indications concerning its transferability to human samples.

Volatolomics in healthcare and its advanced detection technology

Various diseases increasingly challenge the health status and life quality of human beings. Volatolome emitted from patients has been considered as a potential family of markers, volatolomics, for diagnosis/screening. There are two fundamental issues of volatolomics in healthcare. On one hand, the solid relationship between the volatolome and specific diseases needs to be clarified and verified. On the other hand, effective methods should be explored for the precise detection of volatolome. Several comprehensive review articles had been published in this field. However, a timely and systematical summary and elaboration is still desired. In this review article, the research methodology of volatolomics in healthcare is critically considered and given out, at first. Then, the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically summarized. Thirdly, the advanced electronic nose and photonic nose technologies for volatile organic compounds (VOCs) detection are well introduced. The existed obstacles and future perspectives are deeply thought and discussed. This article could give a good guidance to researchers in this interdisciplinary field, not only understanding the cutting-edge detection technologies for doctors (medicinal background), but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists, materials scientists, electronics engineers, etc.

(S)-Reutericyclin: Susceptibility Testing and In Vivo Effect on Murine Fecal Microbiome and Volatile Organic Compounds

We aimed to assess the in vitro antimicrobial activity and the in vivo effect on the murine fecal microbiome and volatile organic compound (VOC) profile of (S)-reutericyclin. The antimicrobial activity of (S)-reutericyclin was tested against Clostridium difficile, Listeria monocytogenes, Escherichia coli, Enterococcus faecium, Staphylococcus aureus, Staphylococcus (S.) epidermidis, Streptococcus agalactiae, Pseudomonas aeruginosa and Propionibacterium acnes. Reutericyclin or water were gavage fed to male BALBc mice for 7 weeks. Thereafter stool samples underwent 16S based microbiome analysis and VOC analysis by gas chromatography mass spectrometry (GC-MS). (S)-reutericyclin inhibited growth of S. epidermidis only. Oral (S)-reutericyclin treatment caused a trend towards reduced alpha diversity. Beta diversity was significantly influenced by reutericyclin. Linear discriminant analysis Effect Size (LEfSe) analysis showed an increase of Streptococcus and Muribaculum as well as a decrease of butyrate producing Ruminoclostridium, Roseburia and Eubacterium in the reutericyclin group. VOC analysis revealed significant increases of pentane and heptane and decreases of 2,3-butanedione and 2-heptanone in reutericyclin animals. The antimicrobial activity of (S)-reutericyclin differs from reports of (R)-reutericyclin with inhibitory effects on a multitude of Gram-positive bacteria reported in the literature. In vivo (S)-reutericyclin treatment led to a microbiome shift towards dysbiosis and distinct alterations of the fecal VOC profile.

A New High-Throughput Screening Method to Detect Antimicrobial Volatiles from Metagenomic Clone Libraries

The ever-growing spread of resistance in medicine and agriculture highlights the need to identify new antimicrobials. Microbial volatile organic compounds (VOCs) are one of the most promising groups of chemicals to meet this need. These rarely exploited molecules exhibit antimicrobial activity and their high vapour pressure makes them ideal for application in surface sterilisation, and in particular, in biofumigation. Therefore, we adapted the previously developed Two Clamp VOCs Assay (TCVA) to a new high-throughput screening for the detection of novel antifungal VOCs from metagenomic clone libraries. As a proof of concept, we tested the new high-throughput TCVA (htTCVA) by sourcing a moss metagenomic library against Fusarium culmorum. This led to the identification of five clones that inhibited the growth of mycelium and spores in vitro by up to 8% and 30% and subsequently, to the identification of VOCs that are potentially, and in part responsible for the clones’ antifungal activity. For these VOCs, the in vitro effect of the pure compounds was as high as 100%. These results demonstrate the robustness and feasibility of the htTCVA, which provides access to completely new and unexplored biosynthetic pathways and their secondary metabolites.

Sniffing Out Urinary Tract Infection-Diagnosis Based on Volatile Organic Compounds and Smell Profile

Current available methods for the clinical diagnosis of urinary tract infection (UTI) rely on a urine dipstick test or culturing of pathogens. The dipstick test is rapid (available in 1-2 min), but has a low positive predictive value, while culturing is time-consuming and delays diagnosis (24-72 h between sample collection and pathogen identification). Due to this delay, broad-spectrum antibiotics are often prescribed immediately. The over-prescription of antibiotics should be limited, in order to prevent the development of antimicrobial resistance. As a result, there is a growing need for alternative diagnostic tools. This paper reviews applications of chemical-analysis instruments, such as gas chromatography-mass spectrometry (GC-MS), selected ion flow tube mass spectrometry (SIFT-MS), ion mobility spectrometry (IMS), field asymmetric ion mobility spectrometry (FAIMS) and electronic noses (eNoses) used for the diagnosis of UTI. These methods analyse volatile organic compounds (VOCs) that emanate from the headspace of collected urine samples to identify the bacterial pathogen and even determine the causative agent's resistance to different antibiotics. There is great potential for these technologies to gain wide-spread and routine use in clinical settings, since the analysis can be automated, and test results can be available within minutes after sample collection. This could significantly reduce the necessity to prescribe broad-spectrum antibiotics and allow the faster and more effective use of narrow-spectrum antibiotics.

Biosynthesis of the Nematode Attractant 2-Heptanone and Its Co-evolution Between the Pathogenic Bacterium Bacillus nematocida and Non-pathogenic Bacterium Bacillus subtilis

Methylketones are broadly distributed in nature and perform a variety of functions. Most microorganisms are thought to produce methylketone by abortive β-oxidation of fatty acid catalytic metabolism. However, two methylketone synthetase genes in wild tomatoes are reported to synthesize methylketone using intermediates of the fatty acids biosynthetic pathway. In our previous study on Trojan horse-like interactions between the bacterium Bacillus nematocida B16 and its host worm, the chemical 2-heptanone was found to be an important attractant for the hosts. So here we used this model to investigate the genes involved in synthesizing 2-heptanone in microorganisms. We identified a novel methylketone synthase gene yneP in B. nematocida B16 and found enhancement of de novo fatty acid synthesis during 2-heptanone production. Interestingly, a homolog of yneP' existed in the non-pathogenic species Bacillus subtilis 168, a close relative of B. nematocida B16 that was unable to lure worms, but GC-MS assay showed no 2-heptanone production. However, overexpression of yneP' from B. subtilis in both heterologous and homologous systems demonstrated that it was not a pseudogene. The transcriptional analysis between those two genes had few differences under the same conditions. It was further shown that the failure to detect 2-heptanone in B. subtilis 168 was at least partly due to its conversion into 6-methyl-2-heptanone by methylation. Our study revealed methylketone biosynthesis of Bacillus species, and provided a co-evolution paradigm of second metabolites during the interactions between pathogenic/non-pathogenic bacteria and host.

Bedside Measurement of Volatile Organic Compounds in the Atmosphere of Neonatal Incubators Using Ion Mobility Spectrometry

Background: Early and non-invasive diagnosis of common diseases is of great importance in the care of preterm infants. We hypothesized that volatile organic compounds (VOC) can be successfully measured in the neonatal incubator atmosphere. Methods: This is a feasibility study to investigate whether the discrimination of occupied and unoccupied neonatal incubators is possible by bedside measurement of volatile organic compounds (VOCs) on the neonatal intensive care unit. VOC profiles were measured in the incubator air using ion mobility spectrometry coupled to multi-capillary columns (BreathDiscovery B&S Analytik GmbH, Dortmund, Germany). Results: Seventeen incubators occupied by preterm infants (50 measurements) and nine unoccupied neonatal incubators were sampled, using 37 room air measurements as controls. Three VOC signals that allow the discrimination between occupied and unoccupied incubators were identified. The best discrimination was reached by peak P20 exhibiting a sensitivity, specificity, positive predictive value and negative predictive value of 94.0, 88.9, 97.3, and 72.3%, respectively. Use of a decision tree improved these values to 100.0, 88.9, 98.0, and 100.0%, respectively. Discussion: A bedside method that allows the characterization of VOC profiles in the neonatal incubator atmosphere using ion mobility spectrometry was established. Occupied and unoccupied incubators could be discriminated by characterizing VOC profiles. This technique has the potential to yield results within minutes. Thus, future studies are recommended to test the hypothesis that VOCs within neonatal incubators are useful biomarkers for non-invasive diagnostics in preterm neonates.

Profiling of headspace volatiles from Escherichia coli cultures using silicone-based sorptive media and thermal desorption GC-MS

Headspace sorptive extraction technique using silicone based sorptive media coated stir bars is used for the first time here to extract, identify, and quantify heavy volatile organic compounds present in Escherichia coli culture headspace. Detection of infection presence is largely accomplished in laboratories through physical sampling and subsequent growth of cultures for biochemical testing. The use of volatile biomarkers released from pathogens as indicators for pathogenic presence can vastly reduce the time needed whilst improving the success rates for infection detection. To validate this, by using a contactless headspace sorptive extraction technique, the volatile compounds released from E. coli, grown in vitro, have been extracted and identified. Two different sorptive media for extracting these headspace volatiles were compared in this study and the identified volatiles were quantified. The large phase volume and wider retention of this sorptive technique compared to traditional sampling approach enabled preconcentration and collection of wider range of volatiles towards developing an extensive database of such heavy volatiles associated with E. coli. This supplements the existing data of potential bacterial markers and use of internal standards in these tests allows semi-quantitative estimation of these compounds towards the development and optimization of novel pathogen sensing devices.

Assessment of a combined gas chromatography mass spectrometer sensor system for detecting biologically relevant volatile compounds

There have been a number of studies in which metal oxide sensors (MOS) have replaced conventional analytical detectors in gas chromatography systems. However, despite the use of these instruments in a range of applications including breath research the sensor responses (i.e. resistance changes w.r.t. concentration of VCs) remain largely unreported. This paper addresses that issue by comparing the response of a metal oxide sensor directly with a mass spectrometer (MS), whereby both detectors are interfaced to the same GC column using an s-swafer. It was demonstrated that the sensitivity of an in-house fabricated ZnO/SnO₂ thick film MOS was superior to a modern MS for the detection of a wide range of volatile compounds (VCs) of different functionalities and masses. Better techniques for detection and quantification of these VCs is valuable, as many of these compounds are commonly reported throughout the scientific literature. This is also the first published report of a combined GC-MS sensor system. These two different detector technologies when combined, should enhance discriminatory abilities to aid disease diagnoses using volatiles from e.g. breath, and bodily fluids. Twenty-nine chemical standards have been tested using solid phase micro-extraction; 25 of these compounds are found on human breath. In all but two instances the sensor exhibited the same or superior limit of detection compared to the MS. Twelve stool samples from healthy participants were analysed; the sensor detected, on average 1.6 peaks more per sample than the MS. Similarly, analysing the headspace of E. coli broth cultures the sensor detected 6.9 more peaks per sample versus the MS. This greater sensitivity is primarily a function of the superior limits of detection of the metal oxide sensor. This shows that systems based on the combination of chromatography systems with solid state sensors shows promise for a range of applications.

The effect of growth medium on an Escherichia coli pathway mirrored into GC/MS profiles

Escherichia coli (E. coli) is a Gram-negative coliform bacterium that is commonly found in the lower intestine of warm-blooded organisms. Most of the strains are harmless but some serotypes are pathogenic, meaning they can cause illness, either diarrhea or illness outside the intestinal tract. The aim of this work is to assess which components are generated for the purpose of E. coli target analysis. In this study, we intend to emphasize the importance of cultivability and to prove that growth media plays a crucial role in bacteria growth. To do this, E. coli was cultivated in three different growth mediums: (a) trypcase soy broth (TSB), (b) Mueller Hinton (MH), and (c) minimal salts (M9) enriched with glucose, respectively. Solid phase micro extraction was used as a sampling method, followed by gas chromatography-mass spectrometry for subsequent analysis. The relevant microbial volatile organic compounds (MVOCs) released in the headspace over the cultures of the E. coli bacteria and the afferent metabolic processes that occur in order to generate these compounds are presented in this work. The characteristic volatile compounds found in E. coli strain emissions were indole, phenylethyl alcohol and a series of esters when it was grown in TSB. Different pyrazines were found (pyrazine, 2-ethyl-3,5-dimethyl-, pyrazine, 2,5-dimethyl- and pyrazine, trimethyl-) when it was cultivated in MH. Long-chain alcohols such as 2-pentadecanol, 9-tetradecen-1-ol and 11-hexadecenol occurred in M9. Dimethyl disulfide, dimethyl trisulfide and a consistent number of alcohols and ketones were observed for E. coli cultivated in all three growth mediums. The occurrence and biosynthesis of these MVOCs clearly denote that the growth media used plays a crucial role in bacterial cultivation. The biomarker chemicals documented from this work may ultimately be used to identify bacterial infections by analyzing exhaled breath.

Detection of gas traces using semiconductor sensors, ion mobility spectrometry, and mass spectrometry

This article deals with the state-of-the-art instrumentation and application in the field of solid state gas sensorics, ion mobility spectrometry and mass-spectrometry-related research for the detection and measurements of low gas and vapor concentrations. The advantages and disadvantages of gas-analytical devices and systems are discussed, as well as the possibilities of its complex and/or complementary applications. Ion mobility spectrometry-mass spectrometry and subsequent techniques based on solid-state gas sensors are proposed for planned medical study.

Mass spectrometric techniques for the analysis of volatile organic compounds emitted from bacteria

Bacteria are the main cause of many human diseases. Typical bacterial identification methods, for example culture-based, serological and genetic methods, are time-consuming, delaying the potential for an early and accurate diagnosis and the appropriate subsequent treatment. Nevertheless, there is a stringent need for in situ tests that are rapid, noninvasive and sensitive, which will greatly facilitate timely treatment of the patients. This review article presents volatile organic metabolites emitted from various micro-organism strains responsible for common bacterial infections in humans. Additionally, the manuscript shows the application of different analytical techniques for fast bacterial identification. Details of these techniques are given, which focuses on their advantages and drawbacks in using for volatile organic components analysis.

Discrimination of bacteria by rapid sensing their metabolic volatiles using an aspiration-type ion mobility spectrometer (a-IMS) and gas chromatography-mass spectrometry GC-MS

The objective of our study was to investigate whether one may quickly and reliably discriminate different microorganism strains by direct monitoring of the headspace atmosphere above their cultures. Headspace samples above a series of in vitro bacterial cultures were directly interrogated using an aspiration type ion mobility spectrometer (a-IMS), which produced distinct profiles ("fingerprints") of ion currents generated simultaneously by the detectors present inside the ion mobility cell. Data processing and analysis using principal component analysis showed net differences in the responses produced by volatiles emitted by various bacterial strains. Fingerprint assignments were conferred on the basis of product ion mobilities; ions of differing size and mass were deflected in a different degree upon their introduction of a transverse electric field, impacting finally on a series of capacitors (denominated as detectors, or channels) placed in a manner analogous to sensor arrays. Three microorganism strains were investigated - Escherichia coli, Bacillus subtilis and Staphylococcus aureus; all strains possess a relatively low pathogenic character. Samples of air with a 5 cm³ volume from the headspace above the bacterial cultures in agar growth medium were collected using a gas-tight chromatographic syringe and injected inside the closed-loop pneumatic circuit of the breadboard a-IMS instrument model ChemPro-100i (Environics Oy, Finland), at a distance of about 1 cm from the ionization source. The resulting chemical fingerprints were produced within two seconds from the moment of injection. The sampling protocol involved to taking three replicate samples from each of 10 different cultures for a specific strain, during a total period of 72 h after the initial incubation - at 24, 48 and 72 h, respectively. Principal component analysis (PCA) was used to discriminate between the IMS fingerprints. PCA was found to successfully discriminate between bacteria at three levels in the experimental campaign: 1) between blank samples from growth medium and samples from bacterial cultures, 2) between samples from different bacterial strains, and 3) between time evolutions of headspace samples from the same bacterial strain over the 3-day sampling period. Consistent classification between growth medium samples and growth medium inoculated with bacteria was observed in both positive and negative detection/ionization modes. In parallel, headspace air samples of 1 dm³ were collected from each bacterial culture and loaded onto Tenax™-Carbograph desorption tubes, using a custom built sampling unit based on a portable sampling pump. One sample was taken for each of 10 different cultures of a strain, at 24, 48 and 72 h after the initial incubation. These adsorption tubes were subsequently analyzed using thermal desorption - gas chromatography - mass spectrometry (TD-GC-MS). This second dataset was intended to produce a qualitative analysis of the volatiles present in the headspace above the bacterial cultures.

Target identification of volatile metabolites to allow the differentiation of lactic acid bacteria by gas chromatography-ion mobility spectrometry

The purpose of this work was to study the potential of gas chromatography-ion mobility spectrometry (GC-IMS) to differentiate lactic acid bacteria (LAB) through target identification and fingerprints of volatile metabolites. The LAB selected were used as reference strains for their influence in the flavour of cheese. The four strains of LAB can be distinguished by the fingerprints generated by the volatile organic compounds (VOCs) emitted. 2-butanone, 2-pentanone, 2-heptanone and 3-methyl-1-butanol were identified as relevant VOCs for Lactobacillus casei and Lactobacillus paracasei subsp. paracasei. 2-Butanone and 3-methyl-1-butanol were identified in Lactococcus lactis subsp. lactis and Lactococcus cremoris subsp. cremoris. The IMS signals monitoring during a 24-30h period showed the growth of the LAB in vitro. The results demonstrated that GC-IMS is a useful technology for bacteria recognition and also for screening the aromatic potential of new isolates of LAB.

Fast detection of coliform bacteria by means of gas chromatography-differential mobility spectrometry

In this study, we demonstrate that the combination of an enzymatic method (based on Colilert-18 medium) and gas chromatography-differential mobility spectrometry (GC-DMS) can reduce the time required for detection of coliform bacteria (including Escherichia coli) from 18 to 2.5 h. The presented method includes the incubation (~2.5 h) of the sample containing coliform bacteria in Colilert-18 medium. The incubation time of 2.5 h is required for the activation of the β-galactosidase enzyme. Produced during the incubation biomarker o-nitrophenol (ONP) can be detected by means of GC-DMS within just 200 s. The detection limit for ONP was 45 ng (on-column). The method developed in this work provides significantly shorter analysis time compared with standard methods, and can be potentially adapted to the field conditions. Therefore, this method is a promising tool for an early detection of coliform bacteria (including E. coli). Graphical Abstract Fast detection of coliform bacteria by means of GC-DMS.

Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles

Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs with similar expression behavior over most patient breath samples and/or groups of patients with a similar VOC intensity pattern. This enables the discovery of dependencies between metabolites. On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking. On the other hand, we may also identify VOCs associated with disease subtypes or concomitant diseases. Carotta is an open source software with an intuitive graphical user interface promoting data handling, analysis and visualization. The back-end is designed to be modular, allowing for easy extensions with plugins in the future, such as new clustering methods and statistics. It does not require much prior knowledge or technical skills to operate. We demonstrate its power and applicability by means of one artificial dataset. We also apply Carotta exemplarily to a real-world example dataset on chronic obstructive pulmonary disease (COPD). While the artificial data are utilized as a proof of concept, we will demonstrate how Carotta finds candidate markers in our real dataset associated with confounders rather than the primary disease (COPD) and bronchial carcinoma (BC). Carotta is publicly available at http://carotta.compbio.sdu.dk [1].

Volatile Organic Compounds during Inflammation and Sepsis in Rats

Background:

Multicapillary column ion-mobility spectrometry (MCC-IMS) may identify volatile components in exhaled gas. The authors therefore used MCC-IMS to evaluate exhaled gas in a rat model of sepsis, inflammation, and hemorrhagic shock.

Methods:

Male Sprague–Dawley rats were anesthetized and ventilated via tracheostomy for 10 h or until death. Sepsis was induced by cecal ligation and incision in 10 rats; a sham operation was performed in 10 others. In 10 other rats, endotoxemia was induced by intravenous administration of 10 mg/kg lipopolysaccharide. In a final 10 rats, hemorrhagic shock was induced to a mean arterial pressure of 35 ± 5 mmHg. Exhaled gas was analyzed with MCC-IMS, and volatile compounds were identified using the BS-MCC/IMS-analytes database (Version 1209; B&S Analytik, Dortmund, Germany).

Results:

All sham animals survived the observation period, whereas mean survival time was 7.9 h in the septic animals, 9.1 h in endotoxemic animals, and 2.5 h in hemorrhagic shock. Volatile compounds showed statistically significant differences in septic and endotoxemic rats compared with sham rats for 3-pentanone and acetone. Endotoxic rats differed significantly from sham for 1-propanol, butanal, acetophenone, 1,2-butandiol, and 2-hexanone. Statistically significant differences were observed between septic and endotoxemic rats for butanal, 3-pentanone, and 2-hexanone. 2-Hexanone differed from all other groups in the rats with shock.

Conclusions:

Breath analysis of expired organic compounds differed significantly in septic, inflammation, and sham rats. MCC-IMS of exhaled breath deserves additional study as a noninvasive approach for distinguishing sepsis from inflammation.

Exhaled breath analysis for lung cancer detection using ion mobility spectrometry

BACKGROUND

Conventional methods for lung cancer detection including computed tomography (CT) and bronchoscopy are expensive and invasive. Thus, there is still a need for an optimal lung cancer detection technique.

METHODS

The exhaled breath of 50 patients with lung cancer histologically proven by bronchoscopic biopsy samples (32 adenocarcinomas, 10 squamous cell carcinomas, 8 small cell carcinomas), were analyzed using ion mobility spectrometry (IMS) and compared with 39 healthy volunteers. As a secondary assessment, we compared adenocarcinoma patients with and without epidermal growth factor receptor (EGFR) mutation.

RESULTS

A decision tree algorithm could separate patients with lung cancer including adenocarcinoma, squamous cell carcinoma and small cell carcinoma. One hundred-fifteen separated volatile organic compound (VOC) peaks were analyzed. Peak-2 noted as n-Dodecane using the IMS database was able to separate values with a sensitivity of 70.0% and a specificity of 89.7%. Incorporating a decision tree algorithm starting with n-Dodecane, a sensitivity of 76% and specificity of 100% was achieved. Comparing VOC peaks between adenocarcinoma and healthy subjects, n-Dodecane was able to separate values with a sensitivity of 81.3% and a specificity of 89.7%. Fourteen patients positive for EGFR mutation displayed a significantly higher n-Dodecane than for the 14 patients negative for EGFR (p<0.01), with a sensitivity of 85.7% and a specificity of 78.6%.

CONCLUSION

In this prospective study, VOC peak patterns using a decision tree algorithm were useful in the detection of lung cancer. Moreover, n-Dodecane analysis from adenocarcinoma patients might be useful to discriminate the EGFR mutation.

Multi-capillary column-ion mobility spectrometry of volatile metabolites emitted by Saccharomyces cerevisiae

Volatile organic compounds (VOCs) produced during microbial fermentations determine the flavor of fermented food and are of interest for the production of fragrances or food additives. However, the microbial synthesis of these compounds from simple carbon sources has not been well investigated so far. Here, we analyzed the headspace over glucose minimal salt medium cultures of Saccharomyces cerevisiae using multi-capillary column-ion mobility spectrometry (MCC-IMS). The high sensitivity and fast data acquisition of the MCC-IMS enabled online analysis of the fermentation off-gas and 19 specific signals were determined. To four of these volatile compounds, we could assign the metabolites ethanol, 2-pentanone, isobutyric acid, and 2,3-hexanedione by MCC-IMS measurements of pure standards and cross validation with thermal desorption-gas chromatography-mass spectrometry measurements. Despite the huge biochemical knowledge of the biochemistry of the model organism S. cerevisiae, only the biosynthetic pathways for ethanol and isobutyric acid are fully understood, demonstrating the considerable lack of research of volatile metabolites. As monitoring of VOCs produced during microbial fermentations can give valuable insight into the metabolic state of the organism, fast and non-invasive MCC-IMS analyses provide valuable data for process control.

Identification of microorganisms based on headspace analysis of volatile organic compounds by gas chromatography-mass spectrometry

The identification of specific volatile organic compounds (VOCs) produced by microorganisms may assist in developing a fast and accurate methodology for the determination of pulmonary bacterial infections in exhaled air. As a first step, pulmonary bacteria were cultured and their headspace analyzed for the total amount of excreted VOCs to select those compounds which are exclusively associated with specific microorganisms. Development of a rapid, noninvasive methodology for identification of bacterial species may improve diagnostics and antibiotic therapy, ultimately leading to controlling the antibiotic resistance problem. Two hundred bacterial headspace samples from four different microorganisms (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumoniae) were analyzed by gas chromatography-mass spectrometry to detect a wide array of VOCs. Statistical analysis of these volatiles enabled the characterization of specific VOC profiles indicative for each microorganism. Differences in VOC abundance between the bacterial types were determined using ANalysis of VAriance-principal component analysis (ANOVA-PCA). These differences were visualized with PCA. Cross validation was applied to validate the results. We identified a large number of different compounds in the various headspaces, thus demonstrating a highly significant difference in VOC occurrence of bacterial cultures compared to the medium and between the cultures themselves. Additionally, a separation between a methicillin-resistant and a methicillin-sensitive isolate of S. aureus could be made due to significant differences between compounds. ANOVA-PCA analysis showed that 25 VOCs were differently profiled across the various microorganisms, whereas a PCA score plot enabled the visualization of these clear differences between the bacterial types. We demonstrated that identification of the studied microorganisms, including an antibiotic susceptible and resistant S. aureus substrain, is possible based on a selected number of compounds measured in the headspace of these cultures. These in vitro results may translate into a breath analysis approach that has the potential to be used as a diagnostic tool in medical microbiology.

Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis

We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.

Volatile metabolites of pathogens: a systematic review

Ideally, invading bacteria are detected as early as possible in critically ill patients: the strain of morbific pathogens is identified rapidly, and antimicrobial sensitivity is known well before the start of new antimicrobial therapy. Bacteria have a distinct metabolism, part of which results in the production of bacteria-specific volatile organic compounds (VOCs), which might be used for diagnostic purposes. Volatile metabolites can be investigated directly in exhaled air, allowing for noninvasive monitoring. The aim of this review is to provide an overview of VOCs produced by the six most abundant and pathogenic bacteria in sepsis, including Staphylococcus aureus, Streptococcus pneumoniae, Enterococcus faecalis, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli. Such VOCs could be used as biological markers in the diagnostic approach of critically ill patients. A systematic review of existing literature revealed 31 articles. All six bacteria of interest produce isopentanol, formaldehyde, methyl mercaptan, and trimethylamine. Since humans do not produce these VOCs, they could serve as biological markers for presence of these pathogens. The following volatile biomarkers were found for identification of specific strains: isovaleric acid and 2-methyl-butanal for Staphylococcus aureus; 1-undecene, 2,4-dimethyl-1-heptane, 2-butanone, 4-methyl-quinazoline, hydrogen cyanide, and methyl thiocyanide for Pseudomonas aeruginosa; and methanol, pentanol, ethyl acetate, and indole for Escherichia coli. Notably, several factors that may effect VOC production were not controlled for, including used culture media, bacterial growth phase, and genomic variation within bacterial strains. In conclusion, VOCs produced by bacteria may serve as biological markers for their presence. Goal-targeted studies should be performed to identify potential sets of volatile biological markers and evaluate the diagnostic accuracy of these markers in critically ill patients.

Detection and validation of volatile metabolic patterns over different strains of two human pathogenic bacteria during their growth in a complex medium using multi-capillary column-ion mobility spectrometry (MCC-IMS)

Headspace analyses over microbial cultures using multi-capillary column-ion mobility spectrometry (MCC-IMS) could lead to a faster, safe and cost-effective method for the identification of pathogens. Recent studies have shown that MCC-IMS allows identification of bacteria and fungi, but no information is available from when on during their growth a differentiation between bacteria is possible. Therefore, we analysed the headspace over human pathogenic reference strains of Escherichia coli and Pseudomonas aeruginosa at four time points during their growth in a complex fluid medium. In order to validate our findings and to answer the question if the results of one bacterial strain can be transferred to other strains of the same species, we also analysed the headspace over cultures from isolates of random clinical origin. We detected 19 different volatile organic compounds (VOCs) that appeared or changed their signal intensity during bacterial growth. These included six VOCs exclusively changing over E. coli cultures and seven exclusively changing over P. aeruginosa cultures. Most changes occurred in the late logarithmic or static growth phases. We did not find differences in timing or trends in signal intensity between VOC patterns of different strains of one species. Our results show that differentiation of human pathogenic bacteria by headspace analyses using MCC-IMS technology is best possible during the late phases of bacterial growth. Our findings also show that VOC patterns of a bacterial strain can be transferred to other strains of the same species.

Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art

Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain.