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Vasiliki Besa,1 Helmut Teschler,2 Isabella Kurth,1 Amir Maqbul Khan,3 Paul Zarogoulidis,4 Joerg Ingo Baumbach,5 Urte Sommerwerck,2 Lutz Freitag,1 Kaid Darwiche1 1Department of Interventional Pneumology, Ruhrlandklinik, West German Lung Center, University Hospital, University Duisburg-Essen, Essen, Germany; 2Department of Pneumology, Ruhrlandklinik, University Hospital Essen, University of Essen-Duisburg, Essen, Germany; 3Division of Thoracic Surgery, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON, Canada; 4Oncology Unit, Pulmonary Department, “G Papanikolaou” General Hospital, Aristotle University of Thessaloniki, Greece; 5Faculty of Applied Chemistry, Reutlingen University, Reutlingen, Germany Abstract: Chronic obstructive pulmonary disease (COPD) is a chronic airway inflammatory disease characterized by incompletely reversible airway obstruction. This clinically heterogeneous group of patients is characterized by different phenotypes. Spirometry and clinical parameters, such as severity of dyspnea and exacerbation frequency, are used to diagnose and assess the severity of COPD. The purpose of this study was to investigate whether volatile organic compounds (VOCs) could be detected in the exhaled breath of patients with COPD and whether these VOCs could distinguish COPD patients from healthy subjects. Moreover, we aimed to investigate whether VOCs could be used as biomarkers for classifying patients into different subgroups of the disease. Ion mobility spectrometry was used to detect VOCs in the exhaled breath of COPD patients. One hundred and thirty-seven peaks were found to have a statistically significant difference between the COPD group and the combined healthy smokers and nonsmoker group. Six of these VOCs were found to correctly discriminate COPD patients from healthy controls with an accuracy of 70%. Only 15 peaks were found to be statistically different between healthy smokers and healthy nonsmokers. Furthermore, by determining the cutoff levels for each VOC peak, it was possible to classify the COPD patients into breathprint subgroups. Forced expiratory volume in 1 second, body mass index, and C-reactive protein seem to play a role in the discrepancies observed in the different breathprint subgroups. Keywords: breath analysis, COPD, ion mobility spectrometry, volatile organic compounds

Ion Mobility Spectrometry (IMS) is a widely used and 'well-known' technique of ion separation in the gaseous phase based on the differences in ion mobilities under an electric field. All IMS instruments operate with an electric field that provides space separation, but some IMS instruments also operate with a drift gas flow that provides also a temporal separation. In this review we will summarize the current IMS instrumentation. IMS techniques have received an increased interest as new instrumentation and have become available to be coupled with mass spectrometry (MS). For each of the eight types of IMS instruments reviewed it is mentioned whether they can be hyphenated with MS and whether they are commercially available. Finally, out of the described devices, the six most-consolidated ones are compared. The current review article is followed by a companion review article which details the IMS hyphenated techniques (mainly gas chromatography and mass spectrometry) and the factors that make the data from an IMS device change as a function of device parameters and sampling conditions. These reviews will provide the reader with an insightful view of the main characteristics and aspects of the IMS technique.

Ion Mobility Spectrometry (IMS) is a widely used and 'well-known' technique of ion separation in the gaseous phase based on the differences of ion mobilities under an electric field. This technique has received increased interest over the last several decades as evidenced by the pace and advances of new IMS devices available. In this review we explore the hyphenated techniques that are used with IMS, specifically mass spectrometry as an identification approach and a multi-capillary column as a pre-separation approach. Also, we will pay special attention to the key figures of merit of the ion mobility spectrum and how data sets are treated, and the influences of the experimental parameters on both conventional drift time IMS (DTIMS) and miniaturized IMS also known as high Field Asymmetric IMS (FAIMS) in the planar configuration. The present review article is preceded by a companion review article which details the current instrumentation and contains the sections that configure both conventional DTIMS and FAIMS devices. These reviews will give the reader an insightful view of the main characteristics and aspects of the IMS technique.

Introduction: Early diagnosis of infections and sepsis is essential as adequate therapy improves the outcome. Unfortunately, current diagnostics are invasive and time-consuming, making diagnosis difficult, especially in neonatology. Novel non-invasive analytical methods might be suitable to detect an infection at an early stage and might even allow identification of the pathogen. Our aim is to identify specific profiles of volatile organic compounds (VOCs) of bacterial species., Methods: Using multicapillary column-coupled ion mobility spectrometry (MCC/IMS), we performed headspace measurements of bacterial cultures from skin and anal swabs of premature infants obtained during weekly screening for bacterial colonization according to KRINKO. We analyzed 25 Klebsiella pneumoniae (KP) cultures on MacConkey (MC) agar plates, 25 Klebsiella oxytoca (KO) cultures on MC agar and 25 bare MC agar plates as a control group., Results: Using MCC/IMS, we identified a total of 159 VOC peaks. 85 peaks allowed discriminating KP and bare MC agar plates, and 51 peaks comparing KO and bare MC agar plates and 6 peaks between KP and KO ( significance level of p  < 0.05 after Bonferroni post hoc analysis) , respectively. Peaks P51 ( n-Decane ) and P158 (Phenylethyl Alcohol), showed the best sensitivity/specificity/ positive predictive value/negative predictive value of 99.9% each ( p  < 0.001) for KP. P158 showed the best sensitivity/specificity/positive predictive value/negative predictive value of 99.9% each ( p  < 0.001) for KO . Comparing KP and KO, best differentiation was enabled using peaks P72, P97 and P16 with sensitivity/specificity/positive predictive value/negative predictive value of 76.0%, 84.0%, 82.6%, 77.8%, respectively ( p  < 0.05)., Discussion: We developed a method for the analysis of VOC profiles of bacteria. Using MCC/IMS, we demonstrated that VOCs derived from bacteria are clearly distinguishable from a bare agar plate. Characteristic peaks obtained by MCC/IMS are particularly suitable for the species-specific identification and differentiation of KP and KO. Thus, MCC/IMS might be a useful tool for in vitro diagnostics. Future studies must clarify whether similar patterns of VOCs can be detected in vivo in patients that are colonized or infected with KP or KO to enable rapid and accurate diagnosis of bacterial colonization., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor HN declared a past co-authorship with the author MB, EK, RW, JB, MZ, and SF., (© 2023 Bous, Tielsch, Papan, Kaiser, Weber, Baumbach, Becker, Zemlin and Goedicke-Fritz.)

The high sensitivity of the methods applied in breath analysis entails a high risk of detecting analytes that do not derive from endogenous production. Consequentially, it appears useful to have knowledge about the composition of inhaled air and to include alveolar gradients into interpretation. The current study aimed to standardise sampling procedures in breath analysis, especially with multicapillary column ion-mobility spectrometry (MCC-IMS), by applying a simultaneous registration of inhaled air and exhaled breath. A "double MCC-IMS" device, which for the first time allows simultaneous analysis of inhaled air and exhaled breath, was developed and tested in 18 healthy individuals. For this, two BreathDiscovery instruments were coupled with each other. Measurements of inhaled air and exhaled breath in 18 healthy individuals (mean age 46±10.9 years; nine men, nine women) identified 35 different volatile organic compounds (VOCs) for further analysis. Not all of these had positive alveolar gradients and could be regarded as endogenous VOCs: 16 VOCs had a positive alveolar gradient in mean; 19 VOCs a negative one. 12 VOCs were positive in >12 of the healthy subjects. For the first time in our understanding, a method is described that enables simultaneous measurement of inhaled air and exhaled breath. This facilitates the calculation of alveolar gradients and selection of endogenous VOCs for exhaled breath analysis. Only a part of VOCs in exhaled breath are truly endogenous VOCs. The observation of different and varying polarities of the alveolar gradients needs further analysis., Competing Interests: Conflict of interest: M. Westhoff has nothing to disclose. M. Friedrich is member of the staff of Braun Melsungen AG, Branch Dortmund, Center of Competence Breath Analysis, Dortmund. J.I. Baumbach is member of the staff of Braun Melsungen AG, Branch Dortmund, Center of Competence Breath Analysis, Dortmund., (Copyright ©The authors 2022.)

Rapid, high-throughput diagnostic tests are essential to decelerate the spread of the novel coronavirus disease 2019 (COVID-19) pandemic. While RT-PCR tests performed in centralized laboratories remain the gold standard, rapid point-of-care antigen tests might provide faster results. However, they are associated with markedly reduced sensitivity. Bedside breath gas analysis of volatile organic compounds detected by ion mobility spectrometry (IMS) may enable a quick and sensitive point-of-care testing alternative. In this proof-of-concept study, we investigated whether gas analysis by IMS can discriminate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from other respiratory viruses in an experimental set-up. Repeated gas analyses of air samples collected from the headspace of virus-infected in vitro cultures were performed for 5 days. A three-step decision tree using the intensities of four spectrometry peaks correlating to unidentified volatile organic compounds allowed the correct classification of SARS-CoV-2, human coronavirus-NL63, and influenza A virus H1N1 without misassignment when the calculation was performed with data 3 days post infection. The forward selection assignment model allowed the identification of SARS-CoV-2 with high sensitivity and specificity, with only one of 231 measurements (0.43%) being misclassified. Thus, volatile organic compound analysis by IMS allows highly accurate differentiation of SARS-CoV-2 from other respiratory viruses in an experimental set-up, supporting further research and evaluation in clinical studies., (© 2021. The Author(s).)

Background: Histologic chorioamnionitis is only diagnosed postnatally which prevents interventions. We hypothesized that volatile organic compounds (VOCs) in the amniotic fluid might be useful biomarkers for chorioamnionitis and that VOC profiles differ between amnionitis of different origins. Methods: Time-mated ewes received intra-amniotic injections of media or saline (controls), or live Ureaplasma parvum serovar 3 (Up) 14, 7 or 3d prior to c-section at day 124 gestational age (GA). 100 μg recombinant ovine IL-1α was instilled at 7, 3 or 1d prior to delivery. Headspace VOC profiles were measured from amniotic fluids at birth using ion mobility spectrometer coupled with multi-capillary columns. Results: 127 VOC peaks were identified. 27 VOCs differed between samples from controls and Up- or IL-1α induced amnionitis. The best discrimination between amnionitis by Up vs. IL-1α was reached by 2-methylpentane, with a sensitivity/specificity of 96/95% and a positive predictive value/negative predictive values of 96 and 95%. The concentration of 2-methylpentane in VOCs peaked 7d after intra-amniotic instillation of Up. Discussion: We established a novel method to study headspace VOC profiles of amniotic fluids. VOC profiles may be a useful tool to detect and to assess the duration of amnionitis induced by Up. 2-methylpentane was previously described in the exhalate of women with pre-eclampsia and might be a volatile biomarker for amnionitis. Amniotic fluids analyzed by ion mobility spectrometry coupled with multi-capillary columns may provide bedside diagnosis of amnionitis and understanding inflammatory mechanisms during pregnancy., Competing Interests: TR is an employee of CSL Behring. JB is an employee of B. Braun Melsungen AG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Goedicke-Fritz, Werner, Niemarkt, Wolfs, Baumbach, Kemp, Jobe, Rogosch, Bous, Kaiser, Stutz, Meyer, Maier, Koczulla, Spiller, Kramer and Zemlin.)

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.

Motivation: Disease classification from molecular measurements typically requires an analysis pipeline from raw noisy measurements to final classification results. Multi capillary column-ion mobility spectrometry (MCC-IMS) is a promising technology for the detection of volatile organic compounds in the air of exhaled breath. From raw measurements, the peak regions representing the compounds have to be identified, quantified, and clustered across different experiments. Currently, several steps of this analysis process require manual intervention of human experts. Our goal is to identify a fully automatic pipeline that yields competitive disease classification results compared to an established but subjective and tedious semi-manual process., Method: We combine a large number of modern methods for peak detection, peak clustering, and multivariate classification into analysis pipelines for raw MCC-IMS data. We evaluate all combinations on three different real datasets in an unbiased cross-validation setting. We determine which specific algorithmic combinations lead to high AUC values in disease classifications across the different medical application scenarios., Results: The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace-operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology.

Background: Alzheimer's disease (AD) is diagnosed based upon medical history, neuropsychiatric examination, cerebrospinal fluid analysis, extensive laboratory analyses and cerebral imaging. Diagnosis is time consuming and labour intensive. Parkinson's disease (PD) is mainly diagnosed on clinical grounds., Objective: The primary aim of this study was to differentiate patients suffering from AD, PD and healthy controls by investigating exhaled air with the electronic nose technique. After demonstrating a difference between the three groups the secondary aim was the identification of specific substances responsible for the difference(s) using ion mobility spectroscopy. Thirdly we analysed whether amyloid beta (Aβ) in exhaled breath was causative for the observed differences between patients suffering from AD and healthy controls., Methods: We employed novel pulmonary diagnostic tools (electronic nose device/ion-mobility spectrometry) for the identification of patients with neurodegenerative diseases. Specifically, we analysed breath pattern differences in exhaled air of patients with AD, those with PD and healthy controls using the electronic nose device (eNose). Using ion mobility spectrometry (IMS), we identified the compounds responsible for the observed differences in breath patterns. We applied ELISA technique to measure Aβ in exhaled breath condensates., Results: The eNose was able to differentiate between AD, PD and HC correctly. Using IMS, we identified markers that could be used to differentiate healthy controls from patients with AD and PD with an accuracy of 94%. In addition, patients suffering from PD were identified with sensitivity and specificity of 100%. Altogether, 3 AD patients out of 53 participants were misclassified. Although we found Aβ in exhaled breath condensate from both AD and healthy controls, no significant differences between groups were detected., Conclusion: These data may open a new field in the diagnosis of neurodegenerative disease such as Alzheimer's disease and Parkinson's disease. Further research is required to evaluate the significance of these pulmonary findings with respect to the pathophysiology of neurodegenerative disorders.

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].

The analysis of exhaled metabolites has become a promising field of research in recent decades. Several volatile organic compounds reflecting metabolic disturbance and nutrition status have even been reported. These are particularly important for long-term measurements, as needed in medical research for detection of disease progression and therapeutic efficacy. In this context, it has become urgent to investigate the effect of fasting and glucose treatment for breath analysis. In the present study, we used a model of ventilated rats that fasted for 12 h prior to the experiment. Ten rats per group were randomly assigned for continuous intravenous infusion without glucose or an infusion including 25 mg glucose per 100 g per hour during an observation period of 12 h. Exhaled gas was analysed using multicapillary column ion-mobility spectrometry. Analytes were identified by the BS-MCC/IMS database (version 1209; B & S Analytik, Dortmund, Germany). Glucose infusion led to a significant increase in blood glucose levels (p < 0.05 at 4 h and thereafter) and cardiac output (p < 0.05 at 4 h and thereafter). During the observation period, 39 peaks were found collectively. There were significant differences between groups in the concentration of ten volatile organic compounds: p < 0.001 at 4 h and thereafter for isoprene, cyclohexanone, acetone, p-cymol, 2-hexanone, phenylacetylene, and one unknown compound, and p < 0.001 at 8 h and thereafter for 1-pentanol, 1-propanol, and 2-heptanol. Our results indicate that for long-term measurement, fasting and the withholding of glucose could contribute to changes of volatile metabolites in exhaled air.

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.

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.

Online measurement of drug concentrations in patient's breath is a promising approach for individualized dosage. A direct transfer from breath- to blood-concentrations is not possible. Measured exhaled concentrations are following the blood-concentration with a delay in non-steady-state situations. Therefore, it is necessary to integrate the breath-concentration into a pharmacological model. Two different approaches for pharmacokinetic modelling are presented. Usually a 3-compartment model is used for pharmacokinetic calculations of blood concentrations. This 3-compartment model is extended with a 2-compartment model based on the first compartment of the 3-compartment model and a new lung compartment. The second approach is to calculate a time delay of changes in the concentration of the first compartment to describe the lung-concentration. Exemplarily both approaches are used for modelling of exhaled propofol. Based on time series of exhaled propofol measurements using an ion-mobility-spectrometer every minute for 346 min a correlation of calculated plasma and the breath concentration was used for modelling to deliver R(2) = 0.99 interdependencies. Including the time delay modelling approach the new compartment coefficient k(e0lung) was calculated to k(e0lung) = 0.27 min(-1) with R(2) = 0.96. The described models are not limited to propofol. They could be used for any kind of drugs, which are measurable in patient's breath.

Background: An ion mobility (IM) spectrometer coupled with a multi-capillary column (MCC) measures volatile organic compounds (VOCs) in the air or in exhaled breath. This technique is utilized in several biotechnological and medical applications. Each peak in an MCC/IM measurement represents a certain compound, which may be known or unknown. For clustering and classification of measurements, the raw data matrix must be reduced to a set of peaks. Each peak is described by its coordinates (retention time in the MCC and reduced inverse ion mobility) and shape (signal intensity, further shape parameters). This fundamental step is referred to as peak extraction. It is the basis for identifying discriminating peaks, and hence putative biomarkers, between two classes of measurements, such as a healthy control group and a group of patients with a confirmed disease. Current state-of-the-art peak extraction methods require human interaction, such as hand-picking approximate peak locations, assisted by a visualization of the data matrix. In a high-throughput context, however, it is preferable to have robust methods for fully automated peak extraction., Results: We introduce PEAX, a modular framework for automated peak extraction. The framework consists of several steps in a pipeline architecture. Each step performs a specific sub-task and can be instantiated by different methods implemented as modules. We provide open-source software for the framework and several modules for each step. Additionally, an interface that allows easy extension by a new module is provided. Combining the modules in all reasonable ways leads to a large number of peak extraction methods. We evaluate all combinations using intrinsic error measures and by comparing the resulting peak sets with an expert-picked one., Conclusions: Our software PEAX is able to automatically extract peaks from MCC/IM measurements within a few seconds. The automatically obtained results keep up with the results provided by current state-of-the-art peak extraction methods. This opens a high-throughput context for the MCC/IM application field. Our software is available at http://www.rahmannlab.de/research/ims.

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors' results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications.

Over the last decade the evaluation of odors and vapors in human breath has gained more and more attention, particularly in the diagnostics of pulmonary diseases. Ion mobility spectrometry coupled with multi-capillary columns (MCC/IMS), is a well known technology for detecting volatile organic compounds (VOCs) in air. It is a comparatively inexpensive, non-invasive, high-throughput method, which is able to handle the moisture that comes with human exhaled air, and allows for characterizing of VOCs in very low concentrations. To identify discriminating compounds as biomarkers, it is necessary to have a clear understanding of the detailed composition of human breath. Therefore, in addition to the clinical studies, there is a need for a flexible and comprehensive centralized data repository, which is capable of gathering all kinds of related information. Moreover, there is a demand for automated data integration and semi-automated data analysis, in particular with regard to the rapid data accumulation, emerging from the high-throughput nature of the MCC/IMS technology. Here, we present a comprehensive database application and analysis platform, which combines metabolic maps with heterogeneous biomedical data in a well-structured manner. The design of the database is based on a hybrid of the entity-attribute-value (EAV) model and the EAV-CR, which incorporates the concepts of classes and relationships. Additionally it offers an intuitive user interface that provides easy and quick access to the platform&rsquo;s functionality: automated data integration and integrity validation, versioning and roll-back strategy, data retrieval as well as semi-automatic data mining and machine learning capabilities. The platform will support MCC/IMS-based biomarker identification and validation. The software, schemata, data sets and further information is publicly available at http://imsdb.mpi-inf.mpg.de.

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.

Exhaled air carries information on human health status. Ion mobility spectrometers combined with a multi-capillary column (MCC/IMS) is a well-known technology for detecting volatile organic compounds (VOCs) within human breath. This technique is relatively inexpensive, robust and easy to use in every day practice. However, the potential of this methodology depends on successful application of computational approaches for finding relevant VOCs and classification of patients into disease-specific profile groups based on the detected VOCs. We developed an integrated state-of-the-art system using sophisticated statistical learning techniques for VOC-based feature selection and supervised classification into patient groups. We analyzed breath data from 84 volunteers, each of them either suffering from chronic obstructive pulmonary disease (COPD), or both COPD and bronchial carcinoma (COPD + BC), as well as from 35 healthy volunteers, comprising a control group (CG). We standardized and integrated several statistical learning methods to provide a broad overview of their potential for distinguishing the patient groups. We found that there is strong potential for separating MCC/IMS chromatograms of healthy controls and COPD patients (best accuracy COPD vs CG: 94%). However, further examination of the impact of bronchial carcinoma on COPD/no-COPD classification performance is necessary (best accuracy CG vs COPD vs COPD + BC: 79%). We also extracted 20 high-scoring VOCs that allowed differentiating COPD patients from healthy controls. We conclude that these statistical learning methods have a generally high accuracy when applied to well-structured, medical MCC/IMS data.

Presently, 2 to 4 days elapse between sampling at infection suspicion and result of microbial diagnostics. This delay for the identification of pathogens causes quite often a late and/or inappropriate initiation of therapy for patients suffering from infections. Bad outcome and high hospitalization costs are the consequences of these currently existing limited pathogen identification possibilities. For this reason, we aimed to apply the innovative method multi-capillary column-ion mobility spectrometry (MCC-IMS) for a fast identification of human pathogenic bacteria by determination of their characteristic volatile metabolomes. We determined volatile organic compound (VOC) patterns in headspace of 15 human pathogenic bacteria, which were grown for 24 h on Columbia blood agar plates. Besides MCC-IMS determination, we also used thermal desorption-gas chromatography-mass spectrometry measurements to confirm and evaluate obtained MCC-IMS data and if possible to assign volatile compounds to unknown MCC-IMS signals. Up to 21 specific signals have been determined by MCC-IMS for Proteus mirabilis possessing the most VOCs of all investigated strains. Of particular importance is the result that all investigated strains showed different VOC patterns by MCC-IMS using positive and negative ion mode for every single strain. Thus, the discrimination of investigated bacteria is possible by detection of their volatile organic compounds in the chosen experimental setup with the fast and cost-effective method MCC-IMS. In a hospital routine, this method could enable the identification of pathogens already after 24 h with the consequence that a specific therapy could be initiated significantly earlier.

Exhaled breath can provide comprehensive information about the metabolic state of the subject. Breath analysis carried out during animal experiments promises to increase the information obtained from a particular experiment significantly. This feasibility study should demonstrate the potential of ion mobility spectrometry for animal breath analysis, even for mice. In the framework of the feasibility study, an ion mobility spectrometer coupled with a multicapillary column for rapid preseparation was used to analyze the breath of orotracheally intubated spontaneously breathing mice during anesthesia for the very first time. The sampling procedure was validated successfully. Furthermore, the breath of four mice (2 healthy control mice, 2 with allergic airway inflammation) was analyzed. Twelve peaks were identified directly by comparison with a database. Additional mass spectrometric analyses were carried out for validation and for identification of unknown signals. Significantly different patterns of metabolites were detected in healthy mice compared with asthmatic mice, thus demonstrating the feasibility of analyzing mouse breath with ion mobility spectrometry. However, further investigations including a higher animal number for validation and identification of unknown signals are needed. Nevertheless, the results of the study demonstrate that the method is capable of rapid analyses of the breath of mice, thus significantly increasing the information obtained from each particular animal experiment.

Over the past years, ion mobility spectrometry (IMS) as a well established method within the fields of military and security has gained more and more interest for biological and medical applications. This highly sensitive and rapid separation technique was crucially enhanced by a multi-capillary column (MCC), pre-separation for complex samples. In order to unambiguously identify compounds in a complex sample, like breath, by IMS, a reference database is mandatory. To obtain a first set of reference data, 16 selected volatile organic substances were examined by MCC-IMS and comparatively analyzed by the standard technique for breath research, thermal desorption-gas chromatography-mass spectrometry. Experimentally determined MCC and GC retention times of these 16 compounds were aligned and their relation was expressed in a mathematical function. Using this function, a prognosis of the GC retention time can be given very precisely according to a recorded MCC retention time and vice versa. Thus, unknown MCC-IMS peaks from biological samples can be assigned-after alignment via the estimated GC retention time-to analytes identified by GC/MS from equivalent accomplished data. One example of applying the peak assignment strategy to a real breath sample is shown in detail.

Introduction: The purpose of this work was to study the prognostic influence in breast cancer of thioredoxin reductase 1 (TXNRD1) and thioredoxin interacting protein (TXNIP), key players in oxidative stress control that are currently evaluated as possible therapeutic targets., Methods: Analysis of the association of TXNRD1 and TXNIP RNA expression with the metastasis-free interval (MFI) was performed in 788 patients with node-negative breast cancer, consisting of three individual cohorts (Mainz, Rotterdam and Transbig). Correlation with metagenes and conventional clinical parameters (age, pT stage, grading, hormone and ERBB2 status) was explored. MCF-7 cells with a doxycycline-inducible expression of an oncogenic ERBB2 were used to investigate the influence of ERBB2 on TXNRD1 and TXNIP transcription., Results: TXNRD1 was associated with worse MFI in the combined cohort (hazard ratio = 1.955; P < 0.001) as well as in all three individual cohorts. In contrast, TXNIP was associated with better prognosis (hazard ratio = 0.642; P < 0.001) and similar results were obtained in all three subcohorts. Interestingly, patients with ERBB2-status-positive tumors expressed higher levels of TXNRD1. Induction of ERBB2 in MCF-7 cells caused not only an immediate increase in TXNRD1 but also a strong decrease in TXNIP. A subsequent upregulation of TXNIP as cells undergo senescence was accompanied by a strong increase in levels of reactive oxygen species., Conclusions: TXNRD1 and TXNIP are associated with prognosis in breast cancer, and ERBB2 seems to be one of the factors shifting balances of both factors of the redox control system in a prognostic unfavorable manner.

Detection and immediate quantification of microbial metabolic activities is of high interest in fields as diverse as biotechnology and infection biology. Interestingly, the most direct signals of microbial metabolism, the evolution of volatile metabolites, is largely ignored in the literature, and rather, metabolite concentrations in the microbial surrounding or even disruptive methods for intracellular metabolite measurements (i.e., metabolome analysis) are favored. Here, the development of a multi capillary column coupled ion mobility spectrometer (MCC-IMS) was described for the detection of volatile organic compounds from microbes and the MCC-IMS was used for characterization of metabolic activity of growing Escherichia coli. The MCC-IMS chromatogram of the microbial culture off-gas of the acetone-producing E. coli strain BL21 pLB4 revealed four analytes that positively correlated with growth, which were identified as ethanol, propanone (acetone), heptan-2-one, and nonan-2-one. The occurrence of these analytes was cross-validated by solid-phase micro-extraction coupled with gas chromatography mass spectrometry analysis. With this information in hand, the dynamic relationship between the E. coli biomass concentration and the metabolite concentrations in the headspace was measured. The results suggest that the metabolic pathways of heptan-2-one and nonan-2-one synthesis are regulated independent of each other. It is shown that the MCC-IMS in-line off-gas analysis is a simple method for real-time detection of microbial metabolic activity and discussed its potential for application in metabolic engineering, bioprocess control, and health care.

Based on the reduced expression of ethanol-oxidizing enzymes in human hepatocellular carcinoma (HepG2) cells, we analyzed the role of nonoxidative metabolites in ethanol-induced apoptosis in HepG2 cells. For this purpose, an analysis of volatile metabolites of ethanol using ion-mobility spectrometry and gas chromatography-mass spectrometry was performed. HepG2 cells exposed to 1 mmol/L ethanol exhibited significant synthesis of undecan-2-one compared to untreated cells. Undecan-2-one is a fatty acid ethyl ester metabolite synthesized through a nonoxidative pathway. Undecan-2-one had a dose-dependent cytotoxic effect on HepG2 cells as shown by release of lactate dehydrogenase (LDH). The most notable finding of this study was the potentiation of ethanol-induced apoptosis demonstrated by an increased apoptotic rate induced by undecan-2-one in ethanol-treated HepG2 cells. The data presented in this study contribute to the better understanding of the molecular mechanisms of ethanol exposure at low concentration in HepG2 cells, a human hepatocellular carcinoma-derived cell line.

A feasibility study with ion mobility spectrometry (IMS) was started to find characteristic peaks of volatile organic compounds in exhaled air of 10 mL sampling volume, which might be relevant for the diagnosis of sarcoidosis. Therefore, breath samples of 9 patients with sarcoidosis and suspicion of sarcoidosis because of mediastinal lymph node enlargement were investigated. The 5 patients with confirmed sarcoidosis showed a highly congruent distribution of metabolites in exhaled air which was different in main component analyses from patients with unspecific mediastinal lymph node enlargement. These results are a first step in breath analysis by IMS in patients with sarcoidosis. The IMS as a new method in breath analysis and the first results of the investigations are presented and discussed in detail.

Ion mobility spectrometry, originally used to detect chemical warfare agents, explosives and illegal drugs, is now frequently applied in the field of process analytics. The method combines both high sensitivity (detection limits down to the ng to pg per liter and ppb(v)/ppt(v) ranges) and relatively low technical expenditure with a high-speed data acquisition. In this paper, the working principles of IMS are summarized with respect to the advantages and disadvantages of the technique. Different ionization techniques, sample introduction methods and preseparation methods are considered. Proven applications of different types of ion mobility spectrometer (IMS) used at ISAS will be discussed in detail: monitoring of gas insulated substations, contamination in water, odoration of natural gas, human breath composition and metabolites of bacteria. The example applications discussed relate to purity (gas insulated substations), ecology (contamination of water resources), plants and person safety (odoration of natural gas), food quality control (molds and bacteria) and human health (breath analysis).

Emissions from surfaces (from furniture, wall paintings or floor coverings for instance) significantly influence indoor air quality and therefore the wellbeing or even the health of the occupants. Together with metabolites from mold they are responsible for the well-known "sick building syndrome". Therefore, it is in the interest of the manufacturer as well as of the occupants to have a fast and accurate method for the detection of substances relevant to this syndrome in order to be able to monitor and control product quality and indoor air quality. The use of small and easy-to-transport ion mobility spectrometers that use UV light as the ionization source enables rapid in situ detection of such substances with high selectivity and sensitivity (detection limits in the lower ppb range). If a multicapillary column is used for preseparation as well, the selectivity is increased and the unwanted influence of humidity on the spectra can be eliminated, thus enabling the use of the instruments under normal ambient conditions. Furthermore, the use of air as carrier gas avoids the need for other sources of high-purity gas. An emission cell with a homogeneous and constant air flow over the surface to be investigated was developed in order to ensure reproducible results. Investigations of emissions from wooden surfaces with and without additional contamination as well as from complex mixtures are presented. The results demonstrate that relevant emissions can be identified and quantified with high sensitivity and selectivity in under five minutes. Therefore, the method is useful for indoor air quality monitoring, especially when miniaturized instruments are applied.

The combination of headspace solid phase microextraction (HS-SPME) [1] with fast chromatographic pre-separation by means of multi-capillary columns (MCC) coupled to an ion mobility spectrometer equipped with a 10.6 eV photoionisation source was applied to rapid on-site monitoring of ground and surface water contaminations. Under field conditions, water contaminants were directly detectable down to the upper microg/L range. The potential of the developed method is illustrated by measurements of BTEX, naphthalene, chlorinated alkenes and chlorinated benzenes in water. Practical results from investigations of contaminated groundwater at a former gasworks site and groundwater from the site of a metal processing plant, focusing on the key substances benzene, naphthalene and tetrachloroethene, demonstrate the feasibility of the system for field studies.

A combination of a custom-designed ion mobility spectrometer (IMS) with a UV ionization source and a high speed capillary column (HSCC) has been developed as an analytical device for the sensitive detection of volatile organic compounds (VOCs), e.g. 2-propanone (acetone), 2-butanone and 3-pentanone (diethyl ketone) in the gas phase. A fast separation of the three selected substances and benzene, toluene and m-xylene (BTX) - all of which occur in human breath - has been achieved within less than four minutes at a carrier gas flow rate of 4.5 mL x min(-1). Multi-dimensional correlations presented support the interpretation of the acquired spectra of mixtures. Method detection limits were 2.7 microg x L(-1) for acetone and 2-butanone and 3.0 microg x L(-1) for diethyl ketone in nitrogen, respectively. The assay linear dynamic range is 4-320 microg x L(-1).