Peak finding and referencing in MCC/IMS-data

Simultaneous measurement of inhaled air and exhaled breath by double multicapillary column ion-mobility spectrometry, a new method for breath analysis: results of a feasibility study

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.

Simultaneous analysis of inhaled air and exhaled breath by a newly invented double MCC-IMS device shows that exhaled breath contains confounding exogeneous analytes and only a smaller number of truly endogenous VOCs, which can be used for further analysishttps://bit.ly/3HGVzV5

Full Workflows for the Analysis of Gas Chromatography-Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma

Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models.

BALSAM-An Interactive Online Platform for Breath Analysis, Visualization and Classification

The field of breath analysis lacks a fully automated analysis platform that enforces machine learning good practice and enables clinicians and clinical researchers to rapidly and reproducibly discover metabolite patterns in diseases. We present BALSAM-a comprehensive web-platform to simplify and automate this process, offering features for preprocessing, peak detection, feature extraction, visualization and pattern discovery. Our main focus is on data from multi-capillary-column ion-mobility-spectrometry. While not limited to breath data, BALSAM was developed to increase consistency and robustness in the data analysis process of breath samples, aiming to expand the array of low cost molecular diagnostics in clinics. Our platform is freely available as a web-service and in form of a publicly available docker container.

Detection of axillary perspiration metabolites using ion mobility spectrometry coupled to rapid gas chromatography

The composition of human sweat-and as a consequence the composition of volatiles released from human skin-strongly depends on genetic preconditions, diet, stress, personal hygiene but also on health status and medication. Accordingly, the composition is a carrier of information on the physical and mental states of a person. Therefore, rapid on-site analysis of the relevant substances may be used for medical diagnosis and medication control or even for psychological characterisation. Ion mobility spectrometry coupled to rapid gas chromatography (GC-IMS) was applied to the analysis of human axillary sweat as a sensitive, selective, rapid, and non-invasive method in a feasibility study. For this purpose, a sampling chamber was designed and manufactured. The design and the experimental setup were validated successfully. At least 179 human metabolites could be detected by GC-IMS from the skin of 7 volunteers. Fifteen metabolites were available in all samples from all volunteers and therefore can be characterised as basic sweat compounds which might enable the localisation of hidden persons. Furthermore, in a preliminary feasibility study, the potential of GC-IMS for differentiating the composition of sweat after physical exercises and in a stressful situation-even gender specific-could be demonstrated. Thus, with GC-IMS, a rapid and mobile analytical tool for the analysis of skin volatiles is available for a broad range of applications, e.g. with regard to axillary odour, human health, nutrition, consumption of remedies or drugs of abuse, the localisation of trapped or hidden persons, or even the characterisation of the reaction on stressful situations. Graphical abstract.

Smell the change: On the potential of gas-chromatographic ion mobility spectrometry in ecosystem monitoring

Plant volatile organic compounds (pVOCs) are being recognized as an important factor in plant–environment interactions. Both the type and amount of the emissions appear to be heavily affected by climate change. A range of studies therefore has been directed toward understanding pVOC emissions, mostly under laboratory conditions (branch/leaf enclosure). However, there is a lack of rapid, sensitive, and selective analytical methods, and therefore, only little is known about VOC emissions under natural, outdoor conditions. An increased sensitivity and the identification of taxon‐specific patterns could turn VOC analysis into a powerful tool for the monitoring of atmospheric chemistry, ecosystems, and biodiversity, with far‐reaching relevance to the impact of climate change on pVOCs and vice versa. This study for the first time investigates the potential of ion mobility spectrometry coupled to gas‐chromatographic preseparation (GC‐IMS) to dramatically increase sensitivity and selectivity for continuous monitoring of pVOCs and to discriminate contributing plant taxa and their phenology. Leaf volatiles were analyzed for nine different common herbaceous plants from Germany. Each plant turned out to have a characteristic metabolite pattern. pVOC patterns in the field would thus reflect the composition of the vegetation, but also phenology (with herbaceous and deciduous plants contributing according to season). The technique investigated here simultaneously enables the identification and quantification of substances characteristic for environmental pollution such as industrial and traffic emissions or pesticides. GC‐IMS thus has an enormous potential to provide a broad range of data on ecosystem function. This approach with near‐continues measurements in the real plant communities could provide crucial insights on pVOC‐level emissions and their relation to climate and phenology and thus provide a sound basis for modeling climate change scenarios including pVOC emissions.

A detailed comparison of analysis processes for MCC-IMS data in disease classification-Automated methods can replace manual peak annotations

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.

Chemical Sniffing Instrumentation for Security Applications

Border control for homeland security faces major challenges worldwide due to chemical threats from national and/or international terrorism as well as organized crime. A wide range of technologies and systems with threat detection and monitoring capabilities has emerged to identify the chemical footprint associated with these illegal activities. This review paper investigates artificial sniffing technologies used as chemical sensors for point-of-use chemical analysis, especially during border security applications. This article presents an overview of (a) the existing available technologies reported in the scientific literature for threat screening, (b) commercially available, portable (hand-held and stand-off) chemical detection systems, and (c) their underlying functional and operational principles. Emphasis is given to technologies that have been developed for in-field security operations, but laboratory developed techniques are also summarized as emerging technologies. The chemical analytes of interest in this review are (a) volatile organic compounds (VOCs) associated with security applications (e.g., illegal, hazardous, and terrorist events), (b) chemical "signatures" associated with human presence, and

Chemometrics for ion mobility spectrometry data: recent advances and future prospects

This is the first comprehensive review on chemometric techniques used in ion mobility spectrometry data analysis.

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].

An online peak extraction algorithm for ion mobility spectrometry data

Ion mobility (IM) spectrometry (IMS), coupled with multi-capillary columns (MCCs), has been gaining importance for biotechnological and medical applications because of its ability to detect and quantify volatile organic compounds (VOC) at low concentrations in the air or in exhaled breath at ambient pressure and temperature. Ongoing miniaturization of spectrometers creates the need for reliable data analysis on-the-fly in small embedded low-power devices. We present the first fully automated online peak extraction method for MCC/IMS measurements consisting of several thousand individual spectra. Each individual spectrum is processed as it arrives, removing the need to store the measurement before starting the analysis, as is currently the state of the art. Thus the analysis device can be an inexpensive low-power system such as the Raspberry Pi. The key idea is to extract one-dimensional peak models (with four parameters) from each spectrum and then merge these into peak chains and finally two-dimensional peak models. We describe the different algorithmic steps in detail and evaluate the online method against state-of-the-art peak extraction methods.

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.

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.

A modular computational framework for automated peak extraction from ion mobility spectra

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.

Peak detection method evaluation for ion mobility spectrometry by using machine learning approaches

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 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.

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.

Linearized equations for the reduced ion mobilities of polar aliphatic organic compounds

Over the years, ion mobility spectrometry has evolved into a powerful technique for rapid identification of analytes in very complex sample matrixes such as human breath. Every analyte detected has a characteristic ion mobility value (and a retention time when additional preseparation techniques are employed) which is used to identify the peaks in a spectrum either by comparison with reference analytes or by simultaneous mass spectrometric measurements. In this study, the mass-mobility correlations between compounds in three different homologous series are used to predict the mobilities of the other substances in the same series in a medium of synthetic air. The results show a very high accuracy (>99.5%) of the prognosis. The linear trend equations of ion mobilities, as a function of the number of carbon atoms, obtained from the different series were then generalized into one linear equation for the reduced ion mobility for the polar aliphatic compounds and is validated by comparing it with the traditional Mason-Schamp equation. To compare the empirical equation obtained from the prognosis and the Mason-Schamp equation, the collision integral term in the latter was split into two terms to linearize it. The resulting novel ion mobility equation could be the starting step to completely describe the relationship between ion collision integral and the ion mobility for polar aliphatic compounds. The splitting of the collision integral into two terms will also give new inputs to describe the various ion models and the different forces that act on the ions and the neutral gas molecules upon which the collision integral is dependent on. This prognosis method could, furthermore, be extended to all other classes of organic compounds and could serve as a useful tool for identification of unknowns in ion mobility spectra, thereby considerably reducing the time-consuming and costly reference measurements and other coupling techniques that are currently employed.

Analyses of mouse breath with ion mobility spectrometry: a feasibility study

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.

Ion mobility spectrometry coupled with multi-capillary columns for metabolic profiling of human breath

Recently, ion mobility spectrometry (IMS) started to be used for direct breath analysis with respect to metabolic profiling, biomarker finding and gas trace analysis. The present review describes the basic operation of an ion mobility spectrometer including the ionization process, humidity effects and sampling procedures. To enhance the resolution, pre-separation by multi-capillary columns (MCCs) is discussed and examples for IMS chromatograms are presented. The focus is to review the analytical method IMS with respect to potential use for direct investigations of humid air in direct breath analysis but not on detailed discussion of results of specific medical application of MCC/IMS or on specific analytes found in exhaled air.