MCC/IMS signals in human breath related to sarcoidosis—results of a feasibility study using an automated peak finding procedure

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

Exhaled breath testing - A tool for the clinician and researcher

Exhaled breath is a robust matrix of biomarkers divided between three fractions - gaseous breath, volatile breath, and breath condensate. Breath is collected non-invasively through bags (for gaseous breath), cold condensation chambers (breath condensate), and adsorbent traps (volatile breath). Due to the incredibly dilute nature of breath matrices, breath biomarker analysis requires precise analytical techniques, highly sensitive technology and often challenges the limit of detection of even the most advanced assays. Interest and advances in breath collection, analysis, and use have increased in recent years largely due to advances in analytical technology. Approved and validated breath tests are available as tools for researchers and clinicians. Novel development is ongoing. This article reviews the current applications for exhaled breath biomarkers.

Ion mobility spectrometry: the diagnostic tool of third millennium medicine

Ion mobility spectrometry (IMS) is a fast, low cost, portable, and sensitive technique that separates ions in a drift tube under the influence of an electric field according to their size and shape. IMS represents a non-invasive and reliable instrumental alternative for the diagnosis of different diseases through the analysis of volatile metabolites in biological samples. IMS has applications in medicine in the study of volatile compounds for the non-invasive diagnose of bronchial carcinoma, chronic obstructive pulmonary disease, and other diseases analysing breath, urine, blood, faeces, and other biological samples. This technique has been used to study complex mixtures such as proteomes, metabolomes, complete organisms like bacteria and viruses, monitor anaesthetic agents, determine drugs, pharmaceuticals, and volatile compounds in human body fluids, and others. Pharmaceutical applications include analysis of over-the-counter-drugs, quality assessment, and cleaning verification. Medical practice needs non-invasive, robust, secure, fast, real-time, and low-cost methods with high sensitivity and compact size instruments to diagnose different diseases and IMS is the diagnostic tool that meets all these requirements of the Medicine of the future.

Online Measurement of Exhaled NO Concentration and Its Production Sites by Fast Non-equilibrium Dilution Ion Mobility Spectrometry

Exhaled nitric oxide (NO) is one of the most promising breath markers for respiratory diseases. Its profile for exhalation and the respiratory NO production sites can provide useful information for medical disease diagnosis and therapeutic procedures. However, the high-level moisture in exhaled gas always leads to the poor selectivity and sensitivity for ion spectrometric techniques. Herein, a method based on fast non-equilibrium dilution ion mobility spectrometry (NED-IMS) was firstly proposed to directly monitor the exhaled NO profile on line. The moisture interference was eliminated by turbulently diluting the original moisture to 21% of the original with the drift gas and dilution gas. Weak enhancement was observed for humid NO response and its limit of detection at 100% relative humidity was down to 0.58 ppb. The NO concentrations at multiple exhalation flow rates were measured, while its respiratory production sites were determined by using two-compartment model (2CM) and Högman and Meriläinen algorithm (HMA). Last but not the least, the NO production sites were analyzed hourly to tentatively investigate the daily physiological process of NO. The results demonstrated the capacity of NED-IMS in the real-time analysis of exhaled NO and its production sites for clinical diagnosis and assessment.

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.

Dopant titrating ion mobility spectrometry for trace exhaled nitric oxide detection

Ion mobility spectrometry (IMS) is a promising non-invasive tool for the analysis of exhaled gas and exhaled nitric oxide (NO), a biomarker for diagnosis of respiratory diseases. However, the high moisture in exhaled gas always brings about extra overlapping ion peaks and results in poor identification ability. In this paper, p-benzoquinone (PBQ) was introduced into IMS to eliminate the interference of overlapping ion peaks and realize the selective identification of NO. The overlapping ions caused by moisture were titrated by PBQ and then converted to hydrated PBQ anions (C6H4[Formula: see text](H2O)n). The NO concentration could be determined by quantifying gas phase hydrated nitrite anions (N[Formula: see text](H2O)n), product ions of NO. Under optimized conditions, a limit of detection (LOD) of about 1.4 ppbv and a linear range of 10-200 ppbv were obtained for NO even in 100% relative humidity (RH) purified air. Furthermore, this established method was applied to measure hourly the exhaled NO of eight healthy volunteers, and real-time monitoring the exhaled NO of an esophageal carcinoma patient during radical surgery. These results revealed the potential of the current dopant titrating IMS method in the measurement of exhaled NO for medical disease diagnosis.

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.

Medical applications of breath hydrogen measurements

In this article, technical developments in breath analysis and its applications in the field of clinical diagnosis and the monitoring of various symptoms, particularly molecular hydrogen in breath, are introduced. First, a brief overview of the current uses of the hydrogen breath test is provided. The principles of the test and how hydrogen can be used as a biomarker for various symptoms, and monitoring microbial metabolism, are introduced. Ten case-study applications of breath hydrogen measurements for which hydrogen exhibits beneficial effects for diagnosis, including the contexts of oxidative stress, gastrointestinal disease, and metabolic disorders, are discussed. The technologies and problems involved in breath hydrogen testing, sampling, pretreatment, and detection in exhaled breath are discussed, and research including current analytical systems and new sensors is focused on in the context of hydrogen detection.

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.

Evaluation of fast volatile analysis for detection of Botrytis cinerea infections in strawberry

Grey mold (Botrytis cinerea) is one of the major phytopathogens causing serious losses during strawberry postharvest and storage. B. cinerea-host interaction affect emissions of volatile compounds during infection resulting in a characteristic earthy, mushroom odor. Therefore, the objective of this study was to evaluate two analytical techniques based on fast volatile analysis on their performance for monitoring evolution and early detection of B. cinerea infections in strawberry. In a first experiment headspace multi-capillary column-ion mobility spectrometry (HS MCC-IMS) has been successfully used to evaluate development of strawberry aroma during shelflife. In a second experiment the same technique has been used to detect the degree of B. cinerea infection through changes in the volatile profile. Additionally, these samples were analyzed with headspace solid-phase-microextraction fast GC-MS (HS SPME fast GC-MS). Both HS MCC-IMS and HS SPME fast GC-MS could determine the changes in volatile composition as a function of the degree of B. cinerea infection as determined by an enzyme-linked immunosorbent assay (ELISA) and could be used to follow the evolution of infection. According to the ELISA data, some fruit were infected even without any symptoms and volatiles produced by the fungus may be overshadowed by the fruit volatiles. Therefore, both analytical techniques could not be used for early detection of B. cinerea infections. After identification of the volatile compounds and multivariate data analysis, potential biomarkers specific for B. cinerea were highlighted, being 3-methylbutanal, cis-4-decenal, 2-methyl-1-butanol, 2-methyl-1-propanol, 1-octen-3-one and 1-octen-3-ol.

Comparison of metabolites in exhaled breath and bronchoalveolar lavage fluid samples in a mouse model of asthma

BACKGROUND

A multi-capillary column ion mobility spectrometer (MCC/IMS) was developed to provide a method for the noninvasive diagnosis of lung diseases. The possibility of measuring the exhaled breath of mice was evaluated previously. The aim of the present study was to reveal whether mice affected by airway inflammation can be identified via MCC/IMS.

METHODS

Ten mice were sensitized and challenged with ovalbumin to induce allergic airway inflammation. The breath and volatile compounds of bronchoalveolar lavage fluid (BALF) were measured by MCC/IMS. Furthermore, histamine, nitric oxide, and arachidonic acid were determined as inflammatory markers in vitro.

RESULTS

Six volatile molecules were found in the BALF headspace at a significantly higher concentration in mice with airway inflammation compared with healthy animals. The concentration of substances correlated with the numbers of infiltrating eosinophilic granulocytes. However, substances showing a significantly different concentration in the BALF headspace were not found to be different in exhaled breath. Histamine and nitric oxide were identified by MCC/IMS in vitro but not in the BALF headspace or exhaled breath.

CONCLUSION

Airway inflammation in mice is detectable by the analysis of the BALF headspace via MCC/IMS. Molecules detected in the BALF headspace of asthmatic mice at a higher concentration than in healthy animals may originate from oxidative stress induced by airway inflammation. As already described for humans, we found no correlation between the biomarker concentration in the BALF and the breath of mice. We suggest using the model described here to gain deeper insights into this discrepancy.