MCC/IMS as potential noninvasive technique in the diagnosis of patients with COPD with and without alpha 1-antitrypsin deficiency

Effects of Different Probiotics on the Volatile Components of Fermented Coffee Were Analyzed Based on Headspace-Gas Chromatography-Ion Mobility Spectrometry

Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was used to study the effects of four kinds of probiotics on the volatile components of fermented coffee. The fingerprints showed that 51 compounds were confirmed and quantified, including 13 esters, 11 aldehydes, 9 alcohols, 6 ketones, 3 furans, 5 terpenes (hydrocarbons), 2 organic acids, 1 pyrazine, and 1 sulfur-containing compound. After fermenting, the aroma of the green beans increases while that of the roasted beans decreases. After roasting, the total amount of aroma components in coffee beans increased by 4.48-5.49 times. The aroma differences between fermented and untreated roasted beans were more significant than those between fermented and untreated green beans. HS-GC-IMS can distinguish the difference in coffee aroma, and each probiotic has a unique influence on the coffee aroma. Using probiotics to ferment coffee can significantly improve the aroma of coffee and provide certain application prospects for improving the quality of commercial coffee beans.

Changes in volatile organic compounds provoked by lipopolysaccharide- or alpha toxin-induced inflammation in ventilated rats

Inflammation may alter volatile organic compounds (VOCs) in exhaled breath. We therefore used ion mobility spectrometry (IMS) to evaluate exhaled breath components in two non-infectious inflammatory models. Fifty male Sprague Dawley rats were anesthetized and ventilated for 24 h. Five treatments were randomly assigned: (1) lipopolysaccharide low dose [5 mg/kg]; (2) lipopolysaccharide high dose [10 mg/kg]; (3) alpha toxin low dose [40 µg/kg]; (4) alpha toxin high dose [80 µg/kg]; and, (5) NaCl 0.9% as control group. Gas was sampled from the expiratory line of the ventilator every 20 min and analyzed with IMS combined with a multi-capillary column. VOCs were identified by comparison with an established database. Survival analysis was performed by log-rank test, other analyses by one-way or paired ANOVA-tests and post-hoc analysis according to Holm-Sidak. Rats given NaCl and low-dose alpha toxin survived 24 h. The median survival time in alpha toxin high-dose group was 23 (95%-confidence interval (CI): 21, 24) h. In contrast, the median survival time in rats given high-dose lipopolysaccharide was 12 (95% CI: 9, 14) and only 13 (95% CI: 10, 16) h in those given high-dose lipopolysaccharide. 73 different VOCs were detected, of which 35 were observed only in the rats, 38 could be found both in the blank measurements of ventilator air and in the exhaled air of the rats. Forty-nine of the VOCs were identifiable from a registry of compounds. Exhaled volatile compounds were comparable in each group before injection of lipopolysaccharide and alpha toxin. In the LPS groups, 1-pentanol increased and 2-propanol decreased. After alpha toxin treatment, 1-butanol and 1-pentanol increased whereas butanal and isopropylamine decreased. Induction of a non-infectious systemic inflammation (niSI) by lipopolysaccharide and alpha toxin changes VOCs in exhaled breath. Exhalome analysis may help identify niSI.

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.

A mobile instrumentation platform to distinguish airway disorders

Asthma and chronic obstructive pulmonary disease (COPD) are distinct but clinically overlapping airway disorders which often create diagnostic and therapeutic dilemmas. Current strategies to discriminate these diseases are limited by insensitivity and poor performance due to biologic variability. We tested the hypothesis that a gas chromatograph/differential mobility spectrometer (GC/DMS) sensor could distinguish between clinically well-defined groups with airway disorders based on the volatile organic compounds (VOCs) obtained from exhaled breath. After comparing VOC profiles obtained from 13 asthma, 5 COPD and 13 healthy control subjects, we found that VOC profiles distinguished asthma from healthy controls and also a subgroup of asthmatics taking the drug omalizumab from healthy controls. The VOC profiles could not distinguish between COPD and any of the other groups. Our results show a potential application of the GC/DMS for non-invasive and bedside diagnostics of asthma and asthma therapy monitoring. Future studies will focus on larger sample sizes and patient cohorts.

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.