Refine
Year of publication
- 2015 (10) (remove)
Document Type
- Journal article (9)
- Conference proceeding (1)
Language
- English (10)
Is part of the Bibliography
- yes (10)
Institute
- Life Sciences (10)
Publisher
- Royal Society of Chemistry (2)
- Springer (2)
- DOVE Medical Press (1)
- IOP Publishing (1)
- Lippincott Williams & Wilkins (1)
- MDPI (1)
- PLOS (1)
- PeerJ Inc. (1)
From raw ion mobility measurements to disease classification : a comparison of analysis processes
(2015)
Ion mobility spectrometry (IMS) is a technology for the detection of volatile compounds in the air of exhaled breath that is increasingly used in medical applications. One major goal is to classify patients into disease groups, for example diseased versus healthy, from simple breath samples. Raw IMS measurements are data matrices in which peak regions representing the compounds have to be identified and quantified. A typical analysis process consists of pre-processing and peak detection in single experiments, peak clustering to obtain consensus peaks across several experiments, and classification of samples based on the resulting multivariate peak intensities. Recently several automated algorithms for peak detection and peak clustering have been introduced, in order to overcome the current need for human-based analysis that is slow, subjective and sometimes not reproducible. We present an unbiased comparison of a multitude of combinations of peak processing and multivariate classification algorithms on a disease dataset. The specific combination of the algorithms for the different analysis steps determines the classification accuracy, with the encouraging result that certain fully-automated combinations perform even better than current manual approaches.
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.
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.
Influence of the respirator on volatile organic compounds : an animal study in rats over 24 hours
(2015)
Long-term animal studies are needed to accomplish measurements of volatile organic compounds (VOCs) for medical diagnostics. In order to analyze the time course of VOCs, it is necessary to ventilate these animals. Therefore, a total of 10 male Sprague–Dawley rats were anaesthetized and ventilated with synthetic air via tracheotomy for 24 h. An ion mobility spectrometry coupled to multi-capillary columns (MCC–IMS) was used to analyze the expired air. To identify background contaminations produced by the respirator itself, six comparative measurements were conducted with ventilators only. Overall, a number of 37 peaks could be detected within the positive mode. According to the ratio peak intensity rat/ peak intensity ventilator blank, 22 peaks with a ratio >1.5 were defined as expired VOCs, 12 peaks with a ratio between 0.5 and 1.5 as unaffected VOCs, and three peaks with a ratio <0.5 as resorbed VOCs. The peak intensity of 12 expired VOCs changed significantly during the 24 h measurement. These results represent the basis for future intervention studies. Notably, online VOC analysis with MCC–IMS is possible over 24 h in ventilated rats and allows different experimental approaches.
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.
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.
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 amodel 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.