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

Koczulla, Rembert; Hattesohl, Akira; Schmid, Severin; Bödeker, Bertram; Maddula, Sasidhar; Baumbach, Jan

doi:10.1007/s12127-011-0070-0

Cited by 10 publications

(11 citation statements)

References 23 publications

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“…In 2011, Bessa et al [ 58 ] and Koczulla et al [ 59 ] examined the volatile organic compounds in exhaled breath of patients suffering from chronic obstructive pulmonary disease (COPD). The first study used the rank sum test to differentiate between COPD patients and healthy controls.…”

Section: Discussionmentioning

confidence: 99%

Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art

et al. 2012

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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.

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Section: Discussionmentioning

confidence: 99%

Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art

et al. 2012

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“…Not surprisingly, more than half of the applications deal with questions in the field of Pneumology (including e.g. tuberculosis [12], asthma [24,31], asbestosis [16], lung cancer [14,26], cystic fibrosis [18], sarcoidosis [17] and chronic obstructive pulmonary disease (COPD) [15,25,29,33,39]. Also, infections and sepsis [13,19,30,44], vaginosis [42,43], prostate cancer [40] and renal failure [20,21,38,41] are considered.…”

Section: Applications Of Ion Mobility Spectrometers In Medicine-medic...mentioning

confidence: 99%

“…Here, effects arising just related to the different numbers make discrimination problematic. Besides the Propofol (intravenous anaesthetic), specific detection between 5 [29] and 224 [32] different volatile organic compounds (VOCs) is detected, and 3 reports of more than 100 analytes were considered [25,32,45].…”

Section: Applications Of Ion Mobility Spectrometers In Medicine-medic...mentioning

confidence: 99%

Ion mobility spectrometry in breath research

2014

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The number of publications in the field of breath analysis using different types of ion mobility spectrometers (IMS) has increased over the last few years. In this paper, the publications between 2010 and 2013 are reviewed with respect to different types of IMS such as differential mobility spectrometers, high-field asymmetric waveform ion mobility spectrometers and multi-capillary columns coupled to conventional IMS. The analytes detected by IMS and declared with significance to a specific medical question were considered further with respect to medical and analytical questions. In total, 42 different analytes were found to be detected using IMS on a high significance level and were compared to findings using other analytical methods with respect to the individual analyte.

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“…While ion mobility (IM) spectrometry (IMS) is an established technology to detect volatile organic compounds (VOCs) in the air or exhaled breath, the more recent combination with multi-capillary columns (MCCs) has opened new applications in biotechnology and medicine, consider Koczulla et al [ 1 ] and Armenta et al [ 2 ]. The analytes, metabolites present within exhaled breath, are pre-separated using the MCC, analogously to gas chromatography (GC) before mass spectrometry (MS).…”

Section: Introductionmentioning

confidence: 99%

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

D'Addario

Kopczynski

Baumbach³

et al. 2014

BMC Bioinformatics

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BackgroundAn 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.ResultsWe 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.ConclusionsOur 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.

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

Cited by 10 publications

References 23 publications

Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art

Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art

Ion mobility spectrometry in breath research

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

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