Detection of infectious agents in the airways by ion mobility spectrometry of exhaled breath

Abstract

Diseases of the lung, e. g. chronic obstructive pulmonary disease (COPD), interstitial lung diseases, bronchiectasis or cystic fibrosis, often lead to recurrent severe respiratory infections that cause exacerbations of the underlying disease. These acute or chronic inflammatory processes can result in a progressive destruction of the lung and in an ongoing decline in lung function. Therefore longer inpatient stays for intravenous antibiotic treatment are necessary and the quality of life in these patients is severely limited. A rapid detection of infectious agents in human lungs is often crucial, because the choice of the appropriate therapeutic regime depends at first on the identification of the infecting species. Standard methods for detection and identification are either time consuming, of low sensitivity or expensive. It is known that bacteria, and also mitosporic fungi, produce volatile organic compounds (VOCs) that can be detected in exhaled breath by ion mobility spectrometry (IMS), were a distinct detection of a specific VOC is related to a "peak". We investigated, whether the detection and characterisation of VOCs by Multi-capillary column coupled to IMS in exhaled breath of patients whose airways are either infected or colonized by Pseudomonas aeruginosa compared to healthy non-smoker controls is capable of identifying those infectious agents. To realize a non invasive identification of pathogens, the exhaled breath of 53 persons (24 patients suffering chronic or infectious on Pseudomonas and 29 healthy controls) was investigated using an ion mobility spectrometer type BioScout. In total 224 different signals were found. Actually, 21 different signals are able to differentiate the two groups, Control and Pseudomonas, with rank sum values less than 0.2. For all 224 signals Box-and-Wisker plots were realized. The peaks with the lowest rank sum values F (0,107) and PS0 (0,112) show rather good separation of both groups. Our preliminary results demonstrate that distinct patterns of a small number of IMSpeaks are sufficient for the identification of these infectious agents. Therefore MCC-IMS seems to be a promising method for the non-invasive identification of patients which are colonized or infected with bacteria such as Pseudomonas aeruginosa.

Clinical Application of Volatile Organic Compound Analysis for Detecting Infectious Diseases

“…Furthermore, HCN has also been detected in the headspace of a cultured H. pylori reference strain (NCTC11637) (92). As mentioned above, HCN is also known to be emitted by P. aeruginosa cultures (85)(86)(87) and is also detectable in exhaledbreath samples of P. aeruginosa-infected individuals (88,101).…”

“…An ion mobility spectrometer coupled with a multicapillary column was used to examine the exhaled breath of 53 individuals, including 24 individuals infected or colonized with P. aeruginosa and 29 health controls. Of a total of 224 signals recorded, 21 enabled discrimination between the healthy and P. aeruginosa-infected groups with sensitivity and specificity values of 89% and 77%, respectively, and positive and negative predictive values of 83% and 86%, respectively (101). In a different methodological approach, breath samples from 105 children, comprising 48 children with CF and the respective controls, were examined for VOC profiles using GC-time of flight-mass spectrometry (GC-TOF-MS) (18).…”

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

“…According to Rabis et al 2011, bacteria produce VOCs [60]. In their study, they focused on pseudomonas aeruginosa , a bacterium, which is associated with COPD exacerbation.…”

“…A further example for the application of the rank sum test is the detection of microorganisms in the human body. According to Rabis et al 2011, bacteria produce VOCs [ 60 ]. In their study, they focused on pseudomonas aeruginosa , a bacterium, which is associated with COPD exacerbation.…”

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

“…The study was approved by the ethics committee of the university of Essen, with informed consent of all subjects. On a similar dataset, [ 5 ] identified single peaks with differential intensities between the two groups.…”