Presently, 2 to 4 days elapse between sampling at infection suspicion and result of microbial diagnostics. This delay for the identification of pathogens causes quite often a late and/or inappropriate initiation of therapy for patients suffering from infections. Bad outcome and high hospitalization costs are the consequences of these currently existing limited pathogen identification possibilities. For this reason, we aimed to apply the innovative method multi-capillary column–ion mobility spectrometry (MCC-IMS) for a fast identification of human pathogenic bacteria by determination of their characteristic volatile metabolomes. We determined volatile organic compound (VOC) patterns in headspace of 15 human pathogenic bacteria, which were grown for 24 h on Columbia blood agar plates. Besides MCC-IMS determination, we also used thermal desorption–gas chromatography–mass spectrometry measurements to confirm and evaluate obtained MCC-IMS data and if possible to assign volatile compounds to unknown MCC-IMS signals. Up to 21 specific signals have been determined by MCC-IMS for Proteus mirabilis possessing the most VOCs of all investigated strains. Of particular importance is the result that all investigated strains showed different VOC patterns by MCC-IMS using positive and negative ion mode for every single strain. Thus, the discrimination of investigated bacteria is possible by detection of their volatile organic compounds in the chosen experimental setup with the fast and cost-effective method MCC-IMS. In a hospital routine, this method could enable the identification of pathogens already after 24 h with the consequence that a specific therapy could be initiated significantly earlier.

MCC-IMS may be a suitable method to determine propofol concentrations in exhaled air, and may be used to predict propofol concentrations in serum.

Over the past years, ion mobility spectrometry (IMS) as a well established method within the fields of military and security has gained more and more interest for biological and medical applications. This highly sensitive and rapid separation technique was crucially enhanced by a multi-capillary column (MCC), pre-separation for complex samples. In order to unambiguously identify compounds in a complex sample, like breath, by IMS, a reference database is mandatory. To obtain a first set of reference data, 16 selected volatile organic substances were examined by MCC-IMS and comparatively analyzed by the standard technique for breath research, thermal desorption-gas chromatography-mass spectrometry. Experimentally determined MCC and GC retention times of these 16 compounds were aligned and their relation was expressed in a mathematical function. Using this function, a prognosis of the GC retention time can be given very precisely according to a recorded MCC retention time and vice versa. Thus, unknown MCC-IMS peaks from biological samples can be assigned-after alignment via the estimated GC retention time-to analytes identified by GC/MS from equivalent accomplished data. One example of applying the peak assignment strategy to a real breath sample is shown in detail.

Headspace analyses over microbial cultures using multi-capillary column-ion mobility spectrometry (MCC-IMS) could lead to a faster, safe and cost-effective method for the identification of pathogens. Recent studies have shown that MCC-IMS allows identification of bacteria and fungi, but no information is available from when on during their growth a differentiation between bacteria is possible. Therefore, we analysed the headspace over human pathogenic reference strains of Escherichia coli and Pseudomonas aeruginosa at four time points during their growth in a complex fluid medium. In order to validate our findings and to answer the question if the results of one bacterial strain can be transferred to other strains of the same species, we also analysed the headspace over cultures from isolates of random clinical origin. We detected 19 different volatile organic compounds (VOCs) that appeared or changed their signal intensity during bacterial growth. These included six VOCs exclusively changing over E. coli cultures and seven exclusively changing over P. aeruginosa cultures. Most changes occurred in the late logarithmic or static growth phases. We did not find differences in timing or trends in signal intensity between VOC patterns of different strains of one species. Our results show that differentiation of human pathogenic bacteria by headspace analyses using MCC-IMS technology is best possible during the late phases of bacterial growth. Our findings also show that VOC patterns of a bacterial strain can be transferred to other strains of the same species.

Volatile metabolites of Aspergillus fumigatus and Candida species can be detected by gas chromatography/mass spectrometry (GC/MS). A multi-capillary column - ion mobility spectrometer (MCC-IMS) was used in this study to assess volatile organic compounds (VOCs) in the headspace above A. fumigatus and the four Candida species Candida albicans, Candida parapsilosis, Candida glabrata and Candida tropicalis in an innovative approach, validated for A. fumigatus and C. albicans by GC/MS analyses. For the detection of VOCs, a special stainless steel measurement chamber for the microbial cultures was used. The gas outlet was either attached to MCC-IMS or to adsorption tubes (Tenax GR) for GC/MS measurements. Isoamyl alcohol, cyclohexanone, 3-octanone and phenethylalcohol can be described as discriminating substances by means of GC/MS. With MCC-IMS, the results for 3-octanone and phenethylalcohol are concordant and additionally to GC/MS, ethanol and two further compounds (p_0642_1/p_683_1 and p_705_3) can be described. Isoamyl alcohol and cyclohexanone were not properly detectable with MCC-IMS. The major advantage of the MCC-IMS system is the feasibility of rapid analysis of complex gas mixtures without pre-concentration or preparation of samples and regardless of water vapour content in an online setup. Discrimination of fungi on genus level of the investigated germs by volatile metabolic profile and therefore detection of VOC is feasible. However, a further discrimination on species level for Candida species was not possible.