Detection and validation of volatile metabolic patterns over different strains of two human pathogenic bacteria during their growth in a complex medium using multi-capillary column-ion mobility spectrometry (MCC-IMS)

Abstract

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.

Fig. 1

Growth curves of E. coli and P. aeruginosa cultures with data points indicating the mean OD and whiskers indicating the 5–95 % confidence interval of OD at each point of measurement. The points in time of MCC-IMS measurements along the growth curve are indicated by arrows

Fig. 2

Substance names and trends in signal intensity of all analysed peaks that were not specific for one of the two bacteria

Fig. 3

Two-dimensional topographic plot with the positions of 19 peaks that were analysed for this study, numbered in alphabetical order. Data labels give information on whether a particular area was considered specific for one of the bacteria or not. Peaks are listed in Table 2

Fig. 4

Substance names and trends in signal intensity of all peaks that were considered to be specific for E. coli. Peak P_642_48 (*) changed its signal intensity in both species, but at different times during their growth. However, it reached its maximum signal intensity over E. coli cultures

Fig. 5

Substance names and trends in signal intensity of all peaks that were considered to be specific for P. aeruginosa