Blood Culture Headspace Gas Analysis Enables Early Detection of Escherichia coli Bacteremia in an Animal Model of Sepsis

Abstract

(1) Background: Automated blood culture headspace analysis for the detection of volatile organic compounds of microbial origin (mVOC) could be a non-invasive method for bedside rapid pathogen identification. We investigated whether analyzing the gaseous headspace of blood culture (BC) bottles through gas chromatography-ion mobility spectrometry (GC-IMS) enables differentiation of infected and non-infected; (2) Methods: BC were gained out of a rabbit model, with sepsis induced by intravenous administration of E. coli (EC group; n = 6) and control group (n = 6) receiving sterile LB medium intravenously. After 10 h, a pair of blood cultures was obtained and incubated for 36 h. The headspace from aerobic and anaerobic BC was sampled every two hours using an autosampler and analyzed using a GC-IMS device. MALDI-TOF MS was performed to confirm or exclude microbial growth in BCs; (3) Results: Signal intensities (SI) of 113 mVOC peak regions were statistically analyzed. In 24 regions, the SI trends differed between the groups and were considered to be useful for differentiation. The principal component analysis showed differentiation between EC and control group after 6 h, with 62.2% of the data variance described by the principal components 1 and 2. Single peak regions, for example peak region P_15, show significant SI differences after 6 h in the anaerobic environment (p < 0.001) and after 8 h in the aerobic environment (p < 0.001); (4) Conclusions: The results are promising and warrant further evaluation in studies with an extended microbial panel and indications concerning its transferability to human samples.

Keywords: bacteremia; bloodstream infections; gas chromatography-ion mobility spectrometry (GC-IMS); microbial diagnostics; rapid pathogen identification; volatile organic compounds (VOCs).

Figure 1

Development of inflammatory markers in animal blood against time for control (green, n = 6) and EC group (red, n = 6) (mean with corresponding 95%-CI). EC group shows an increase in inflammatory serum cytokines (IL-6, TNF-α) and development of leukopenia and thrombocytopenia.

Figure 2

Pattern (GC retention time vs. IMS drift time) of the detected 24 mVOCs in the headspace of E. coli-infected blood cultures. The peaks are numbered according to Table 1 and listed there with their particular retention-/drift times and 1/K₀.

Figure 3

Development of SI (mean with corresponding 95%-CI) for control (green, n = 6) and EC group (red, n = 6) of two exemplary peaks (P_1; P_15) in aerobic (left) and anaerobic (right) media against time. An SI increase can be observed after 6–8 h.

Figure 4

Unpaired t-Test E. coli (n = 6) vs. Control (n = 6) (Mean, 95% CI) for (a) aerobic media: P_15 aerobic 6 h (not significant, p = 0.10); P_15 aerobic 8 h (*** p < 0.001). (b) Anaerobic media: P_15 anaerobic 4 h (not significant, p = 0.81); P_15 anaerobic 6 h (*** p < 0.001). Differences in P_15 SI are significant the first time after 8 h in aerobic media and after 6 h in anaerobic media.

Figure 5

PCA biplots for timepoints 0, 6, 8, 12, 24, and 36 h incubation time showing the development of differentiation after 6 h in anaerobic media and after 8 h in aerobic media. Each point represents one BC sample.

Figure 6

Heatmap of a Hierarchical-Clustering-Dendrogram after 8 h incubation time. Every row represents a single BC measurement (# = measurement ID; ae = aerobic; an = anaerobic) with the color-coded logarithmic peak SI [V] for each mVOC area. Two clusters representing Control and EC group (green and red box on the left-hand side respectively) are shown, assigning the single measurements (right) correctly to the two groups.