Gram-negative and -positive bacteria differentiation in blood culture samples by headspace volatile compound analysis

Abstract

Background: Identification of microorganisms in positive blood cultures still relies on standard techniques such as Gram staining followed by culturing with definite microorganism identification. Alternatively, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or the analysis of headspace volatile compound (VC) composition produced by cultures can help to differentiate between microorganisms under experimental conditions. This study assessed the efficacy of volatile compound based microorganism differentiation into Gram-negatives and -positives in unselected positive blood culture samples from patients.

Methods: Headspace gas samples of positive blood culture samples were transferred to sterilized, sealed, and evacuated 20 ml glass vials and stored at -30 °C until batch analysis. Headspace gas VC content analysis was carried out via an auto sampler connected to an ion-molecule reaction mass spectrometer (IMR-MS). Measurements covered a mass range from 16 to 135 u including CO2, H2, N2, and O2. Prediction rules for microorganism identification based on VC composition were derived using a training data set and evaluated using a validation data set within a random split validation procedure.

Results: One-hundred-fifty-two aerobic samples growing 27 Gram-negatives, 106 Gram-positives, and 19 fungi and 130 anaerobic samples growing 37 Gram-negatives, 91 Gram-positives, and two fungi were analysed. In anaerobic samples, ten discriminators were identified by the random forest method allowing for bacteria differentiation into Gram-negative and -positive (error rate: 16.7 % in validation data set). For aerobic samples the error rate was not better than random.

Conclusions: In anaerobic blood culture samples of patients IMR-MS based headspace VC composition analysis facilitates bacteria differentiation into Gram-negative and -positive.

Keywords: Blood culture; Chemical ionization; Gram identification; Mass spectrometry; Prediction rule; Volatile compound.

Fig. 1

Identified discriminators for differentiation between Gram-negative and Gram-positive bacteria. Boxplot of random forest method identified discriminators for the training (left) and validation (right) set for differentiation into Gram-negative (white boxplots) and Gram-positive (grey boxplots) bacteria in anaerobic samples. Discriminators are given at their mass to charge ratio (m/z) of appearance. H₂ (m/z = 2) was identified using electron impact ionization. Compounds detected at m/z = 34–36, 64 and 66 were measured by chemical ionization using mercury as primary ion. Compounds detected at m/z = 35, 64, 76 and 80 were measured by chemical ionization using xenon as primary ion. Signal intensity is given in counts per second (cps). p values were computed using a Kolmogorov–Smirnov test for testing if distributions were equal in both groups. Due to a previous H₂ calibration, negative H₂ values were obtained. Therefore, for the graphical presentation a uniform projection of the H₂ values into the positive was performed