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Volatile organic compounds (VOCs) have great promise for bacterial detection and identification because they are emitted from all organisms, and different microbes have unique VOC signatures. Volatile molecules have been utilized as a non-invasive measurement for detecting various respiratory infections including chronic obstructive pulmonary disease1, tuberculosis2 in urine3, and ventilator-associated pneumonia4, in addition to distinguishing subjects with cystic fibrosis (CF) from healthy control subjects5,6. Volatile signatures have even been used to distinguish specific pathogen infections in CF (Staphylococcus aureus7, Pseudomonas aeruginosa8,9, and S. aureus vs. P. aeruginosa10). However, with the complexity of such biological samples, it is often difficult to pinpoint the source of specific VOCs.
One strategy for disentangling the volatile profiles from multiple infecting microbes is to perform headspace analysis of microorganisms in both mono- and co-culture11. Headspace analysis examines the analytes emitted into the "headspace" above a sample rather than those embedded in the sample itself. Microbial metabolites have often been characterized in mono-cultures because of the difficulty in determining the origin of microbial metabolites in complex clinical samples. By profiling volatiles from bacterial mono-cultures, the types of volatiles a microbe produces in vitro may represent a baseline of its volatile repertoire. Combining bacterial cultures, e.g., creating co-cultures, and profiling the volatile molecules produced may reveal the interactions or cross-feeding between the bacteria12.
Another strategy for identifying the microbial origin of volatile molecules is to provide a nutrient source that is labeled with a stable isotope. Stable isotopes are naturally occurring, non-radioactive forms of atoms with a different number of neutrons. In a strategy that has been utilized since the early 1930s to trace active metabolism in animals13, the microorganism feeds off of the labeled nutrient source and incorporates the stable isotope into its metabolic pathways. More recently, a stable isotope in the form of heavy water (D2O) has been used to identify metabolically active S. aureus in a clinical CF sputum sample14. In another example, 13C-labeled glucose has been used to demonstrate the cross-feeding of metabolites between CF clinical isolates of P. aeruginosa and Rothia mucilaginosa12 .
With the advancement of mass spectrometry techniques, methods of detecting volatile cues have moved from qualitative observations to more quantitative measurements. By using gas chromatography mass spectrometry (GC-MS), processing of biological samples has become within reach for most laboratory or clinical settings. Many methods to survey volatile molecules have been used to profile samples such as food, bacterial cultures, and other biological samples, and air and water to detect contamination. However, several common methods of volatile sampling with high-throughput require solvent and are not performed with the advantages provided by vacuum extraction. In addition, larger volumes or quantities (greater than 0.5 mL) of sampled materials are often required for analysis15,16,17,18,19, although this is substrate-specific and requires optimization for each sample type and method.
Here, vacuum-assisted sorbent extraction (VASE) followed by thermal desorption on a GC-MS was employed to survey the volatile profiles of bacterial mono- and co-cultures and identify actively produced volatiles with stable isotope probing from human feces, saliva, sewage, and sputum samples (Figure 1). With limited sample quantities, VOCs were extracted from as little as 15 µL of sputum. Isotope probing experiments with human samples required adding a stable isotope source, such as 13C glucose, and media to cultivate the growth of the microbial community. The active production of volatiles was identified as a heavier molecule by GC-MS. Extraction of volatile molecules under a static vacuum enabled the detection of volatile molecules with increased sensitivity20,21,22.