In vitrovolatile organic compound profiling using GC×GC-TOFMS to differentiate bacteria associated with lung infections: a proof-of-concept study

Abstract

Chronic pulmonary infections are the principal cause of morbidity and mortality in individuals with cystic fibrosis (CF). Due to the polymicrobial nature of these infections, the identification of the particular bacterial species responsible is an essential step in diagnosis and treatment. Current diagnostic procedures are time-consuming, and can also be expensive, invasive and unpleasant in the absence of spontaneously expectorated sputum. The development of a rapid, non-invasive methodology capable of diagnosing and monitoring early bacterial infection is desired. Future visions of real-time, in situ diagnosis via exhaled breath testing rely on the differentiation of bacteria based on their volatile metabolites. The objective of this proof-of-concept study was to investigate whether a range of CF-associated bacterial species (i.e. Pseudomonas aeruginosa, Burkholderia cenocepacia, Haemophilus influenzae, Stenotrophomonas maltophilia, Streptococcus pneumoniae and Streptococcus milleri) could be differentiated based on their in vitro volatile metabolomic profiles. Headspace samples were collected using solid phase microextraction (SPME), analyzed using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS) and evaluated using principal component analysis (PCA) in order to assess the multivariate structure of the data. Although it was not possible to effectively differentiate all six bacteria using this method, the results revealed that the presence of a particular pattern of VOCs (rather than a single VOC biomarker) is necessary for bacterial species identification. The particular pattern of VOCs was found to be dependent upon the bacterial growth phase (e.g. logarithmic versus stationary) and sample storage conditions (e.g. short-term versus long-term storage at -18 °C). Future studies of CF-associated bacteria and exhaled breath condensate will benefit from the approaches presented in this study and further facilitate the production of diagnostic tools for the early detection of bacterial lung infections.

Initial study of three different pathogenic microorganisms by gas chromatography-mass spectrometry

“…Another important point is that the percentage of the total area that the average peaks for 2,3-Pentandione, cis-Dihydro-α-terpinyl acetate, 1-Decyne, 1,3-Heptadiene-3-yne, 2,5-dimethyl Pyrazine, Ethyl butanoate and Cyclohexene,4-ethenyl covered were at least 15%; thus, they are remarkable VOCs for S. aureus . Some of the VOCs produced by S. aureus in the current study have been reported in other studies 34, 37 but some of them have not 28, 33 . The origin of all produced VOCs is not exactly known.…”

Volatile Organic Compound and Metabolite Signatures as Pathogen Identifiers and Biomarkers of Infectious Disease

“…A small SIFT-MSbased analytical instrument has been developed for routine use in a clinical setting [32]. Both GC and PTR ionization technology can be coupled to a Time Of Flight mass spectrometer (GC-TOF-MS, PTR-TOF-MS), thereby making real-time VOC analysis possible [33,34].…”

“…From a technical standpoint, it is important to emphasize that the presence of a unique pattern of VOCs (constituting a complete VOC signature), rather than a single VOC biomarker, will be necessary for bacterial species identification [34]. Diagnostic tests based on a single VOC biomarker do not appear possible, given the fact that all pathogens produce a wide range of overlapping volatile metabolites.…”

“…logarithmic versus stationary phase), sample storage conditions (e.g. short-term versus long-term), and the type of culture media used [34,62,63]. To confound analysis further, patient samples are far less well-defined than laboratory cultures of reference strains, and therefore vary greatly in terms of growth phase, host response, viscosity, confounding co-morbidities, and medications (including antibiotics) [43,61,64].…”

Achieving a Near-Theoretical Maximum in Peak Capacity Gain for the Forensic Analysis of Ignitable Liquids Using GC×GC-TOFMS

“…The Statistical Compare software feature (built directly into the ChromaTOF interface) was utilized for chromatographic alignment and the calculation of Fisher ratios. Fisher ratio filtering [43][44][45][46][47][48] (see Section 2.3 Data Processing) was performed to identify class distinguishing compounds for use in PCA. Figure 9 displays the PCA scores plots generated using the pre-processed GC×GC-TOFMS peak area data for all analytes detected above F crit in the unweathered and weathered gasoline, kerosene, mineral spirits and diesel fuel standards investigated.…”