Investigation of gaseous metabolites from moulds by Ion Mobility Spectrometry (IMS) and Gas Chromatography-Mass Spectrometry (GC-MS)

Peppermint protocol: first results for gas chromatography-ion mobility spectrometry

The Peppermint Initiative seeks to inform the standardisation of breath analysis methods. Five Peppermint Experiments with gas chromatography-ion mobility spectrometry (GC-IMS), operating in the positive mode with a tritium 3H 5.68 keV, 370 MBq ionisation source, were undertaken to provide benchmark Peppermint Washout data for this technique, to support its use in breath-testing, analysis, and research. Headspace analysis of a peppermint-oil capsule by GC-IMS with on-column injection (0.5 cm3) identified 12 IMS responsive compounds, of which the four most abundant were: eucalyptol; β-pinene; α-pinene; and limonene. Elevated concentrations of these four compounds were identified in exhaled-breath following ingestion of a peppermint-oil capsule. An unidentified compound attributed as a volatile catabolite of peppermint-oil was also observed. The most intense exhaled peppermint-oil component was eucalyptol, which was selected as a peppermint marker for benchmarking GC-IMS. Twenty-five washout experiments monitored levels of exhaled eucalyptol, by GC-IMS with on-column injection (0.5 cm3), at t=0 min, and then at t+60, t+90, t+165, t+285 and t+360 min from ingestion of a peppermint capsule resulting in 148 peppermint breath analyses. Additionally, the Peppermint Washout data was used to evaluate clinical deployments with a further five washout tests run in clinical settings generating an additional 35 breath samples. Regression analysis yielded an average extrapolated time taken for exhaled eucalyptol levels to return to baseline values to be 429 ± 62 min (± 95% confidence-interval). The benchmark value was assigned to the lower 95 % confidence-interval, 367 min. Further evaluation of the data indicated that the maximum number of volatile organic compounds (VOC) discernible from a 0.5 cm3 breath sample was 69, while the use of an in-line biofilter appeared to reduce this to 34.

Diagnosis of COVID-19 by analysis of breath with gas chromatography-ion mobility spectrometry - a feasibility study

BACKGROUND

There is an urgent need to rapidly distinguish COVID-19 from other respiratory conditions, including influenza, at first-presentation. Point-of-care tests not requiring laboratory- support will speed diagnosis and protect health-care staff. We studied the feasibility of using breath-analysis to distinguish these conditions with near-patient gas chromatography-ion mobility spectrometry (GC-IMS).

METHODS

Independent observational prevalence studies at Edinburgh, UK, and Dortmund, Germany, recruited adult patients with possible COVID-19 at hospital presentation. Participants gave a single breath-sample for VOC analysis by GC-IMS. COVID-19 infection was identified by transcription polymerase chain reaction (RT- qPCR) of oral/nasal swabs together with clinical-review. Following correction for environmental contaminants, potential COVID-19 breath-biomarkers were identified by multi-variate analysis and comparison to GC-IMS databases. A COVID-19 breath-score based on the relative abundance of a panel of volatile organic compounds was proposed and tested against the cohort data.

FINDINGS

Ninety-eight patients were recruited, of whom 21/33 (63.6%) and 10/65 (15.4%) had COVID-19 in Edinburgh and Dortmund, respectively. Other diagnoses included asthma, COPD, bacterial pneumonia, and cardiac conditions. Multivariate analysis identified aldehydes (ethanal, octanal), ketones (acetone, butanone), and methanol that discriminated COVID-19 from other conditions. An unidentified-feature with significant predictive power for severity/death was isolated in Edinburgh, while heptanal was identified in Dortmund. Differentiation of patients with definite diagnosis (25 and 65) of COVID-19 from non-COVID-19 was possible with 80% and 81.5% accuracy in Edinburgh and Dortmund respectively (sensitivity/specificity 82.4%/75%; area-under-the-receiver- operator-characteristic [AUROC] 0.87 95% CI 0.67 to 1) and Dortmund (sensitivity / specificity 90%/80%; AUROC 0.91 95% CI 0.87 to 1).

INTERPRETATION

These two studies independently indicate that patients with COVID-19 can be rapidly distinguished from patients with other conditions at first healthcare contact. The identity of the marker compounds is consistent with COVID-19 derangement of breath-biochemistry by ketosis, gastrointestinal effects, and inflammatory processes. Development and validation of this approach may allow rapid diagnosis of COVID-19 in the coming endemic flu seasons.

FUNDING

MR was supported by an NHS Research Scotland Career Researcher Clinician award. DMR was supported by the University of Edinburgh ref COV_29.

Chemo-profiling of bioactive metabolites from Chaetomium globosum for biocontrol of Sclerotinia rot and plant growth promotion

Chaetomium globosum Kunze ex. Fries has been known to produce diverse bioactive metabolites, attracting researchers to exploit the biocontrol agent for plant disease management. However, distinct research gaps are visible regarding detail characterization of bioactive metabolites. Thus the current study has been planned to characterize volatile and nonvolatile compounds of most potential strain of C. globosum 5157. GC-MS analysis of hexane fraction revealed twenty-six volatile organic compounds, representing 65.5% of total components in which 3-octanone (21.4%) was found to be most abundant. UPLC-QTOF-MS/MS analysis of ethyl acetate and methanolic fractions resulted tentative characterization of fifteen and eleven metabolites, respectively. Among these, nine metabolites were isolated, purified and characterized using ¹H NMR and High resolution mass spectrometric analysis to delineate mass fragmentation pattern for the first time. Antifungal potential of hexane fraction exhibited high inhibitory action against Sclerotium rolfsii (139.2 μg mL^-1) whereas ethyl acetate fraction was highly effective against Sclerotinia sclerotiorum (112.1 μg mL^-1). Comparative assessment of C. globosum 5157 vis a vis Trichoderma harzianum A28 revealed promising effect of C. globosum 5157 with respect to antifungal properties and plant growth promotion of Brassica seedlings.

Mass spectrometric techniques for the analysis of volatile organic compounds emitted from bacteria

Bacteria are the main cause of many human diseases. Typical bacterial identification methods, for example culture-based, serological and genetic methods, are time-consuming, delaying the potential for an early and accurate diagnosis and the appropriate subsequent treatment. Nevertheless, there is a stringent need for in situ tests that are rapid, noninvasive and sensitive, which will greatly facilitate timely treatment of the patients. This review article presents volatile organic metabolites emitted from various micro-organism strains responsible for common bacterial infections in humans. Additionally, the manuscript shows the application of different analytical techniques for fast bacterial identification. Details of these techniques are given, which focuses on their advantages and drawbacks in using for volatile organic components analysis.

Discrimination of bacteria by rapid sensing their metabolic volatiles using an aspiration-type ion mobility spectrometer (a-IMS) and gas chromatography-mass spectrometry GC-MS

The objective of our study was to investigate whether one may quickly and reliably discriminate different microorganism strains by direct monitoring of the headspace atmosphere above their cultures. Headspace samples above a series of in vitro bacterial cultures were directly interrogated using an aspiration type ion mobility spectrometer (a-IMS), which produced distinct profiles ("fingerprints") of ion currents generated simultaneously by the detectors present inside the ion mobility cell. Data processing and analysis using principal component analysis showed net differences in the responses produced by volatiles emitted by various bacterial strains. Fingerprint assignments were conferred on the basis of product ion mobilities; ions of differing size and mass were deflected in a different degree upon their introduction of a transverse electric field, impacting finally on a series of capacitors (denominated as detectors, or channels) placed in a manner analogous to sensor arrays. Three microorganism strains were investigated - Escherichia coli, Bacillus subtilis and Staphylococcus aureus; all strains possess a relatively low pathogenic character. Samples of air with a 5 cm³ volume from the headspace above the bacterial cultures in agar growth medium were collected using a gas-tight chromatographic syringe and injected inside the closed-loop pneumatic circuit of the breadboard a-IMS instrument model ChemPro-100i (Environics Oy, Finland), at a distance of about 1 cm from the ionization source. The resulting chemical fingerprints were produced within two seconds from the moment of injection. The sampling protocol involved to taking three replicate samples from each of 10 different cultures for a specific strain, during a total period of 72 h after the initial incubation - at 24, 48 and 72 h, respectively. Principal component analysis (PCA) was used to discriminate between the IMS fingerprints. PCA was found to successfully discriminate between bacteria at three levels in the experimental campaign: 1) between blank samples from growth medium and samples from bacterial cultures, 2) between samples from different bacterial strains, and 3) between time evolutions of headspace samples from the same bacterial strain over the 3-day sampling period. Consistent classification between growth medium samples and growth medium inoculated with bacteria was observed in both positive and negative detection/ionization modes. In parallel, headspace air samples of 1 dm³ were collected from each bacterial culture and loaded onto Tenax™-Carbograph desorption tubes, using a custom built sampling unit based on a portable sampling pump. One sample was taken for each of 10 different cultures of a strain, at 24, 48 and 72 h after the initial incubation. These adsorption tubes were subsequently analyzed using thermal desorption - gas chromatography - mass spectrometry (TD-GC-MS). This second dataset was intended to produce a qualitative analysis of the volatiles present in the headspace above the bacterial cultures.

Evaluation and application of static headspace-multicapillary column-gas chromatography-ion mobility spectrometry for complex sample analysis

An evaluation of static headspace-multicapillary column-gas chromatography-ion mobility spectrometry (SHS-MCC-GC-IMS) has been undertaken to assess its applicability for the determination of 32 volatile compounds (VCs). The key experimental variables of sample incubation time and temperature have been evaluated alongside the MCC-GC variables of column polarity, syringe temperature, injection temperature, injection volume, column temperature and carrier gas flow rate coupled with the IMS variables of temperature and drift gas flow rate. This evaluation resulted in six sets of experimental variables being required to separate the 32 VCs. The optimum experimental variables for SHS-MCC-GC-IMS, the retention time and drift time operating parameters were determined; to normalise the operating parameters, the relative drift time and normalised reduced ion mobility for each VC were determined. In addition, a full theoretical explanation is provided on the formation of the monomer, dimer and trimer of a VC. The optimum operating condition for each VC calibration data was obtained alongside limit of detection (LOD) and limit of quantitation (LOQ) values. Typical detection limits ranged from 0.1ng bis(methylthio)methane, ethylbutanoate and (E)-2-nonenal to 472ng isovaleric acid with correlation coefficient (R(2)) data ranging from 0.9793 (for the dimer of octanal) through to 0.9990 (for isobutyric acid). Finally, the developed protocols were applied to the analysis of malodour in sock samples. Initial work involved spiking an inert matrix and sock samples with appropriate concentrations of eight VCs. The average recovery from the inert matrix was 101±18% (n=8), while recoveries from the sock samples were lower, that is, 54±30% (n=8) for sock type 1 and 78±24% (n=6) for sock type 2. Finally, SHS-MCC-GC-IMS was applied to sock malodour in a field trial based on 11 volunteers (mixed gender) over a 3-week period. By applying the SHS-MCC-GC-IMS database, four VCs were identified and quantified: ammonia, dimethyl disulphide, dimethyl trisulphide and butyric acid. A link was identified between the presence of high ammonia and dimethyl disulphide concentrations and a high malodour odour grading, that is, ≥ 6. Statistical analysis did not find any correlation between the occurrence of dimethyl disulphide and participant gender.