BackgroundVolatile organic compounds (VOCs) can be intermediates of metabolic pathways and their levels in biological samples may provide a better understanding about diseases in addition to potential methods for diagnosis. Headspace analysis of VOCs in urine samples using solid phase micro extraction (SPME) coupled to gas chromatography - mass spectrometry (GC-MS) is one of the most used techniques. However, it generally produces a limited profile of VOCs if applied to fresh urine. Sample preparation methods, such as addition of salt, base or acid, have been developed to improve the headspace-SPME-GC-MS analysis of VOCs in urine samples. These methods result in a richer profile of VOCs, however, they may also add potential contaminants to the urine samples, result in increased variability introduced by manually processing the samples and promote degradation of metabolites due to extreme pH levels. Here, we evaluated if freeze-drying can be considered an alternative sample preparation method for headspace-SPME-GC-MS analysis of urine samples.ResultsWe collected urine from three volunteers and compared the performances of freeze-drying, addition of acid (HCl), addition of base (NaOH), addition of salt (NaCl), fresh urine and frozen urine when identifying and quantifying metabolites in 4 ml samples. Freeze-drying and addition of acid produced a significantly higher number of VOCs identified than any other method, with freeze-drying covering a slightly higher number of chemical classes, showing an improved repeatability and reducing siloxane impurities.ConclusionIn this work we compared the performance of sample preparation methods for the SPME-GC-MS analysis of urine samples. To the best of our knowledge, this is the first study evaluating the potential of freeze-dry as an alternative sample preparation method. Our results indicate that freeze-drying has potential to be used as an alternative method for the SPME-GC-MS analysis of urine samples. Additional studies using internal standard, synthetic urine and calibration curves will allow a more precise quantification of metabolites and additional comparisons between methods.Graphical abstractEnhancing VOC profiling from urine samples.Electronic supplementary materialThe online version of this article (doi10.1186/s13065-016-0155-2) contains supplementary material, which is avaialble to authorize users.

BackgroundEarly diagnosis of necrotising enterocolitis (NEC) may improve prognosis but there are no proven biomarkers.ObjectiveTo investigate changes in faecal volatile organic compounds (VOCs) as potential biomarkers for NEC.DesignMulticentre prospective study.Settings8 UK neonatal units.PatientsPreterm infants <34 weeks gestation.MethodsDaily faecal samples were collected prospectively from 1326 babies of whom 49 subsequently developed definite NEC. Faecal samples from 32 NEC cases were compared with samples from frequency-matched controls without NEC. Headspace, solid phase microextraction gas chromatography/mass spectrometry was performed and VOCs identified from reference libraries. VOC samples from cases and controls were compared using both discriminant and factor analysis methods.ResultsVOCs were found to cluster into nine groups (factors), three were associated with NEC and indicated the possibility of disease up to 3–4 days before the clinical diagnosis was established. For one factor, a 1 SD increase increased the odds of developing NEC by 1.6 times; a similar decrease of the two other factors was associated with a reduced risk (OR 0.5 or 0.7, respectively). Discriminant analyses identified five individual VOCs, which are associated with NEC in babies at risk, each with an area under the receiver operating characteristics curve of 0.75–0.76, up to 4 days before the clinical diagnosis was made.ConclusionsFaecal VOCs are altered in preterm infants with NEC. These data are currently insufficient to enable reliable cotside detection of babies at risk of developing NEC and further work is needed investigate the role of VOCs in clarifying the aetiology of NEC.

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Background: Giardiasis is a common intestinal infection caused by the flagellated intestinal protozoan Giardia duodenalis. Several methods are available for the laboratory diagnosis of Giardia, ranging from the microscopic identification of the parasite trophozoite and cyst stages, to immunodiagnosis and PCR. Giardia has unique metabolic pathways resulting from its lack of mitochondria, making it an ideal target for volatile organic compound (VOC) profiling. Aim: To characterise the VOC profile of stool infected with Giardia to detect differences from those found in samples of diarrhoea without Giardia or other infections. Method: Stool was obtained from patients with confirmed Giardia infection and controls with diarrhoea but no identifiable infection. Faecal headspace gas extraction and gas chromatography-mass spectrometry were used to extract and identify VOCs. Results: More than 100 VOCs were identified when control and Giardia groups were combined, of which 24 showed significant differences between the two groups (p<0.05). Three VOCs had a significantly greater prevalence amongst Giardia cases (p<0.0001) and 9 VOCs showed a significant difference in terms of abundance (p<0.05). AUROC analysis demonstrated a value of 0.902. Conclusion: There is a significant difference in the VOC profile of stool from subjects infected with Giardia spp, when compared with non-infected controls. These findings can be explained by the unique metabolism of Giardia.

BackgroundMetabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites. However, AMDIS generates a high number of false-positives and does not have an interface amenable for high-throughput data analysis. Although additional computational tools have been developed for processing AMDIS results and to perform normalisations and statistical analysis of metabolomics data, there is not yet a single free software or package able to reliably identify and quantify metabolites analysed by GC-MS.ResultsHere we introduce a new algorithm, PScore, able to score peaks according to their likelihood of representing metabolites defined in a mass spectral library. We implemented PScore in a R package called MetaBox and evaluated the applicability and potential of MetaBox by comparing its performance against AMDIS results when analysing volatile organic compounds (VOC) from standard mixtures of metabolites and from female and male mice faecal samples. MetaBox reported lower percentages of false positives and false negatives, and was able to report a higher number of potential biomarkers associated to the metabolism of female and male mice.ConclusionsIdentification and quantification of metabolites is among the most critical and time-consuming steps in GC-MS metabolome analysis. Here we present an algorithm implemented in a R package, which allows users to construct flexible pipelines and analyse metabolomics data in a high-throughput manner.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0374-2) contains supplementary material, which is available to authorized users.