Analysis of volatile organic compounds in human saliva by a static sorptive extraction method and gas chromatography-mass spectrometry.

Human saliva not only helps control oral health (with anti-microbial proteins), but it may also play a role in chemical communication. As is the case with other mammalian species, human saliva contains peptides, proteins, and numerous volatile organic compounds (VOCs). A high-throughput analytical method is described for profiling a large number of saliva samples to screen the profiles of VOCs. Saliva samples were collected in a non-stimulated fashion. The method utilized static stir bar extraction followed by gas chromatography-mass spectrometry (GC-MS). The method provided excellent reproducibility for a wide range of salivary compounds, including alcohols, aldehydes, ketones, carboxylic acids, esters, amines, amides, lactones, and hydrocarbons. Furthermore, substantial overlap of salivary VOCs and the previously reported skin VOCs in the same subject group was found in this study by using pattern recognition analyses. Sensitivity, precision, and reproducibility of the method suggest that this technique has potential in physiological, metabolomic, pharmacokinetic, forensic, and toxicological studies of small organic compounds where a large number of human saliva samples are involved.

Mouse urinary biomarkers provide signatures of maturation, diet, stress level, and diurnal rhythm.

Body fluids such as urine potentially contain a wealth of information pertaining to age, sex, social and reproductive status, physiologic state, and genotype of the donor. To explore whether urine could encode information regarding environment, physiology, and development, we compared the volatile compositions of mouse urine using solid-phase microextraction and gas chromatography-mass spectrometry (SPME-GC/MS). Specifically, we identified volatile organic compounds (VOCs) in individual urine samples taken from inbred C57BL/6J-H-2(b) mice under several experimental conditions-maturation state, diet, stress, and diurnal rhythms, designed to mimic natural variations. Approximately 1000 peaks (i.e., variables) were identified per comparison and of these many were identified as potential differential biomarkers. Consistent with previous findings, we found groups of compounds that vary significantly and consistently rather than a single unique compound to provide a robust signature. We identified over 49 new predictive compounds, in addition to identifying several published compounds, for maturation state, diet, stress, and time-of-day. We found a considerable degree of overlap in the chemicals identified as (potential) biomarkers for each comparison. Chemometric methods indicate that the strong group-related patterns in VOCs provide sufficient information to identify several parameters of natural variations in this strain of mice including their maturation state, stress level, and diet.

Consensus multivariate methods in gas chromatography mass spectrometry and denaturing gradient gel electrophoresis: MHC-congenic and other strains of mice can be classified according to the profiles of volatiles and microflora in their scent-marks.

House mice (Mus domesticus) communicate using scent-marks, and the chemical and microbial composition of these 'extended phenotypes' are both influenced by genetics. This study examined how the genes of the major histocompatibility complex (MHC) and background genes influence the volatile compounds (analysed with Gas Chromatography Mass Spectrometry or GC/MS) and microbial communities (analysed using Denaturating Gradient Gel Electrophoresis or DGGE) in scent-marks produced by congenic strains of mice. The use of Consensus Principal Components Analysis is described and shows relationships between the two types of fingerprints (GC/MS and DGGE profiles). Classification methods including Support Vector Machines and Discriminant Partial Least Squares suggest that mice can be classified according to both background strain and MHC-haplotype. As expected, the differences among the mice were much greater between strains that vary at both MHC and background loci than the congenics, which differ only at the MHC. These results indicate that the volatiles in scent-marks provide information about genetic similarity of the mice, and support the idea that the production of these genetically determined volatiles is influenced by commensal microflora. This paper describes the application of consensus methods to relate two blocks of analytical data.

Application of dissimilarity indices, principal coordinates analysis, and rank tests to peak tables in metabolomics of the gas chromatography/mass spectrometry of human sweat.

The majority of works in metabolomics employ approaches based on principal components analysis (PCA) and partial least-squares, primarily to determine whether samples fall within large groups. However, analytical chemists rarely tackle the problem of individual fingerprinting, and in order to do this effectively, it is necessary to study a large number of small groups rather than a small number of large groups and different approaches are required, as described in this paper. Furthermore, many metabolomic studies on mammals and humans involve analyzing compounds (or peaks) that are present in only a certain portion of samples, and conventional approaches of PCA do not cope well with sparse matrices where there may be many 0s. There is, however, a large number of qualitative similarity measures available for this purpose that can be exploited via principal coordinates analysis (PCO). It can be shown that PCA scores are a specific case of PCO scores, using a quantitative similarity measure. A large-scale study of human sweat consisting of nearly 1000 gas chromatography/mass spectrometry analyses from the sweat of an isolated population of 200 individuals in Carinthia (Southern Austria) sampled once per fortnight over 10 weeks was employed in this study and grouped into families. The first step was to produce a peak table requiring peak detection, alignment, and integration. Peaks were reduced from 5080 to 373 that occurred in at least 1 individual over 4 out of 5 fortnights. Both qualitative (presence/absence) and quantitative (equivalent to PCA) similarity measures can be computed. PCO and the Kolomorogov-Smirnoff (KS) rank test are applied to these similarity matrices. It is shown that for this data set there is a reproducible individual fingerprint, which is best represented using the qualitative similarity measure as assessed both by the Hotelling t2 statistic as applied to PCO scores and the probabilities associated with the KS rank test.

Individual and gender fingerprints in human body odour.

Individuals are thought to have their own distinctive scent, analogous to a signature or fingerprint. To test this idea, we collected axillary sweat, urine and saliva from 197 adults from a village in the Austrian Alps, taking five sweat samples per subject over 10 weeks using a novel skin sampling device. We analysed samples using stir bar sorptive extraction in connection with thermal desorption gas chromatograph-mass spectrometry (GC-MS), and then we statistically analysed the chromatographic profiles using pattern recognition techniques. We found more volatile compounds in axillary sweat than in urine or saliva, and among these we found 373 peaks that were consistent over time (detected in four out of five samples per individual). Among these candidate compounds, we found individually distinct and reproducible GC-MS fingerprints, a reproducible difference between the sexes, and we identified the chemical structures of 44 individual and 12 gender-specific volatile compounds. These individual compounds provide candidates for major histocompatibility complex and other genetically determined odours. This is the first study on human axillary odour to sample a large number of subjects, and our findings are relevant to understanding the chemical nature of human odour, and efforts to design electronic sensors (e-nose) for biometric fingerprinting and disease diagnoses.