Metabolomic fingerprinting is a growing strategy for characterizing complex biological samples without detailed prior knowledge about the metabolic system. A two-way analysis system with liquid separation and mass spectrometric detection provides detail-rich data suitable for such fingerprints. As a model study, human urine samples, obtained after intake of coffee, tea, or water, were analyzed with capillary electrophoresis electrospray ionization time-of-flight mass spectrometry (CE-ESI-TOF-MS). In-house-developed software (in Matlab) was utilized to manage and explore the large amount of data acquired (230 CE-MS runs, each with 50-100 million nonzero data points). After baseline and noise reduction, followed by suitable binning in time and m/z, the data sets comprised 9 and 14 million data points in negative and positive ESI mode, respectively. Finally, a signal threshold was applied, further reducing the number to about 100 000 data points per data set. A set of interactive exploratory tools, utilizing principal component analysis (PCA) and analysis of variance (ANOVA) results based on a general linear model, facilitated visual interpretation with score plots (for group assessment) and differential fingerprints (for "hot spot" detection). In the model study highly significant differences due to beverage intake were obtained among the 10 first principal components (p < 10(-6) for two of the components in both ESI modes). Especially, the contrasts between "coffee" and "tea or water" indicated several "hot spots" with highly elevated intensities (e.g., for uncharged masses 93, 94, 109, 119, 123, 132, 148, 169, 178, 187, 190, and 193) suitable for further analysis, for example, with tandem MS.

Comparing CE-MS fingerprints of urine samples, obtained after intake of coffee, tea or water

Peptide mass fingerprints were obtained for three different proteins using three different digestion procedures in triplicates with liquid chromatography coupled to electrospray ionization mass spectrometry. For each protein the results were compared with multivariate data analysis (cluster analysis, kernel principal component analysis) and pair-wise contrast evaluation. Clear systematic differences between the digestion procedures were established for all the proteins. The visual presentation of the pair-wise differences between procedures could to some extent be related to the protein fragments, although the main objective was to identify m/z and retention regions in the original peptide maps that should be subject to further exploration.

This work presents the development of a general and fast method for metabolic profiling of urine, using capillary electrophoresis-electrospray ionisation mass spectrometry (CE-ESIMS) and multivariate data analysis (DA). Human urine samples collected before and after ingestion of paracetamol were analysed at acidic and basic CE conditions, using both positive and negative ESI-MS detection. Analysis of the entire resulting data set, with no prior knowledge of the target compounds, using pair-wise 'fuzzy' correlation and eigenvalue analysis enabled the samples to be discriminated between on the basis of blank urine and urine collected after drug intake. By generating two-dimensional loadings plots, it was also possible to identify the m/z values of the substances responsible for the differentiation between control and dosed samples.

One- or two-dimensional data obtained with LC/GC/CE and single or multiple channel detection (MS, UV/VIS) are often used as ‘fingerprints’ in order to characterize complex samples. The relation between samples is then explored by multivariate data analysis (PCA, hierarchical clustering), but inevitable more or less random variation in separation conditions obstructs the analysis. Several methods for peak alignment have been developed, with more or less increase of time and efforts for computations. In this work another approach is presented, based on a correlation measure less sensitive for variations in retention/migration time. The merits of the method as a fast initial data exploration tool are demonstrated for a case study of urine profiling with CE/MS.