Quantification in untargeted mass spectrometry-based metabolomics

The aim of this thesis was to develop concepts and methods to extract qualitative and quantitative information about metabolites from untargeted mass spectrometric data of biological samples. Several typical challenges in data handling were addressed that prevent a straightforward interpretation (data analysis) of the data acquired with different types of mass spectrometric-based metabolomics methods (GC-MS, LC-MS, CE-MS or DI-MS) methods. The critical parameters causing variation in quantitative results were identified and studied at different stages in the metabolomics workflow such as data acquisition, data pre-processing and data analysis. Different methods and concepts were developed to address these and to improve the quantitation of metabolites and the comparison between metabolite data in different samples of the same study measured at different moments or between studies. The methods developed focused on improved normalization, data pre-processing of untargeted analysis and data pre-processing of high resolution direct infusion mass spectrometry data. Furthermore it was demonstrated that even for metabolomic studies with few samples cross-validation of multivariate models can be very time consuming and parallel implementation on a (large) cluster of computers is the way to make such computations feasibleUBL - phd migration 201

Separating common from distinctive variation

BACKGROUND: Joint and individual variation explained (JIVE), distinct and common simultaneous component analysis (DISCO) and O2-PLS, a two-block (X-Y) latent variable regression method with an integral OSC filter can all be used for the integrated analysis of multiple data sets and decompose them in three terms: a low(er)-rank approximation capturing common variation across data sets, low(er)-rank approximations for structured variation distinctive for each data set, and residual noise. In this paper these three methods are compared with respect to their mathematical properties and their respective ways of defining common and distinctive variation. RESULTS: The methods are all applied on simulated data and mRNA and miRNA data-sets from GlioBlastoma Multiform (GBM) brain tumors to examine their overlap and differences. When the common variation is abundant, all methods are able to find the correct solution. With real data however, complexities in the data are treated differently by the three methods. CONCLUSIONS: All three methods have their own approach to estimate common and distinctive variation with their specific strength and weaknesses. Due to their orthogonality properties and their used algorithms their view on the data is slightly different. By assuming orthogonality between common and distinctive, true natural or biological phenomena that may not be orthogonal at all might be misinterpreted

Genetic Markers in Long-Term Survivors of Glioblastoma Multiforme

Scope: Genistein from foods or supplements is metabolized by the gut microbiota and the human body, thereby releasingmany different metabolites into systemic circulation. The order of their appearance in plasma and the possible influence of food format are still unknown. This study compared the nutrikinetic profiles of genistein metabolites. Methods and results: In a randomized cross-over trial, 12 healthy young volunteers were administered a single dose of 30mggenistein provided as a genistein tablet, a genistein tablet in low fat milk, and soy milk containing genistein glycosides. A high mass resolution LC-LTQ-Orbitrap FTMS platform detected and quantified in human plasma: free genistein, seven of its phase-II metabolites and 15 gut-derived metabolites. Interestingly, a novel metabolite, genistein-4- glucuronide-7-sulfate (G-4 G7S) was identified. Nutrikinetic analysis using population-based modeling revealed the order of appearance of five genistein phase II metabolites in plasma: (1) genistein-4,7-diglucuronide, (2) genistein-7-sulfate, (3) genistein-4--sulfate-7-glucuronide, (4) genistein-4-glucuronide, and (5) genistein-7-glucuronide, independent of the food matrix. Conclusion: The conjugated genistein metabolites appear in a distinct order in human plasma. The specific early appearance of G-4 ,7-diG suggests a multistep formation process for the mono and hetero genistein conjugates, involving one or two deglucuronidation steps

Rapid metabolic screening of early zebrafish embryogenesis based on direct infusion-nanoESI-FTMS

Analytical BioScience

Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping

Analytical errors caused by suboptimal performance of the chosen platform for a number of metabolites and instrumental drift are a major issue in large-scale metabolomics studies. Especially for MS-based methods, which are gaining common ground within metabolomics, it is difficult to control the analytical data quality without the availability of suitable labeled internal standards and calibration standards even within one laboratory. In this paper, we suggest a workflow for significant reduction of the analytical error using pooled calibration samples and multiple internal standard strategy. Between and within batch calibration techniques are applied and the analytical error is reduced significantly (increase of 25% of peaks with RSD lower than 20%) and does not hamper or interfere with statistical analysis of the final data. © 2009 American Chemical Society

Increased comparability between RNA-Seq and microarray data by utilization of gene sets

The field of transcriptomics uses and measures mRNA as a proxy of gene expression. There are currently two major platforms in use for quantifying mRNA, microarray and RNA-Seq. Many comparative studies have shown that their results are not always consistent. In this study we aim to find a robust method to increase comparability of both platforms enabling data analysis of merged data from both platforms. We transformed high dimensional transcriptomics data from two different platforms into a lower dimensional, and biologically relevant dataset by calculating enrichment scores based on gene set collections for all samples. We compared the similarity between data from both platforms based on the raw data and on the enrichment scores. We show that the performed data transforms the data in a biologically relevant way and filters out noise which leads to increased platform concordance. We validate the procedure using predictive models built with microarray based enrichment scores to predict subtypes of breast cancer using enrichment scores based on sequenced data. Although microarray and RNA-Seq expression levels might appear different, transforming them into biologically relevant gene set enrichment scores significantly increases their correlation, which is a step forward in data integration of the two platforms. The gene set collections were shown to contain biologically relevant gene sets. More in-depth investigation on the effect of the composition, size, and number of gene sets that are used for the transformation is suggested for future research