Author
Götz Schlotterbeck
Other affiliations: University of Tübingen
Bio: Götz Schlotterbeck is an academic researcher from Hoffmann-La Roche. The author has contributed to research in topics: Proton NMR & Nuclear magnetic resonance spectroscopy. The author has an hindex of 22, co-authored 31 publications receiving 2360 citations. Previous affiliations of Götz Schlotterbeck include University of Tübingen.
Papers
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TL;DR: The probabilistic quotient normalization is introduced in this work, which is based on the calculation of a most probable dilution factor by looking at the distribution of the quotients of the amplitudes of a test spectrum by those of a reference spectrum.
Abstract: For the analysis of the spectra of complex biofluids, preprocessing methods play a crucial role in rendering the subsequent data analyses more robust and accurate. Normalization is a preprocessing method, which accounts for different dilutions of samples by scaling the spectra to the same virtual overall concentration. In the field of 1H NMR metabonomics integral normalization, which scales spectra to the same total integral, is the de facto standard. In this work, it is shown that integral normalization is a suboptimal method for normalizing spectra from metabonomic studies. Especially strong metabonomic changes, evident as massive amounts of single metabolites in samples, significantly hamper the integral normalization resulting in incorrectly scaled spectra. The probabilistic quotient normalization is introduced in this work. This method is based on the calculation of a most probable dilution factor by looking at the distribution of the quotients of the amplitudes of a test spectrum by those of a refer...
1,454 citations
TL;DR: RG1678 is a potent, noncompetitive glycine reuptake inhibitor that can modulate both glutamatergic and dopaminergic neurotransmission in animal experiments that model aspects of schizophrenia.
118 citations
TL;DR: All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.
Abstract: Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.
104 citations
TL;DR: The coupling of gradient capillary HPLC with on-column, high-resolution NMR detection and on-line-detected NMR separation of dansylated amino acids is reported on.
Abstract: Coupling HPLC and NMR is one of the most powerful techniques for simultaneous separation and structural elucidation of unknown compounds in mixtures. To date, however, minimizing the detection volume, as is required when coupling NMR with miniaturized separation techniques, has been accompanied by a dramatic loss in resolution of the NMR spectra. Here, we report on the coupling of gradient capillary HPLC with on-column, high-resolution NMR detection. On-line stopped-flow and static 1H NMR spectra were acquired with capillary columns of 75−315 μm i.d. With detection over a length of 1.2 cm, cell volumes cover a range of 50−900 nL. An on-line-detected NMR separation of dansylated amino acids was carried out in a 315 μm i.d. fused silica capillary packed to a length of 12 cm with C18 stationary phase. The low solvent consumption makes the use of fully deuterated solvents economically feasible. NMR spectra with resolution on the order of 3 Hz were obtained using a 50 nL detection cell to measure 1.1 nmol of d...
84 citations
TL;DR: The state-of-the-art of nuclear magnetic resonance spectroscopy, mass spectrometry and statistical tools for the acquisition and evaluation of complex multidimensional spectroscopic data in metabolic profiling is reviewed in this article.
Abstract: The state-of-the-art of nuclear magnetic resonance spectroscopy, mass spectrometry and statistical tools for the acquisition and evaluation of complex multidimensional spectroscopic data in metabolic profiling is reviewed in this article. The continuous evolution of the sensitivity, precision and throughput has made these technologies powerful and extremely robust tools for application in systems biology, pharmaceutical and diagnostics research. Particular emphasis is also given to the collection and storage of biological samples that are subjected to metabolite profiling. Selected examples from preclinical and clinical applications are paradigmatically shown. These illustrate the power of the profiling technologies for characterizing the metabolic phenotype of healthy, diseased and treated subjects. The complexity of disease and drug treatment is asking for an adequate response by integrated and comprehensive metabolite profiling approaches that allow the discovery of new combinations of metabolic biomarkers.
83 citations
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TL;DR: This work reviews strategies for natural product screening that harness the recent technical advances that have reduced technical barriers and assess the use of genomic and metabolomic approaches to augment traditional methods of studying natural products.
Abstract: Natural products have been a rich source of compounds for drug discovery. However, their use has diminished in the past two decades, in part because of technical barriers to screening natural products in high-throughput assays against molecular targets. Here, we review strategies for natural product screening that harness the recent technical advances that have reduced these barriers. We also assess the use of genomic and metabolomic approaches to augment traditional methods of studying natural products, and highlight recent examples of natural products in antimicrobial drug discovery and as inhibitors of protein-protein interactions. The growing appreciation of functional assays and phenotypic screens may further contribute to a revival of interest in natural products for drug discovery.
1,822 citations
TL;DR: A freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst, which supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods.
Abstract: Metabolomics is a newly emerging field of 'omics' research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca.
1,596 citations
TL;DR: An overview of the main functional modules and the general workflow of MetaboAnalyst 4.0 is provided, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc.
Abstract: MetaboAnalyst (https://www.metaboanalyst.ca) is an easy-to-use web-based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever-expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta-analysis, and network-based multi-omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web-based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time-series and two-factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta-analysis Basic Protocol 11: Knowledge-based network exploration of multi-omics data Basic Protocol 12: MetaboAnalystR introduction.
1,522 citations
TL;DR: This unit provides an overview of the main functional modules and the general workflow of the latest version of MetaboAnalyst (MetaboAn analyst 3.0), followed by eight detailed protocols.
Abstract: MetaboAnalyst (http://www.metaboanalyst.ca) is a comprehensive Web application for metabolomic data analysis and interpretation. MetaboAnalyst handles most of the common metabolomic data types from most kinds of metabolomics platforms (MS and NMR) for most kinds of metabolomics experiments (targeted, untargeted, quantitative). In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst also supports a number of data analysis and data visualization tasks using a range of univariate, multivariate methods such as PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), heatmap clustering and machine learning methods. MetaboAnalyst also offers a variety of tools for metabolomic data interpretation including MSEA (metabolite set enrichment analysis), MetPA (metabolite pathway analysis), and biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. This unit provides an overview of the main functional modules and the general workflow of the latest version of MetaboAnalyst (MetaboAnalyst 3.0), followed by eight detailed protocols. © 2016 by John Wiley & Sons, Inc.
1,330 citations
TL;DR: An overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies is provided, including the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS, and dynamic extensions thereof.
Abstract: We provide an overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies. Four steps are demonstrated: (1) definition of the aim, (2) selection of objects, (3) sample preparation and characterization, and (4) evaluation of the collected data. This includes the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS (OPLS), and dynamic extensions thereof. This is illustrated by examples from the literature. Keywords: Statistical Experimental Design (SED) • PCA • OPLS • Class-specific studies • Dynamic studies • Multivariate Design
1,194 citations