Project description:The many advantages of (13)C NMR are often overshadowed by its intrinsically low sensitivity. Given that carbon makes up the backbone of most biologically relevant molecules, (13)C NMR offers a straightforward measurement of these compounds. Two-dimensional (13)C-(13)C correlation experiments like INADEQUATE (incredible natural abundance double quantum transfer experiment) are ideal for the structural elucidation of natural products and have great but untapped potential for metabolomics analysis. We demonstrate a new and semiautomated approach called INETA (INADEQUATE network analysis) for the untargeted analysis of INADEQUATE data sets using an in silico INADEQUATE database. We demonstrate this approach using isotopically labeled Caenorhabditis elegans mixtures.
Project description:The 1H NMR spectra of crude extracts from a total of 33 Actaea samples were acquired and analyzed for their species- and plant part-specific metabolomic characteristics by identifying fingerprint resonances via visual observation as well as a chemometric approach using principal component analysis (PCA). The main study subjects were the roots/rhizomes and aerial parts of three American species, Actaea racemosa (AR), Actaea podocarpa (AP) and Actaea cordifolia (AC). AP exhibited an already visually distinct chemical profile from those of the other two species. The species-characteristic resonances were identified as analytical chemotaxonomic markers. AR and AC exhibited visually similar 1H NMR spectral profiles that required statistical analysis for differentiation. Several characteristic peaks and peak patterns were identified for each group of samples. Together with the three American Actaea species, the characteristics of the 1H NMR spectra of Asian species are also discussed. A statistical analysis method using PCA was employed to provide the metabolomic profile for visually minor but analytically significant chemotaxonomic differences. PCA scores allowed differentiation between the three American Actaea species, as well as the ability to differentiate between the various plant parts (aboveground vs. roots/rhizomes).
Project description:BackgroundDespite wide-spread use of Nuclear Magnetic Resonance (NMR) in metabolomics for the analysis of biological samples there is a lack of graphically driven, publicly available software to process large one and two-dimensional NMR data sets for statistical analysis.ResultsHere we present MetaboLab, a MATLAB based software package that facilitates NMR data processing by providing automated algorithms for processing series of spectra in a reproducible fashion. A graphical user interface provides easy access to all steps of data processing via a script builder to generate MATLAB scripts, providing an option to alter code manually. The analysis of two-dimensional spectra (¹H,¹³C-HSQC spectra) is facilitated by the use of a spectral library derived from publicly available databases which can be extended readily. The software allows to display specific metabolites in small regions of interest where signals can be picked. To facilitate the analysis of series of two-dimensional spectra, different spectra can be overlaid and assignments can be transferred between spectra. The software includes mechanisms to account for overlapping signals by highlighting neighboring and ambiguous assignments.ConclusionsThe MetaboLab software is an integrated software package for NMR data processing and analysis, closely linked to the previously developed NMRLab software. It includes tools for batch processing and gives access to a wealth of algorithms available in the MATLAB framework. Algorithms within MetaboLab help to optimize the flow of metabolomics data preparation for statistical analysis. The combination of an intuitive graphical user interface along with advanced data processing algorithms facilitates the use of MetaboLab in a broader metabolomics context.