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HYPHENATED $$lc-NMR/MS FOR THE CHARACTERIZATION OF COMPLEX METABOLIC PROFILES AND BIOMARKER DISCOVERY IN BIOFLUIDS

Gabriela Zurek1, Birgit Schneider1, Thomas Zey1, John Shockcor2, Manfred Spraul2, Arnd Ingendoh1, Carsten Baessmann1
(1) Bruker Daltonik GmbH, Fahrenheitstr. 4, 28359 Bremen, Germany; (2) Bruker Biospin, Billerica, MA & Karlsruhe, Germany

Metabonomics, as discipline to learn more about metabolic fluxes in complex organisms (be it man or plant) and the implications of their perturbations, has gained significant interest in many research areas ranging from clinical diagnostics, drug development to agrochemistry. Despite this variety, the analytical requirements for metabolic profiling are very similar: 1. analyze complex mixtures of partially known compounds and unknowns in varying concentrations; 2. evaluate, visualize and manage the data.
Chromatography (GC, $$lc, CE) has been applied ever since to reduce sample complexity. The hyphenation of liquid chromatography to mass spectrometry (MS) and/or nuclear magnetic resonance (NMR) has created a powerful platform for the analysis of complex mixtures yielding both qualitative and quantitative information.
To determine molecular weights and the composition of a complex mixture within fmol/µL concentrations, the technology of choice combined to $$lc is mass spectro-metry with a maximum of mass resolution and accuracy - as in API-TOF or FTMS. For the characterization of unknowns, the exact mass, the isotopic pattern and characteristic fragmentation in MSn are the key information gained by MS. When sufficient material is available, NMR can deliver in addition the true de-novo-structure elucidation - using a cryoprobe, and nowadays 20-50 µg are sufficient for this task.
Besides data acquisition, it is of major importance to intelligently organize data interpretation and visualization. The first step in data interpretation is the reduction of raw data to peak lists being then evaluated with an appropriate statistical technique for classification (e.g. PCA principal component analysis, or genetic algorithms).
Presented here is the combination of $$lc/API-TOF, quadrupole ion trap and NMR for metabolic profiling. The goals of ongoing works are: the search for potential biomarkers and their characterization by $$lc-MS/NMR as well as the generation of libraries for NMR and $$lc/MSn data (including retention time information) for  a finally automated screening procedure.
A set of spiked human urine samples was created for method development and validation.  Subsequently, urine samples of children with various indications were analyzed using an RP-$$lc/API-TOF (ESI, positive and negative). Monoisotopic peak lists of $$lc/MS data were generated using a fuzzy-logic based peak finder algorithm which dissects co-eluting compounds. The molecular elemental compositions were generated using a sigma-fit algorithm. It combines the information from the exact mass assignment and the best fit of the measured isotopic pattern to the theoretical ones of the proposed compounds. Statistical analysis was performed using PCA and the results compared to those of NMR. Potential biomarkers were then subjected to $$lc-MS/NMR for complete characterization.
Urine samples were analyzed with an ion trap in the Auto-MSn mode. An MSn library was generated from the sample data as well as from standards. This MSn library together with an NMR library creates a unique platform for fast automated screening.
Results from NMR showed clear separation of children with metabolic disorders (e.g. methylmalon aciduria) from normal children. First results from $$lc/MS measurements yielded complementary data to NMR: small amino acids and organic acids are easily determined by NMR without separation, while larger and less polar endogenous metabolites (e.g. fatty acid metabolites) are detected by $$lc-MS.

On-line LC-NMR-MS characterization of sesame oil extracts and assessment of their antioxidant activity

Sesame lignans were isolated by solvent extraction and subsequently purified by solvent crystallization from crude, unroasted sesame oil, and a sesame oil deodorizer distillate. In addition, an aliquot of the purified sesame oil extract was treated with camphorsulfonic acid to obtain a sesaminol-enriched extract. The sesame lignan composition of the extracts was characterized by on-line liquid chromatography nuclear magnetic resonance spectroscopy mass spectrometry coupling (LC-NMR-MS). The effect of the sesame oil extracts as well as pure sesame lignans and γ-tocopherol on the oxidative stability of sunflower oil (lignan-free) was studied by the Rancimat assay. The Rancimat assay revealed the following oxidative stability order: sesame oil extract < sesame oil deodorizer distillate < sunflower oil (no added sesame oil extracts) < sesamol < sesaminol-enriched sesame oil extract. In addition, the radical-scavenging capacity of these extracts was assessed by the Trolox® equivalent antioxidant capacity (TEAC) assay. The TEAC assay revealed a slightly different AOX activity order: sesamin < sesame oil extract < sesaminol-enriched sesame oil extract < sesamol. In conclusion, the sesaminol-enriched extract revealed strong antioxidant activity and is therefore suitable to increase the oxidative stability of edible oils high in polyunsaturated fatty acids.

European journal of lipid science and technology  ISSN 1438-7697 
Source / Source
2003, vol. 105, no9, pp. 488-496 [9 page(s) (article)] (32 ref.)

LC–NMR–MS in drug discovery
LC–NMR–MS in drug discovery

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