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Updated: Apr 16, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
Combining DI-ESI-MS and NMR datasets for metabolic profiling
Darrell D Marshall1, Shulei Lei1, Bradley Worley1
1Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304.
Combining mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) metabolomics data improves metabolite identification and analysis. This study optimized sample preparation and data handling for simultaneous MS and NMR analysis, enhancing neurotoxin research.
Area of Science:
- Metabolomics
- Analytical Chemistry
- Biochemistry
Background:
- Metabolomics datasets are typically acquired using mass spectrometry (MS) or nuclear magnetic resonance (NMR) spectroscopy separately.
- These techniques are fundamentally complementary, and their combined use enhances metabolome coverage and metabolite identification accuracy.
- Integrating MS and NMR data provides a more comprehensive analysis of metabolic changes.
Purpose of the Study:
- To optimize sample preparation, data acquisition, and data handling for simultaneous analysis of metabolomics samples by NMR and direct-infusion electrospray ionization mass spectrometry (DI-ESI-MS).
- To develop protocols for integrating 1D 1H NMR spectra with DI-ESI-MS data.
- To demonstrate the utility of integrated metabolomics data for identifying metabolites involved in disease processes.
Main Methods:
- Optimization of sample preparation for dual NMR and MS analysis.
- High-throughput positive-ion DI-ESI-MS for complex mixtures.
- Data handling protocols using multiblock bilinear factorizations (multiblock principal component analysis [MB-PCA] and multiblock partial least squares [MB-PLS]).
- Utilized backscaled loadings, accurate mass measurements, and tandem MS for metabolite identification.
Main Results:
- Successfully optimized protocols for simultaneous NMR and DI-ESI-MS analysis from a single sample.
- Demonstrated the effectiveness of multiblock analyses (MB-PCA, MB-PLS) for integrating diverse metabolomics datasets.
- Identified specific metabolites contributing to class separation in neurotoxin-induced cell death models.
Conclusions:
- Integration of NMR and DI-ESI-MS datasets significantly improves metabolome analysis.
- The developed methodology enhances the accuracy and coverage of metabolite identification.
- This integrated approach provides valuable insights into metabolic changes, particularly in studies of neurotoxin involvement in dopaminergic cell death.
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