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Mass Spectrum: Interpretation01:24

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An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
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Related Experiment Video

Updated: Dec 14, 2025

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

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Identifying unknown metabolites using NMR-based metabolic profiling techniques.

Isabel Garcia-Perez1, Joram M Posma2,3, Jose Ivan Serrano-Contreras1

  • 1Division of Digestive Diseases, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Hammersmith Campus, Imperial College London, London, UK.

Nature Protocols
|July 19, 2020
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Summary
This summary is machine-generated.

This study introduces an automated system for identifying molecular structures from NMR spectroscopy data, improving biomarker discovery in metabolic profiling. The efficient, cost-effective approach enhances chemical space coverage for faster, more accurate biomarker assignment.

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Area of Science:

  • Metabolomics
  • Biomarker Discovery
  • Analytical Chemistry

Background:

  • Metabolic profiling offers insights into physiological and pathological processes.
  • Current methods lack automated annotation and standardized structure elucidation for biomarkers.
  • Identifying molecular species from NMR data is crucial but challenging.

Purpose of the Study:

  • To describe a comprehensive system for identifying molecular species from NMR spectroscopy-based metabolic phenotyping.
  • To provide modular workflows for automated annotation and structure elucidation of biomarkers.
  • To enhance the efficiency and accuracy of biomarker discovery in metabolomics.

Main Methods:

  • Development of an eight-module, sequential workflow for data analysis.
  • Utilized statistical spectroscopic tools: Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM), and Resolution-Enhanced (RED)-STORM.
  • Integrated multi-dimensional NMR, separation techniques (SPE, LC), hyphenated platforms (LC-NMR-MS), and database extraction.

Main Results:

  • The system enables identification of molecular species from NMR spectra.
  • Modular workflows range in difficulty, with full implementation taking up to a month.
  • Faster identification (2-3 days) is possible for simpler cases.
  • Achieved increased chemical space coverage and improved accuracy in biomarker assignment.

Conclusions:

  • The developed system provides an efficient and cost-effective approach to biomarker discovery.
  • It addresses the limitations of automated annotation and standardized structure elucidation in metabolomics.
  • Facilitates faster and more accurate assignment of NMR-generated biomarkers for metabolic phenotyping studies.