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Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
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HERMES: a molecular-formula-oriented method to target the metabolome.

Roger Giné1, Jordi Capellades1,2, Josep M Badia1,2

  • 1Universitat Rovira i Virgili, Department of Electronic Engineering & IISPV, Tarragona, Spain.

Nature Methods
|November 2, 2021
PubMed
Summary

HERMES, a new method for untargeted metabolomics, enhances metabolite identification by optimizing MS2 scans using LC/MS1 data. This approach improves sensitivity, selectivity, and annotation accuracy for comprehensive metabolome analyses.

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Environmental Science

Background:

  • Comprehensive metabolome analyses are crucial across various scientific disciplines.
  • Current untargeted metabolomics methods using MS1 and MS2 acquisition exhibit limitations in metabolite identification rates.
  • Existing data-dependent acquisition (DDA) strategies require optimization for improved specificity and sensitivity.

Purpose of the Study:

  • To introduce HERMES, a novel molecular-formula-oriented, peak-detection-free method for untargeted metabolomics.
  • To enhance MS2 acquisition strategies by utilizing raw LC/MS1 information.
  • To improve metabolite identification rates, sensitivity, selectivity, and annotation accuracy in metabolomic studies.

Main Methods:

  • HERMES employs a molecular-formula-oriented approach, bypassing traditional peak detection.
  • It leverages raw LC/MS1 data to intelligently guide and optimize MS2 acquisition.
  • The method was validated using environmental water, Escherichia coli, and human plasma extracts.

Main Results:

  • HERMES demonstrated increased biological specificity in MS2 scans compared to DDA.
  • Improved mass spectral similarity scoring and higher metabolite identification rates were achieved.
  • The method successfully enhanced sensitivity, selectivity, and annotation of metabolites across diverse sample types.

Conclusions:

  • HERMES offers a significant advancement in untargeted metabolomics, overcoming limitations of current DDA approaches.
  • The method provides improved accuracy and efficiency for metabolite identification and annotation.
  • HERMES is accessible as an R package with a graphical interface, facilitating its adoption in research.