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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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Enhanced detection and annotation of small molecules in metabolomics using molecular-network-oriented parameter

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  • 1Human Nutrition Program, The Ohio State University, Columbus, Ohio 43210, USA. zhu.2484@osu.edu.

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Optimizing data acquisition parameters improves untargeted metabolomics. This study details key settings for generating high-quality metabolic features for molecular networking on the GNPS platform.

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Area of Science:

  • * Analytical Chemistry
  • * Biochemistry
  • * Natural Products Chemistry

Background:

  • * Untargeted metabolomics generates vast datasets requiring robust analysis.
  • * Data-dependent acquisition (DDA) with tandem mass spectrometry (MS) is crucial for feature identification.
  • * Systematic studies on data acquisition parameters for molecular network (MN) analysis on the Global Natural Products Social Molecular Networking (GNPS) platform are lacking.

Purpose of the Study:

  • * To investigate key data acquisition parameters influencing the formation and quality of MNs.
  • * To propose and evaluate an optimization workflow for Thermo Scientific QE Hybrid Orbitrap instruments.
  • * To compare phytochemical profiles of black raspberry extracts using GNPS MNs.

Main Methods:

  • * Evaluation of MS1 resolution, normalized collision energy (NCE), intensity threshold, and exclusion time.
  • * Application of these parameters in data collection for GNPS MN analysis.
  • * Comparative analysis of black raspberry extracts based on generated MNs.

Main Results:

  • * Demonstrated the impact of MS1 resolution, NCE, intensity threshold, and exclusion time on GNPS analyses.
  • * Developed and described an optimization workflow for Thermo Scientific QE Hybrid Orbitrap instruments.
  • * Successfully compared phytochemical contents of two black raspberry extract forms using GNPS MNs.

Conclusions:

  • * Key data acquisition parameters significantly affect the quality of metabolic features for MN analysis.
  • * The proposed optimization workflow enhances MN generation for specific instruments.
  • * This study provides a framework for natural product analysis workflows using GNPS and similar setups.