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Related Concept Videos

Mass Spectrometry: Molecular Fragmentation Overview01:20

Mass Spectrometry: Molecular Fragmentation Overview

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The ionization of a molecule into a molecular ion inside the mass spectrometer causes instability in the molecule's structure due to the loss of an electron. This eventually leads to the fragmentation or breaking of some bonds in the molecule. The fragmentation occurs predominantly at specific bonds to yield relatively stable fragments.
One type of fragmentation pattern is the cleavage of a single bond in the molecular ion. The cleavage leads to a radical and a cation. The cleavage can...
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Mass Spectrometry: Branched Alkane Fragmentation01:29

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This lesson delves into the mass spectrometry of branched alkane fragmentation. Branched alkanes possess secondary or tertiary carbon atoms, which generate relatively stable carbocations if the cleavage occurs at the branching point. The high stability of carbocations drives the instant fragmentation of branched alkanes. Accordingly, the branched alkane's molecular ion peak is very weak or invisible in the mass spectra, especially in comparison to a linear alkane.
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Related Experiment Video

Updated: Oct 3, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems

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  • 1Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Rd., La Jolla, CA, 92037, USA.

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|February 22, 2022
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Summary

This study introduces a new framework for analyzing diverse metabolomics data, enabling comparisons across studies and data types. This approach facilitates systems-level interpretation of metabolic pathways for broader research applications.

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Area of Science:

  • Metabolomics
  • Systems Biology
  • Bioinformatics

Background:

  • Archived metabolomics data are a valuable resource but lack tools for meta-analysis of heterogeneous data types.
  • Direct comparison of diverse metabolomics datasets is challenging within a unified workflow.

Purpose of the Study:

  • To present a novel framework for the meta-analysis of metabolic pathways.
  • To enable interpretation of metabolomics data alongside proteomic and transcriptomic data.
  • To facilitate comparison of heterogeneous metabolomics data from various online repositories and local sources.

Main Methods:

  • Developed a framework for meta-analysis of metabolic pathways.
  • Integrated proteomic and transcriptomic data interpretation.
  • Enabled comparison of data from XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights.
  • Applied the workflow to colon cancer and Alzheimer's disease/mild cognitive impairment studies.

Main Results:

  • Demonstrated high-throughput capability for systems-level interpretation of metabolic pathways.
  • Successfully applied the framework to independent colon cancer studies, integrating omics data.
  • Validated the workflow on multimodal data from Alzheimer's disease and mild cognitive impairment studies.

Conclusions:

  • The developed framework overcomes key bottlenecks in analyzing diverse metabolomics datasets.
  • Facilitates the comprehensive exploitation of archival metabolomics data for metabolism research and systems biology.
  • Enhances knowledge dissemination through collaboration with Metabolomics Workbench and LIPID MAPS.