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RECOGNITION AND AVOIDANCE OF ION SOURCE-GENERATED ARTIFACTS IN LIPIDOMICS ANALYSIS.

Changfeng Hu1, Wenqing Luo1, Jie Xu2

  • 1College of Basic Medical Sciences, Zhejiang Chinese Medical University, 548 Bingwen Road, Hangzhou, Zhejiang, 310053, China.

Mass Spectrometry Reviews
|September 30, 2020
PubMed
Summary

In-source fragmentation artifacts in mass spectrometry (MS)-based lipidomics can lead to misidentification of lipids. This study details these artifacts and strategies to mitigate them for more accurate lipidomics research.

Keywords:
MALDI-MSIartifactsfunctional lipidomicsin-source fragmentationlipidomicsmass spectrometry

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Area of Science:

  • Lipidomics
  • Mass Spectrometry
  • Analytical Chemistry

Background:

  • Lipid research is expanding due to newly discovered biological roles.
  • Mass spectrometry (MS)-based lipidomics offers high sensitivity and specificity for lipid analysis.
  • In-source fragmentation artifacts are a common issue across various MS ion sources.

Purpose of the Study:

  • To summarize artifacts generated from in-source fragmentation in MS-based lipidomics.
  • To discuss the impact of these artifacts on lipid identification and interpretation.
  • To present strategies for avoiding or reducing these artifacts in lipidomics studies.

Main Methods:

  • Review of common MS-based lipidomics approaches including LC-MS, shotgun lipidomics, and MALDI-MS imaging.
  • Analysis of artifact generation mechanisms in different ion sources.
  • Identification of strategies to minimize in-source fragmentation.

Main Results:

  • In-source fragmentation generates artifacts that can mimic endogenous lipid species.
  • These artifacts can lead to misannotation, false identification, and incorrect phenotypic attribution.
  • Common lipidomics techniques are susceptible to these artifacts.

Conclusions:

  • Recognizing and avoiding in-source fragmentation artifacts is crucial for accurate lipidomics.
  • Mitigation strategies can improve the reliability of MS-based lipidomics.
  • Enhanced accuracy in lipidomics will advance functional lipidomics, metabolism research, and precision medicine.