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Proteomics. 2019 Jul 10:e1900070. doi: 10.1002/pmic.201900070. [Epub ahead of print]

Strategies to Improve/Eliminate the Limitations in Shotgun Lipidomics.

Hu C1, Duan Q1, Han X2,3.

Author information

1
College of Basic Medical Sciences, Zhejiang Chinese Medical University, 548 Bingwen Road, Hangzhou, Zhejiang, 310053, China.
2
Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA.
3
Division of Diabetes, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229, USA.

Abstract

Direct infusion-based shotgun lipidomics is one of the most powerful and useful tools in comprehensive analysis of lipid species from lipid extracts of various biological samples with high accuracy/precision. However, despite many advantages, the classical shotgun lipidomics suffers some general dogmas of limitations, such as ion suppression, ambiguous identification of isobaric/isomeric lipid species, and ion source-generated artifacts, restraining the applications in analysis of low-abundance lipid species, particularly those less ionizable or isomers that yield almost identical fragmentation patterns. This article reviews the strategies (such as modifier addition, prefractionation, chemical derivatization, charge feature utilization) that have been employed to improve/eliminate these limitations in modern shotgun lipidomics approaches (e.g., high mass resolution mass spectrometry-based and multidimensional mass spectrometry-based shotgun lipidomics). Therefore, with the enhancement of these strategies for shotgun lipidomics, comprehensive analysis of lipid species including isomeric/isobaric species is achieved in a more accurate and effective manner, greatly substantiating the aberrant lipid metabolism, signaling trafficking, and homeostasis under pathological conditions.

KEYWORDS:

in-source fragmentation; ion suppression; isobaric/isomeric species; multidimensional mass spectrometry; shotgun lipidomics

PMID:
31291508
DOI:
10.1002/pmic.201900070

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