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Lipid Droplet Isolation for Quantitative Mass Spectrometry Analysis
Published on: April 17, 2017
Basic analytical systems for lipidomics by mass spectrometry in Japan
Ryo Taguchi1, Mashahiro Nishijima, Takao Shimizu
1Department of Metabolome, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Methods in Enzymology
|October 24, 2007
Summary
Mass spectrometry (MS) is vital for analyzing phospholipid molecular species. This study explores multiple MS approaches, including LC-MS/MS and targeted methods, to identify and profile these lipids for functional elucidation.
Area of Science:
- Lipidomics
- Analytical Chemistry
- Biochemistry
Background:
- Phospholipids are crucial cellular components, and understanding their molecular species is essential for elucidating their functions.
- Mass spectrometry (MS) has emerged as a powerful tool for comprehensive lipid analysis.
Purpose of the Study:
- To evaluate and compare different mass spectrometry-based approaches for the identification and profiling of phospholipid molecular species.
- To provide accessible data resources for phospholipid research.
Main Methods:
- Global and shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS) using data-dependent scanning.
- Structure-focused MS methods, including precursor ion scanning and neutral loss scanning.
- Theoretically expanded multiple reaction monitoring for targeted analysis of low-abundance molecules.
Main Results:
- The study presents multiple MS strategies for detailed phospholipid analysis.
- A search engine, Lipid Search, facilitates data acquisition and identification of probable molecular species based on compensated ion intensities.
- Identified species are automatically profiled and visualized, with data accessible through Lipid Bank and MassBank.
Conclusions:
- Multiple MS techniques offer complementary strategies for comprehensive phospholipid molecular species identification and profiling.
- Integrated data analysis platforms and public databases enhance the accessibility and utility of lipidomics data.
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