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基于系统非靶向 UHPLC-Q-TOF-MS 的脂质组学工作流程，可提高血清中脂质亚类的检测和注释能力。

Systematic untargeted UHPLC-Q-TOF-MS based lipidomics workflow for improved detection and annotation of lipid sub-classes in serum.

机构信息

Metabolomics Research Facility, Institute of Nuclear Medicine and Allied Sciences (INMAS), DRDO, S. K. Mazumdar Road, Timarpur, Delhi, 110054, India.

Sciex, Bangalore, India.

出版信息

Metabolomics. 2023 Mar 27;19(4):24. doi: 10.1007/s11306-023-01983-2.

DOI:10.1007/s11306-023-01983-2
PMID:36971892
Abstract

INTRODUCTION AND OBJECTIVE

Taking into consideration the challenges of lipid analytics, present study aims to design the best high-throughput workflow for detection and annotation of lipids.

MATERIAL AND METHODS

Serum lipid profiling was performed on CSH-C18 and EVO-C18 columns using UHPLC Q-TOF-MS and generated lipid features were annotated based on m/z and fragment ion using different software.

RESULT AND DISCUSSION

Better detection of features was observed in CSH-C18 than EVO-C18 with enhanced resolution except for Glycerolipids (triacylglycerols) and Sphingolipids (sphingomyelin).

CONCLUSION

The study revealed an optimized untargeted Lipidomics-workflow with comprehensive lipid profiling (CSH-C18 column) and confirmatory annotation (LipidBlast).

摘要

简介和目的

考虑到脂质分析的挑战，本研究旨在设计最佳的高通量工作流程，以检测和注释脂质。

材料和方法

使用 UHPLC Q-TOF-MS 在 CSH-C18 和 EVO-C18 柱上进行血清脂质分析，根据质荷比（m/z）和碎片离子使用不同的软件对生成的脂质特征进行注释。

结果与讨论

与 EVO-C18 相比，CSH-C18 柱对特征的检测更好，分辨率更高，但甘油酯（三酰基甘油）和鞘脂（神经酰胺）除外。

结论

该研究揭示了一种优化的非靶向脂质组学工作流程，具有全面的脂质分析（CSH-C18 柱）和确证性注释（LipidBlast）。

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