Three-dimensional Kendrick mass plots as a tool for graphical lipid identification.

Area of Science:

• Lipidomics
• Analytical Chemistry
• Biochemistry

Background:

• Lipidomics research requires efficient identification of lipids in complex biological samples.
• Advances in liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) allow rapid data acquisition but create interpretation challenges.
• Data analysis remains a bottleneck in comprehensive lipidome mapping.

Purpose of the Study:

• To develop a rapid and accurate method for lipid identification in complex matrices using LC/HRMS data.
• To improve the interpretation of multidimensional lipidomics datasets.

Main Methods:

• A novel 3D Kendrick mass plot analysis was developed for lipid identification.
• Lipids were separated by head group using hydrophilic interaction liquid chromatography (HILIC) coupled to HRMS.
• An optimized MZmine 2 workflow processed LC/HRMS data, and features were plotted in 3D Kendrick mass plots for class identification and database matching.

Main Results:

• The 3D Kendrick mass plot method significantly accelerated lipid species identification in Chlamydomonas reinhardtii.
• A total of 106 lipid species were identified across seven lipid classes.
• The method facilitated accurate lipid class identification and detection of potential annotation errors.

Conclusions:

• Integrating chromatographic retention time with Kendrick mass plot analysis enables rapid and accurate LC/HRMS data analysis.
• 3D Kendrick mass plots enhance lipid class identification and streamline the spotting of misannotated lipid species.
• This approach is valuable for analyzing complex lipidomics data, as demonstrated in a green alga sample.