Computationally unmasking each fatty acyl C=C position in complex lipids by routine LC-MS/MS lipidomics

Area of Science:

• Lipidomics
• Mass Spectrometry
• Computational Biology

Background:

• Identifying carbon-carbon double bond (C=C) positions in complex lipids is crucial for understanding biological processes.
• Current methods for C=C position analysis are not high-throughput and require specialized instrumentation, limiting broad application.
• High-throughput analysis of native lipids is hindered by the inability to precisely determine C=C positions.

Purpose of the Study:

• To develop an automated, chain-specific method for identifying C=C positions in complex lipids.
• To enable high-throughput lipidomic studies by integrating retention time data from routine chromatography with computational analysis.
• To provide a universally applicable computational solution for C=C position determination in lipids.

Main Methods:

• Utilized reverse-phase chromatography tandem mass spectrometry (RPLC-MS/MS) for lipid separation and data acquisition.
• Developed LC=CL, a computational tool leveraging a comprehensive database of over 2400 complex lipid species elution profiles.
• Employed machine learning algorithms within LC=CL for automated and precise C=C position assignments.

Main Results:

• Demonstrated automated, chain-specific identification of C=C positions in complex lipids.
• LC=CL successfully assigned C=C positions using retention time data from routine RPLC-MS/MS.
• Re-evaluation of previous data using LC=CL revealed novel C=C position-dependent specificity of cytosolic phospholipase A2 (cPLA2).

Conclusions:

• LC=CL provides an accessible solution for determining C=C positions in large-scale, high-throughput lipidomic studies.
• The method is adaptable to various chromatographic conditions and MS/MS instrumentation.
• This advancement facilitates deeper understanding of lipid function in physiological and pathological contexts.