Automated lipid identification and quantification by multidimensional mass spectrometry-based shotgun lipidomics

Area of Science:

• Biochemistry
• Analytical Chemistry
• Computational Biology

Background:

• Lipidomics is crucial for understanding biological processes.
• High-throughput analysis of lipid molecular species is challenging.
• Existing methods for lipid identification and quantification can be time-consuming.

Purpose of the Study:

• To develop and present strategies for automated identification and quantification of individual lipid molecular species.
• To enable high-throughput analysis of lipidomics data.
• To create a program adaptable for various shotgun lipidomics approaches.

Main Methods:

• Utilized multidimensional mass spectrometry-based shotgun lipidomics (MDMS-SL) data.
• Employed a building block analysis strategy using precursor ion scans and neutral loss scans for identification.
• Implemented a two-step quantitation procedure with a linear dynamic range over three orders of magnitude.
• Developed a program screening against a database of over 36,000 potential lipid molecular species.

Main Results:

• Successfully automated the identification and quantification of individual lipid molecular species.
• Achieved high-throughput screening of a comprehensive lipid database.
• Demonstrated a robust two-step quantitation method with broad dynamic range.
• Verified results through redundant quantification of multidimensional mass spectra.

Conclusions:

• The developed automated program significantly accelerates high-throughput lipid analysis using MDMS-SL.
• The strategy is adaptable to other mass spectrometry-based lipid analysis techniques.
• This advancement facilitates deeper insights into lipidomics research.