LipidQuant 1.0: automated data processing in lipid class separation-mass spectrometry quantitative workflows

Area of Science:

• Biochemistry
• Analytical Chemistry
• Computational Biology

Background:

• Lipidomic quantitation is crucial for understanding biological processes.
• Existing methods face challenges with co-ionization and require robust data processing.
• Automated workflows are needed to improve efficiency and accuracy in lipid analysis.

Purpose of the Study:

• To introduce LipidQuant 1.0, a novel tool for automated lipidomic data processing.
• To detail the workflow of LipidQuant 1.0, including lipid identification, quantitation, and isotopic correction.
• To demonstrate the utility of LipidQuant 1.0 in analyzing human serum samples.

Main Methods:

• Development of LipidQuant 1.0 software for automated data processing.
• Integration of lipid class separation techniques (HILIC, SFC) with high-resolution mass spectrometry.
• Application of the LipidQuant workflow to human serum samples for validation.

Main Results:

• LipidQuant 1.0 provides automated lipid identification and quantitation.
• The tool incorporates isotopic correction for enhanced accuracy.
• Successful application to a small cohort of human serum samples demonstrates its practical utility.

Conclusions:

• LipidQuant 1.0 offers an efficient and accurate solution for lipidomic quantitation.
• The tool supports workflows utilizing lipid class separation, crucial for minimizing co-ionization effects.
• LipidQuant 1.0 is freely available, facilitating broader adoption in lipidomic research.