An alignment algorithm for LC-MS-based metabolomics dataset assisted by MS/MS information.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Metabolomics

Background:

• Liquid chromatography-mass spectrometry (LC-MS) is a crucial analytical technique in metabolomics.
• Accurate peak alignment of LC-MS data is essential for reliable metabolomics studies.
• Existing alignment methods can struggle with significant retention time drift in datasets.

Purpose of the Study:

• To develop a robust MS/MS-based peak alignment method for LC-MS metabolomics data.
• To address challenges posed by larger retention time drifts in metabolomics datasets.
• To provide a reliable and simple alignment solution for LC-MS data.

Main Methods:

• A novel strategy for screening endogenous reference variables using MS, MS/MS, and retention time.
• Selection and confirmation of multiple robust endogenous reference variables across samples.
• Correction of metabolite feature retention times using local linear regression with identified reference variables.
• Final peak alignment based on corrected retention time, MS, and MS/MS data.

Main Results:

• The developed MS/MS-based method demonstrated superior performance compared to two other alignment techniques.
• The method effectively handles metabolomics data exhibiting substantial retention time drift.
• The approach provides a simple, robust, and reliable solution for aligning LC-MS metabolomics datasets.

Conclusions:

• The proposed peak alignment method is highly suitable for LC-MS metabolomics data, especially with significant retention time variations.
• This technique enhances the reliability and accuracy of metabolomics data analysis.
• The developed approach offers a valuable tool for researchers in the field of metabolomics.