Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity

Area of Science:

• Metabolomics
• Analytical Chemistry
• Biochemistry

Background:

• Untargeted LC-MS metabolomics faces challenges in annotating complex data, particularly adducts.
• Distinguishing adduct peaks from isotopes and fragments is difficult due to similar mass differences.

Purpose of the Study:

• To develop and validate a buffer modification workflow (BMW) for improved adduct annotation in LC-MS metabolomics.
• To provide a cost-effective and efficient method for metabolite identification in biological samples.

Main Methods:

• The buffer modification workflow (BMW) involves running samples with two different ammonia-based buffers (¹⁴NH₃-acetate and ¹⁵NH₃-formate).
• Characteristic mass and signal intensity shifts of adduct peaks are analyzed after buffer switching.
• The workflow was applied to yeast and mouse liver samples for LC-MS data annotation.

Main Results:

• The BMW method effectively annotated adducts in yeast metabolomics data, comparable to isotope labeling.
• Application to mouse liver data achieved 95% accuracy in annotating known metabolites and adducts.
• The workflow identified 26% of liver LC-MS features as putative metabolites, with ~2600 matching HMDB or KEGG databases.

Conclusions:

• The buffer modification workflow (BMW) is a simple, convenient, and effective method for adduct annotation in LC-MS metabolomics.
• This approach is suitable for various biological samples, including those difficult to isotope label, such as plants, mammalian tissues, and tumors.