A systemic workflow for profiling metabolome and lipidome in tissue.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Metabolomics

Background:

• Accurate metabolome and lipidome analysis is crucial for understanding biological processes.
• Traditional methods often suffer from incomplete sample analysis due to limited tissue amounts.
• Developing robust and efficient sample preparation and metabolite identification strategies is essential.

Purpose of the Study:

• To develop simple, efficient sample preparation procedures for metabolome and lipidome analysis.
• To establish an advanced metabolite identification (MetID) strategy integrating multiple MS data features and in silico simulation.
• To improve metabolite coverage and extraction efficiency compared to existing methods.

Main Methods:

• Developed a two-step successive extraction protocol for small tissue samples.
• Implemented a MetID strategy combining MS information mining (adducts, in-source CID, ESI polarity, CFIs, CNLs, multimers) and in silico MS simulation.
• Utilized manual and in silico filtering for feature recognition and structure elucidation.

Main Results:

• The new sample preparation method offers advantages in metabolite coverage, extraction efficiency, robustness, and ease of use.
• The MetID strategy successfully identified a large number of features, including adduct ions, in-source CID fragments, and characteristic ions.
• Structurally characterized 2.5 times more metabolites compared to traditional methods, with potential for identifying unknowns.

Conclusions:

• The developed sample preparation and MetID strategy significantly enhances the structural characterization of metabolites.
• This integrated approach overcomes limitations of insufficient tissue amounts and expands metabolite identification capabilities.
• The strategy is valuable for identifying metabolites, especially those lacking MS/MS library data.