Optimizing targeted/untargeted metabolomics by automating gas chromatography/mass spectrometry workflows

Area of Science:

• Analytical Chemistry
• Metabolomics
• Food Science

Background:

• Automated analysis of complex samples like tea requires advanced computational tools.
• Traditional methods for compound identification in chromatography often rely on high-resolution mass spectrometry data, limiting accessibility.

Purpose of the Study:

• To develop and apply novel database building and mass spectrum (MS) subtraction algorithms for automated, sequential two-dimensional gas chromatography/mass spectrometry (GC-GC/MS).
• To demonstrate the utility of these tools for compound identification in tea without high-resolution MS data.

Main Methods:

• Development of a database building tool incorporating full mass spectrum subtraction, independent of high-resolution MS.
• Application of the software to analyze GC-GC/MS data from high-elevation Yunnan tea, building a target compound database.
• Utilizing spectral deconvolution and GC/MS for compound identification in both high and low-elevation tea samples.
• Employing non-targeted MS subtraction for the discovery of unique metabolites.

Main Results:

• A database of 350 target compounds was successfully built from high-elevation tea using the new algorithms.
• Spectral deconvolution identified 285 compounds in the same high-elevation tea via GC/MS.
• Targeted GC/MS analysis of low-elevation tea detected 275 compounds.
• Non-targeted MS subtraction revealed eight additional metabolites unique to low-elevation tea.

Conclusions:

• The developed database building and MS subtraction algorithms provide an effective method for automated compound identification in GC-GC/MS data.
• This approach significantly enhances the ability to analyze complex natural products like tea, even without high-resolution MS.
• The study successfully identified numerous compounds and unique metabolites differentiating tea from different elevations.