Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Area of Science:

• Analytical Chemistry
• Biochemistry
• Bioinformatics

Background:

• Metabolomics is crucial for understanding biological systems, but identifying unknown metabolites remains a significant challenge.
• Current methods like library matching are limited by small spectral libraries, hindering comprehensive analysis.
• The vast chemical diversity of metabolites makes structure elucidation time-consuming and difficult.

Purpose of the Study:

• To review advancements in using multi-stage mass spectrometry (MSn) trees for metabolite identification over the past decade.
• To highlight the potential of fragmentation trees and mass spectral trees in overcoming current metabolomics bottlenecks.
• To discuss algorithms, software, and databases that support MSn-based metabolite structure elucidation.

Main Methods:

• Review of literature on multi-stage mass spectrometry (MSn) techniques.
• Analysis of algorithms and computational tools for fragmentation tree and mass spectral tree construction.
• Examination of databases and software for metabolite identification using MSn data.

Main Results:

• Multi-stage mass spectrometry (MSn) offers a promising approach to generate richer data for metabolite identification.
• Fragmentation trees and mass spectral trees provide valuable insights into fragmentation processes and compound substructures.
• Advancements in algorithms and software have improved the implementation of MSn-based identification strategies.

Conclusions:

• MSn-based approaches, particularly fragmentation and mass spectral trees, are essential for resolving the metabolite identification bottleneck.
• Improved data acquisition and computational tools are critical for advancing metabolomics interpretation.
• This review consolidates recent progress, providing a foundation for future research in metabolite structure elucidation.