Increasing Confidence in Non-Targeted Metabolite Identification with Library Comparison and Simplified Unknown Analysis Workflow with Novel Software Solution

Importance of the Topic

Non-targeted metabolomics enables comprehensive profiling of small molecules in biological samples, supporting biomarker discovery and systems biology research. The integration of rich MS1 data with targeted MS/MS fragmentation enhances identification confidence but poses a significant data processing challenge.

Objectives and Study Overview

This study introduces a streamlined workflow using novel software to combine high-resolution MS1 scans and iterative data-dependent MS/MS in a unified analysis platform. The goal is to simplify feature extraction, statistical evaluation, and compound identification in untargeted metabolomics.

Methodology and Instrumentation

Metabolites were extracted from mouse plasma using Captiva Lipid EMR plates and separated by robust HILIC chromatography. A pooled QC sample underwent iterative MS/MS injections with an accumulating exclusion list, while individual samples were analyzed by MS1. Data files were processed through a six-step software workflow including setup, alignment, normalization, filtering, statistics, and identification.

Used Instrumentation

• Agilent Revident LC/Q-TOF with temperature-inert flight tube detector
• Agilent 1290 Infinity III UHPLC system
• Captiva Lipid EMR plates for sample preparation

Key Results and Discussion

The software consolidated MS1 and MS/MS data to identify 2705 features in negative mode and 1996 in positive mode. Statistical analysis revealed significant differences in male versus female mouse plasma, with 222 features annotated in METLIN and 58 tentative structures proposed via direct export to SIRIUS CSI:FingerID. Mirror plots and isotope matching facilitated confident assignment.

Benefits and Practical Applications

• Unified interface for combined MS1 and MS/MS workflows
• Automated iterative exclusion for deeper metabolite coverage
• Direct export to external tools for structure elucidation
• High-quality figure exports for publication

Future Trends and Potential Uses

Further developments may integrate machine learning for improved annotation, expand spectral libraries, and leverage cloud computing for real-time analysis and collaboration across laboratories.

Conclusion

This integrated workflow streamlines untargeted metabolomics analysis, enhancing identification confidence and reducing the barrier to adoption in diverse research and industrial settings.

References

• Yannell KE et al An End-to-End Targeted Metabolomics Workflow Agilent Application Note 5994-5628EN 2023
• Agilent ChemVista Library Manager Agilent Technical Overview 5994-5924EN 2023