Metabolomics: High quality curated HRAM MSn spectral libraries and real time library search for the confident annotation of metabolites

Significance of the Topic

Confident identification of metabolites is key to unlocking the biological insights hidden in untargeted metabolomics datasets. High-resolution accurate mass MSn spectral libraries, combined with real-time library search strategies, can improve the depth and specificity of metabolite annotation. This approach addresses challenges of extracting meaningful information from complex samples and enhances the discovery of both known and unknown compounds.

Study Objectives and Overview

This study describes the development and application of a curated HRAM MSn spectral library optimized for flavonoid analysis, in tandem with an on-instrument Real Time Library Search (RTLS) workflow. The goals include:

• Creating a flavonoid spectral library using authentic standards and multiple fragmentation techniques.
• Implementing an AcquireX deep scan and RTLS-driven MSn acquisition method on the Orbitrap IQ-X.
• Demonstrating the workflow on tea extracts to annotate additional putative flavonoids.

Methodology

Sample preparation involved hot water extraction of green, black, and herbal tea, followed by pooled QC samples. LC-MS data were acquired using an AcquireX Deep Scan heuristic that builds an exclusion list from blanks and an inclusion list from pooled samples. Real Time Library Search (RTLS) was applied to trigger higher‐order MSn events for spectra matching the curated library. Data processing and annotation employed Thermo Scientific Compound Discoverer 3.3 with custom flavonoid and mzCloud spectral libraries.

Instrumentation

• Thermo Scientific Vanquish Horizon UHPLC system with Accucore Vanquish C18+ column.
• Orbitrap IQ-X Tribrid Mass Spectrometer.
• Thermo Scientific AcquireX Deep Scan and Met-IQ RTLS templates.
• Thermo Scientific Mass Frontier 8.0, Compound Discoverer 3.3, and mzVault 2.3 software.

Results and Discussion

• The curated flavonoid library and RTLS enabled annotation of nearly 100 additional putative flavonoids in tea extracts compared to workflows without RTLS.
• RTLS-triggered MS3 spectra increased from 416 to 591 compounds matching the Arita Flavonoid Database, demonstrating improved structural confidence.
• Volcano plot analysis revealed distinct metabolite profiles between green and black tea, highlighting the method's utility in comparative studies.

Benefits and Practical Applications

This targeted deep‐scan approach enhances metabolome coverage and confidence in compound identification, particularly for compound classes of interest. It streamlines analysis in natural product research, food chemistry, and quality control by automating selective MSn acquisition based on real-time spectral matching.

Future Trends and Potential Uses

Expanding high‐quality spectral libraries to other compound classes will broaden RTLS applicability. Integration with machine learning can further refine library matching and predictive fragmentation. Real-time annotation workflows are poised to accelerate discovery in metabolomics, environmental analysis, and clinical research.

Conclusion

The combination of a curated HRAM MSn spectral library and on-instrument RTLS significantly improves the confident annotation of flavonoids in complex samples. The workflow demonstrates enhanced sensitivity, specificity, and automation, providing a robust platform for targeted metabolite discovery.

References

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