Grant application resource for Orbitrap IQ-X Tribrid mass spectrometer

Importance of the Topic

The thorough characterization of small molecules in complex matrices is crucial across metabolomics, lipidomics, natural products, pharmaceutical analysis, and toxicology.
High-resolution accurate mass and MSn spectral data are key to confidently identifying unknown compounds against challenging backgrounds and isomers.

Objectives and Study Overview

This white paper presents the Thermo Scientific Orbitrap IQ-X Tribrid Mass Spectrometer, designed to advance small molecule identification, quantitation, and structural elucidation.
It compares this platform to previous Orbitrap and QTOF systems, highlighting innovations in acquisition strategies, fragmentation techniques, and workflow automation.

Methodology

• Tribrid architecture combining quadrupole, linear ion trap, and ultra-high-field Orbitrap analyzers for parallel isolation and detection.
• Multiple fragmentation modes: HCD, CID, and optional UVPD at any MSn stage for enhanced structural detail.
• Real-Time Library Search: on-the-fly spectral matching against local mzVault/mzCloud libraries to guide MSn trigger decisions.
• AcquireX intelligent data-dependent acquisition workflows for sample-specific inclusion/exclusion and deep scanning across re-injections.
• Advanced Precursor Detection optimized for small molecules and halogenated species.
• EASY-IC internal calibration and Auto-Ready ion source for continuous <1 ppm mass accuracy and automated maintenance.

Instrumentation Used

• Orbitrap IQ-X Tribrid Mass Spectrometer coupled with Vanquish Duo UHPLC System.
• EASY-IC and Auto-Ready calibration sources.
• Optional 1 M resolution ultra-high-field Orbitrap analyzer upgrade.
• Optional 213 nm UVPD source for complementary photodissociation.
• Optional FAIMS Pro Duo interface for differential ion mobility separation.

Main Results and Discussion

The Orbitrap IQ-X achieved up to 1,000,000 resolution at m/z 200 enabling resolved fine isotopic structures and elemental composition assignment.
Real-Time Library Search increased confidence in unknown annotation and reduced acquisition of irrelevant features.
AcquireX workflows expanded coverage of low-abundance metabolites and isomeric lipids while minimizing background noise.
UVPD delivered unique fragment ions for lipid double-bond localization and flavonoid structural elucidation.
FAIMS enhanced LOQ and selectivity for targeted small molecules.

Benefits and Practical Applications

• Confident metabolite and lipid annotations in untargeted studies.
• Rapid, automated method templates for metabolomics, lipidomics, metabolite ID, extractables/leachables.
• High-throughput structural elucidation for natural products and oligonucleotides.
• Reduced sample complexity and false positives through intelligent acquisition.
• Improved maintenance and uptime via automated calibration.

Future Trends and Opportunities

Integration of advanced AI-driven data interpretation with real-time acquisition will further accelerate unknown identification.
Expanding spectral libraries and cloud-based databases will enhance Real-Time Library Search capabilities.
Broader adoption of ion mobility separation and ultra-high-resolution options will drive deeper coverage in single-cell and clinical applications.
Continuous automation of end-to-end workflows will streamline metabolomics and pharmaceutical impurity profiling.

Conclusion

The Orbitrap IQ-X Tribrid MS redefines small molecule analysis by uniting Tribrid hardware innovation, multiple fragmentation modes, real-time library search, and intelligent AcquireX workflows.
Its modular options, ultra-high resolution, and automated calibration deliver unparalleled confidence, sensitivity, and ease-of-use for comprehensive unknown characterization.

References

• Eliuk S and Makarov A. Evolution of Orbitrap Mass Spectrometry Instrumentation. Annu. Rev. Anal. Chem. 2015;8:61–80.
• Patti G, Dunn W, Creek D, Sumner L. The Journey from Features to Compound Identification in Metabolomics: When Will We Get There? The Scientist, 2018; eBook #65354.
• Peake D. High-Resolution Compound Identification in Metabolomics: A Review of Current Practices. Review Paper #65356.
• Mahieu NG and Patti GJ. Systems-Level Annotation of a Metabolomics Data Set Reduces 25,000 Features to Fewer than 1,000 Unique Metabolites. Anal. Chem. 2017;89:10397–10406.