Improved Metabolome Coverage and Increased Confidence in Unknown Identification Through Novel Automated Acquisition Strategy Combining Sequential Injections and MSn

Importance of Topic

Comprehensive profiling of small molecules in complex biological samples is essential for understanding metabolic pathways, discovering biomarkers, and advancing drug development. Traditional data‐dependent acquisition (DDA) often misses low‐abundance metabolites and generates redundant, uninformative spectra, limiting identification confidence and coverage. An optimized, data‐informed acquisition strategy can overcome these bottlenecks and deliver deeper metabolome insights.

Objectives and Study Overview

This study introduces AcquireX, a novel automated acquisition workflow designed to maximize unique precursor interrogation via sequential injections and intelligent inclusion/exclusion lists. Applied to human plasma (NIST SRM1950), the approach was benchmarked against conventional DDA to assess improvements in metabolome coverage, spectral quality, and identification confidence.

Methodology and Instrumentation

The experimental workflow encompassed:

• Sample Preparation: Protein precipitation of human plasma with methanol (3:1 v/v), centrifugation, evaporation, and reconstitution in 0.1% formic acid.
• Chromatography: Separation on a Hypersil GOLD™ C18 column (15 cm×2.1 mm, 1.9 µm) using water and methanol both containing 0.1% formic acid.
• Mass Spectrometry: Thermo Scientific Vanquish™ UHPLC coupled to an Orbitrap ID-X™ Tribrid mass spectrometer, employing AcquireX for iterative DDA, automatic blank exclusion, and real‐time list updating.
• Data Analysis: Thermo Scientific Compound Discoverer™ 3.0 for feature detection, spectral matching (mzCloud), and compound annotation.

Results and Discussion

AcquireX delivered marked improvements over traditional DDA:

• Metabolome Coverage: After three injections, AcquireX increased the number of unique fragmented precursors by 139% in human plasma.
• Reduction of Background: Automated exclusion of blank contaminants reduced >70% of uninformative MS/MS spectra, focusing duty cycle on true metabolites.
• Enhanced Low‐Abundance Sampling: Sequential priority of previously unfragmented ions enabled detection of lower‐intensity features across injections.
• MSn Flexibility: Multi‐stage fragmentation (HCD, CID) was performed on all prioritized precursors, generating rich product‐ion spectra for structural elucidation.
• Identification Confidence: Spectral matching against mzCloud increased both exact and similarity identifications by over 100%, and MSn data supported high‐confidence ChemSpider annotations.

Benefits and Practical Applications

This workflow delivers several key advantages for analytical laboratories:

• Greater depth of coverage in untargeted metabolomics studies.
• Reduced acquisition of redundant or background spectra, improving instrument efficiency.
• Enhanced confidence in compound identification via richer MS/MS and MSn datasets.
• Streamlined method development through automated inclusion/exclusion management.

Future Trends and Potential Applications

Ongoing advances may integrate AcquireX with real‐time database searching and machine‐learning–driven prioritization to further improve selectivity. Expansion into lipidomics, imaging mass spectrometry, and single‐cell analyses could leverage the automated sequential acquisition paradigm. Coupling with ion mobility separation or ion‐reaction chemistries may provide additional structural resolution.

Conclusion

AcquireX represents a significant advance in data‐dependent acquisition for metabolomics, delivering richer, more targeted fragmentation data and expanding metabolome coverage. By intelligently excluding background ions and prioritizing novel precursors across sequential injections, this approach enhances identification rates and deepens biological interpretation. Implementing such data‐informed workflows will be pivotal for high‐throughput, high‐confidence metabolomic investigations.

Reference

This study and results are documented in the Thermo Fisher Scientific application note titled “Improved Metabolome Coverage and Increased Confidence in Unknown Identification Through Novel Automated Acquisition Strategy Combining Sequential Injections and MSn.”