Intelligent data acquisition for automated data reduction leading to increased metabolome annotation

Significance of the topic

Untargeted metabolomics generates thousands of features per sample, but the majority of spectral data correspond to background contaminants and redundant signals. Efficient isolation and fragmentation of biologically relevant ions are essential for metabolite identification, which remains the bottleneck in translating feature lists into biochemical insights for research and quality control applications.

Objectives and study overview

This study aimed to demonstrate Thermo Scientific AcquireX intelligent data acquisition on Orbitrap Exploris and Orbitrap Tribrid instruments. AcquireX automates the annotation and exclusion of nonbiological and redundant features during acquisition, enabling comprehensive and efficient MS and MSn coverage of unique sample components. Four standard reference materials were evaluated: human plasma, yeast extract, green tea extract and non smoker urine, alongside spike experiments with isotopically labeled lipid standards.

Methodology and instrumentation

The AcquireX Deep Scan workflow uses an iterative series of injections to generate blank exclusion lists and sample inclusion lists. A blank run first detects constant and peak shaped background ions which are excluded from subsequent fragmentation. A pooled or individual sample run then generates an inclusion list of sample relevant features. In successive data dependent acquisition injections, inclusion and exclusion lists are updated in real time to prioritize the fragmentation of new, lower abundance precursors. This process supports high resolution accurate mass MS and MSn analyses.

• Orbitrap Exploris mass spectrometer
• Orbitrap Tribrid mass spectrometer
• AcquireX intelligent data acquisition and Deep Scan workflow
• Dynamic inclusion and exclusion lists for iterative DDA

Main results and discussion

Compared to conventional DDA, AcquireX yielded a two to three fold increase in the number of unique compounds with MS/MS spectra across all tested matrices after three injections. Background ion triggered spectra were reduced by more than seventy six percent. Dynamic range expansion was demonstrated by detection and high quality MS/MS spectra of a spiked phosphoethanolamine standard at concentrations ten times lower than possible with traditional DDA. In addition, AcquireX enabled efficient multistage fragmentation, improving the annotation of isomeric metabolites such as flavonoids in green tea extract by providing diagnostic MS3 spectra.

Benefits and practical applications

• Automated filtering of nonbiological and redundant features maximizes instrument duty cycle
• Enhanced coverage of low abundance metabolites and increased confidence in annotations
• Reduced data complexity accelerates downstream processing and structural elucidation
• Applicable to diverse biological samples and compatible with existing spectral libraries

Future trends and opportunities

Continued advances may include integration of real time database matching, machine learning driven precursor prioritization and expansion of acquisition workflows to large molecule and multiomic analyses. Adaptive acquisition strategies promise further improvements in coverage and throughput, facilitating broader adoption across clinical, environmental and industrial applications.

Conclusion

AcquireX intelligent data acquisition introduces a knowledge driven approach that substantially improves fragmentation of unique, biologically relevant metabolites. By harnessing iterative blank exclusion and dynamic inclusion lists, it delivers deeper metabolome coverage, reduces redundant spectra and streamlines metabolite identification workflows.

References

1. Mahieu NG Patti GJ Systems level annotation of a metabolomics data set reduces 25 000 features to fewer than 1000 unique metabolites Anal Chem 89 10397 10406 2017
2. Sindelar M Patti GJ Chemical discovery in the era of metabolomics J Am Chem Soc 142 9097 9105 2020
3. de Jong FA Beecher C Isotopic Ratio Outlier Analysis for accurate biochemical profiling Bioanalysis 4 2303 2314 2012