Improving Coverage of the Plasma Lipidome Using Iterative MS/MS Data Acquisition Combined with Lipid Annotator Software and 6546 LC/Q-TOF

Significance of the Topic

Comprehensive characterization of the plasma lipidome is essential for understanding physiological processes, biomarker discovery, and quality control in pharmaceutical and clinical research.
Traditional shotgun lipidomics faces challenges in distinguishing isobaric species and suffers from ion suppression, limiting analytical depth and dynamic range.
Reversed-phase liquid chromatography coupled with high-resolution MS/MS improves separation but data-dependent acquisition (DDA) often prioritizes abundant precursors and overlooks low-level lipids.
Iterative MS/MS acquisition overcomes these limitations by excluding previously fragmented ions, thereby enriching coverage of diverse lipid classes.

Objectives and Study Overview

This work evaluates an iterative MS/MS strategy on the Agilent 6546 LC/Q-TOF mass spectrometer combined with Agilent Lipid Annotator software.
By performing multiple injections with a rolling exclusion list, the study quantifies improvements in lipid annotation depth for human plasma.
Comparisons between conventional AutoMS/MS and Iterative MS/MS methods are performed in both positive and negative ionization modes.

Methodology and Instrumentation

• Sample Preparation: Modified Folch extraction of NIST SRM 1950 human plasma, reconstitution in methanol/chloroform (9:1).
• Chromatography: Agilent 1290 Infinity II LC with Poroshell 120 EC-C18 column (3.0×100 mm, 2.7 µm), 50 °C, 0.6 mL/min flow; gradient of 10 mM ammonium acetate and 0.2 mM ammonium fluoride buffers in water/methanol and acetonitrile/methanol/isopropanol.
• Mass Spectrometry: Agilent 6546 LC/Q-TOF with Jet Stream source; MS1/MS2 range m/z 40–1700; acquisition rate 3 spectra/s; collision energy 20 eV (positive) and 25 eV (negative).
• Iterative MS/MS Settings: Narrow isolation width (~1.3 m/z), maximum three precursors per cycle, rolling exclusion duration 0.05 min, intensity-based scan speed targeting 25,000 counts/s.
• Data Analysis: Agilent MassHunter and Lipid Annotator using in silico LipidBlast database for Bayesian-scored annotation, batch processing of multiple injections, and RT-annotated library export.

Main Results and Discussion

Iterative MS/MS increased cumulative annotated lipids by 69 % in positive mode (355 vs. 223) and by 34 % in negative mode (326 vs. 243) over five injections.
Plateau analysis indicated that three to five iterations capture most lipid species, with diminishing gains thereafter.
Sequential exclusion of abundant precursors enriched low-abundance classes such as cholesterol esters, diacylglycerols, and certain phospholipids.
Optimization of cycle time and a narrow retention-time exclusion window (±0.1 min) ensured sampling of narrow peaks and isomeric species, with over 40 % of annotated lipids represented by distinct isomers.

Benefits and Practical Applications

• Significantly deeper and more comprehensive lipidome coverage in complex biological samples.
• Automated generation of accurate, retention-time annotated libraries for both targeted and untargeted lipidomics workflows.
• Improved detection of low-level, poorly ionized, and isomeric lipid species relevant to biomarker discovery and industrial QA/QC.

Future Trends and Potential Applications

Real-time adaptive exclusion algorithms and machine learning–driven precursor prioritization could further enhance iterative acquisition efficiency.
Extending iterative MS/MS to other omics platforms, incorporating ion mobility separation, and advanced fragmentation methods will improve structural elucidation of complex isomers.
Integration with big-data analytics and cloud-based workflows promises scalable, high-throughput lipidomics for systems biology.

Conclusion

Iterative MS/MS on the Agilent 6546 LC/Q-TOF, leveraged by Agilent Lipid Annotator software, markedly extends plasma lipidome coverage beyond conventional DDA approaches.
Sequentially excluding previously fragmented ions enables robust detection of low-abundance and isomeric lipids, supporting comprehensive, high-confidence lipid profiling.

References

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