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Increased Throughput and Confidence for Lipidomics Profiling Using Comprehensive HCD MS2 and CID MS2/MS3 on a Tribrid Orbitrap Mass Spectrometer

Applications | 2016 | Thermo Fisher ScientificInstrumentation
LC/HRMS, LC/MS, LC/MS/MS, LC/Orbitrap
Industries
Lipidomics
Manufacturer
Thermo Fisher Scientific

Summary

Importance of the Topic


Comprehensive profiling of lipid species is essential for understanding cellular physiology and disease associations. High-resolution accurate-mass lipidomics enables sensitive detection of phospholipids, triglycerides, and isomeric species, supporting research in metabolic disorders, biomarker discovery, and nutritional analysis.

Objectives and Study Overview


This study presents a unified LC/MSn workflow on the Orbitrap Fusion Lumos tribrid mass spectrometer. It combines alternating positive/negative data-dependent HCD MS2 acquisition with targeted CID MS2 and MS3 to achieve confident, high-throughput characterization and quantitation of complex lipidomes in a single run.

Methodology and Instrumentation


  • Instrument: Thermo Scientific Orbitrap Fusion Lumos with Advanced Quadrupole Technology, dual-pressure linear ion trap, and high-field Orbitrap analyzer.
  • Ionization: HESI source, positive/negative mode switching, spray voltage 3.5 kV, sheath gas 40, aux gas 3, capillary 320 °C, heater 350 °C.
  • LC conditions: Thermo RSLC 3000, Accucore C18 column (2.1×150 mm, 2.6 µm), 45 °C, 260 µL/min flow; mobile phases A: ACN/H₂O 60:40, B: IPA/ACN 90:10 with 10 mM ammonium formate and 0.1% formic acid; 30 min gradient.
  • Acquisition: Top-speed dd-HCD MS2 with 1.0 s cycle (positive/negative), targeted CID MS2 on PC upon m/z 184.0733 detection, and targeted CID MS3 on neutral FA+NH₃ loss for TG isomers (12:0–26:0 list).
  • Data processing: LipidSearch 4.1 SP1, precursor tolerance 3 ppm, product tolerance 5 ppm, retention time alignment ±0.1 min, grade filtering for high-confidence IDs.

Main Results and Discussion


  • 1 s HCD MS2 cycle time on the Orbitrap Fusion Lumos showed no loss in lipid identifications versus longer cycles and enabled polarity switching within a single 30 min run.
  • Combining positive HCD/CID and negative HCD MS2 identified 208 PC molecular species (Grades A+B) versus 158 in positive-only dd-HCD MS2 runs.
  • Triggered CID MS3 on neutral fatty acid plus ammonia loss resolved co-eluting TG isomers, allowing molecular composition assignment of TG 46:2 variants.
  • Application to bovine heart extract demonstrated reliable relative quantitation across six orders of magnitude dynamic range.
  • Analysis of three food plate samples (USA, CA, Davis diets) identified over 700 lipid species with high reproducibility, reflecting distinct dietary lipidomes.

Benefits and Practical Applications


The integrated LC/MSn strategy doubles throughput by combining both ion modes in a single analysis and enhances structural confidence for PC and TG profiling. It supports quantitative lipidomics in biomedical research, quality control, and nutritional metabolomics.

Future Trends and Applications


Emerging directions include real-time adaptive acquisition strategies, deeper MSn workflows for global lipid network mapping, expansion to low-abundance lipid classes, and integration with machine learning for automated annotation in clinical, environmental, and food lipidomics.

Conclusion


The novel Orbitrap Fusion Lumos workflow achieves comprehensive, high-throughput lipid profiling with enhanced confidence via parallel HCD MS2 and targeted CID MSn. It streamlines single-run analysis of diverse lipid classes, offering a robust platform for advanced lipidomics applications.

References


  • Kiyonami R, Peake DA, Yokoi Y, Miller K. Increased identification coverage and throughput for complex lipidomes. Thermo Fisher Scientific Application Note #607.
  • Matyash V, Liebisch G, Kurzchalia TV, Shevchenko A, Schwudke D. Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. Journal of Lipid Research. 2008;49(5):1137–1146.
  • Peake DA, et al. Processing of a complex lipid dataset for the NIST inter-laboratory comparison exercise for lipidomics measurements in human serum and plasma. ASMS Poster 2015.
  • Michalski A, et al. Mass spectrometry based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer. MCP. 2011. doi:10.1074/mcp.M111.011015.
  • Liebisch G, et al. Shorthand notation for lipid structures derived from mass spectrometry. Journal of Lipid Research. 2013;54(6):1523–1530.
  • Vaniya A, Fiehn O. What are we eating? Comprehensive analysis of food metabolites and natural products using eight metabolomics platforms. Metabolomics Society Poster 2015.

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