Excel in productivity with the TSQ Altis mass spectrometer

Importance of the Topic

Lipids represent a diverse class of biomolecules critical for cell structure, energy storage and signaling. Their broad chemical variations make them valuable clinical biomarkers in studies of human physiology and disease. Routine measures of cholesterol and triglycerides illustrate the translational potential of lipid profiling in cardiovascular research. However, comprehensive lipidomic analysis faces challenges in throughput, coverage and quantitation reliability when surveying thousands of lipid species across complex biological matrices.

Study Objectives and Overview

This work presents a targeted workflow for large-scale lipid profiling combining high-performance liquid chromatography and triple quadrupole mass spectrometry. The primary objectives were to establish a screening pipeline covering nearly 2 000 lipid species, optimize quantitative analysis with selected reaction monitoring, and ensure robust method transfer between laboratories and instrument generations. Application examples use human plasma reference materials to demonstrate analytical performance and comparability to community consensus intervals.

Methodology and Applied Instrumentation

Samples were separated by hydrophilic interaction liquid chromatography to group lipid classes by headgroup chemistry, yielding narrow peaks of 2–6 seconds full-width. Targeted mass spectrometry employed scheduled selected-reaction-monitoring (SRM) on Thermo Scientific TSQ Altis and TSQ Altis Plus triple quadrupole instruments. Dwell times below one millisecond and mass filters of 0.2–0.7 Da enabled acquisition of over 20 000 SRMs per method with sufficient data points per peak. The Vanquish Horizon UHPLC, with single and dual channel configurations, provided reproducible gradients and injection cycles. Avanti UltimateSPLASH ONE deuterated internal standards covering 19 lipid classes supported normalization and single-point quantitation. Data processing and quantitation utilized TraceFinder software with a prototype screening tool to refine the final SRM list.

Main Results and Discussion

Initial screening targeted 1 900 lipid species across positive and negative electrospray modes. Scheduled SRM windows and rapid dwell times yielded six to fourteen data points per 3.5-minute gradient peak, even with hundreds of concurrent transitions. Method transfer to a second laboratory on TSQ Altis Plus showed consistent retention times and quantitation. Screening results were refined to 498 species in positive mode and 648 in negative mode. Quantitative linearity was demonstrated over six concentration levels (10–100 nmol/µL) with r2 values of 0.998 for representative lipids. Comparison against NIST SRM 1950 reference plasma confirmed agreement with consensus concentration intervals.

Benefits and Practical Applications

• Extensive lipid coverage across multiple classes with high specificity and sensitivity.
• Reliable quantitation through internal standard normalization and single-point calibration.
• High throughput using fast chromatography and millisecond SRM dwell times.
• Seamless method transfer between instruments and laboratories without reoptimization.
• Ability to support clinical research cohorts with standardized workflows and QA/QC benchmarks.

Future Trends and Opportunities

Advancements in multi-channel UHPLC promise to double effective gradient time and reduce cycle durations, further increasing throughput. Community-driven SRM databases and automated software tools for screening data processing will accelerate method development and reproducibility. Expanded panels of labeled standards and refined QA/QC strategies will enhance confidence in lipid biomarker discovery and routine clinical applications.

Conclusion

The presented LC-MS/MS workflow leverages state-of-the-art chromatography, fast scheduled SRM acquisition and comprehensive internal standards to enable robust, high-throughput lipid profiling. Validated against reference materials and transferable across instruments, this approach addresses key challenges in large-scale clinical lipidomics and supports reliable biomarker analysis.

References

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