Long-Term Robustness of Agilent LC/Q-TOF Systems for Untargeted Lipidomics

Significance of the Topic

Large-scale untargeted lipidomics in clinical cohorts demands highly reproducible and accurate mass spectrometric workflows to detect subtle biological variations over extended periods. Robust LC/Q-TOF instrumentation reduces technical variability, thereby enhancing statistical power and confidence in biomarker discovery related to diseases such as type 1 diabetes.

Objectives and Study Overview

This study benchmarks the long-term precision and mass accuracy of Agilent LC/Q-TOF systems during untargeted lipidomic analyses of over 14,000 human plasma samples collected in the TEDDY cohort. Spanning 26 months and 241 measurement days, it evaluates instrument performance under real-world throughput conditions for both positive and negative electrospray ionization modes.

Methods and Instrumentation

• Chromatography: C18 reversed-phase column (100×2.1 mm, 1.7 μm) at 65 °C; 15-minute gradient; flow rate 0.6 mL/min.
• Mass Spectrometry: Agilent 6550 iFunnel Q-TOF (negative ESI, 20,000 RP) and 6530 Q-TOF (positive ESI, 10,000 RP) with Jet Stream source; MS1 and data-dependent MS2 at 2 spectra/s; mass range m/z 60–1700.
• Quality Control: Method blanks, pooled plasma QCs, and NIST SRM 1950 external controls; injection sequence bracketing; scheduled maintenance.
• Data Processing: Feature extraction in MassHunter, alignment in Mass Profiler Professional, lipid annotation via LipidBlast, quantification in MassHunter Quantitative Analysis; SERRF normalization applied to correct signal drifts.

Key Results and Discussion

• Precision: SERRF normalization yielded median RSDs of <2% for pool QCs and 8.3% for NIST QCs, outperforming LOESS (12–45% RSD).
• Mass Accuracy: Sustained average mass errors <1 mDa (95% CI <2 mDa) across six internal standards over 26 months, ensuring reliable elemental formula assignment.
• Data Stability: Representative lipids (e.g., PC 36:4, SM d36:0) showed <2.2% variance in QC samples versus much larger biological variation in TEDDY samples.

Benefits and Practical Applications

High reproducibility and mass accuracy enable confident long-term monitoring of lipid profiles in large cohorts. Reduced technical variance increases statistical power and minimizes the number of replicates, facilitating efficient biomarker discovery and supporting studies on disease etiology such as type 1 diabetes.

Future Trends and Potential Applications

• Integration of advanced machine-learning normalization techniques across omics platforms.
• Expansion to other clinical matrices and multi-center studies requiring extended instrument robustness.
• Coupling with ion mobility and high-throughput data analytics for deeper lipidome coverage.
• Development of standardized QC protocols and community-based lipid libraries.

Conclusion

Agilent LC/Q-TOF systems demonstrate exceptional long-term robustness for untargeted lipidomics in large plasma cohorts, maintaining high precision and mass accuracy over 26 months. The approach supports reliable quantitative evaluation of lipophilic biomarkers in clinical research settings.

References

• Huynh et al. Agilent application note 5994-3747EN (2021).
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