Lipid-class specific internal standard normalization of HILIC-MS/MS data embedded into untargeted data processing and interactive exploration

Importance of the Topic

This summary addresses a class-specific normalization workflow for hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry (HILIC-MS/MS) data in lipidomics. Accurate normalization is essential to correct for sample preparation variability, instrument fluctuations, and class-dependent ionization efficiencies, improving the reliability of lipid profiling in biological and clinical research.

Study Objectives and Overview

The main goal was to develop and integrate a software pipeline that uses stable isotope labelled internal standards (SIL-IS) representative for each lipid class to normalize untargeted HILIC-MS/MS data. The workflow was embedded in Bruker’s MetaboScape® 2023b and T-ReX® 4D environment to automate internal standard detection, lipid annotation, and intensity normalization.

Methodology and Instrumentation

Chromatographic separation employed an iHILIC Fusion(+) column with a binary gradient over 18 minutes, using ammonium formate buffer (pH 3.5) and acetonitrile.

• Mobile phase A: 35 mM ammonium formate/5 % acetonitrile (pH 3.5)
• Mobile phase B: acetonitrile
• Gradient: 97 % B at start, linear to 60 % B at 8.5 min, return to 97 % B by 12 min

Data were processed in MetaboScape® 2023b with the T-ReX® 4D workflow. Preprocessing detected SIL-IS targets in a targeted mode prior to untargeted feature finding. Rule-based annotation classified lipids into LIPID MAPS hierarchy levels and assigned each to its appropriate internal standard.

Main Results and Discussion

Separation achieved coelution of endogenous lipids with their class-specific deuterated standards across major phospholipid, glycerolipid, sphingolipid, and sterol classes. Targeted detection of internal standards enabled intensity normalization for 72 annotated lipid species, reducing mean relative standard deviation from 14.3 % to 8.7 %. Visualization of m/z versus retention time confirmed consistent chromatographic behavior for standards and endogenous lipids.

Benefits and Practical Applications of the Method

• Enhanced precision and accuracy in quantitative lipidomics studies
• Robust correction for class-dependent matrix effects and ionization biases
• Seamless integration into untargeted workflows for high-throughput laboratories
• Applicability across diverse lipid classes relevant to biomarker discovery, QA/QC, and mechanistic research

Future Trends and Potential Applications

Further developments may include expanded internal standard libraries covering additional lipid subclasses and automated correction for in-source fragmentation. Integration with multi-omics platforms could enable cross-platform normalization. Machine learning models may refine class assignments and normalization factors, improving throughput and reliability.

Conclusion

The presented software workflow leverages class-specific SIL-IS to improve normalization of HILIC-MS/MS lipidomics data, significantly reducing analytical variability. Its integration into untargeted processing pipelines facilitates accurate lipid quantification for research and industrial applications.

Used Instrumentation

• LC: iHILIC Fusion(+) column (Bruker)
• MS: Bruker timsTOF or equivalent high-resolution tandem mass spectrometer
• Software: MetaboScape® 2023b, T-ReX® 4D workflow