A Multidimensional Lipidomics Method: HILIC Coupled with Ion Mobility Enabled Time-of-Flight Mass Spectrometry

Importance of the Topic

Lipidomics aims to profile the full spectrum of lipid species in complex biological matrices, which is essential for understanding cellular functions, disease mechanisms, and discovering diagnostic biomarkers.
Conventional one-dimensional chromatographic methods often lack the resolving power to distinguish structurally similar or isobaric lipids.
Integrating hydrophilic interaction chromatography (HILIC) with ion mobility–enabled time‐of‐flight mass spectrometry (IM‐ToF MS) provides orthogonal separations, enhancing both specificity and sensitivity in lipid analysis.

Study Objectives and Overview

The primary objective of this study was to establish a multidimensional lipidomics workflow combining HILIC‐UPLC, ion mobility spectrometry, and ToF MS to achieve comprehensive characterization of lipids in animal tissue extracts.
The approach was demonstrated using bovine brain, heart, and liver lipids, with data processing facilitated by HDMS Compare and TransOmics Informatics for feature detection, alignment, and multivariate comparison.

Methodology

Aquity UPLC BEH HILIC (2.1 × 100 mm, 1.7 µm) was operated at 30 °C with mobile phases of 10 mM ammonium acetate in 95% ACN (A) and 50% ACN (B) at pH 8.0 under a gradient elution (0–13 min: 0.1–80% B; 13.01–16 min: re‐equilibration).
Flow rate was 0.5 mL/min, injection volume 5 µL. The SYNAPT G2‐S HDMS used a Z‐spray ESI source in positive and negative modes with capillary voltages of 2.8 kV and 1.9 kV, respectively.
Ion mobility separation was achieved with nitrogen gas in a T‐Wave cell (900 m/s velocity, 40 V amplitude).
Data were acquired in alternating low (6 V) and high (20–35 V) collision energy (HDMSE) to obtain precursor and fragment ion information in a single run.

Used Instrumentation

• Waters ACQUITY UPLC system with BEH HILIC column
• Waters SYNAPT G2‐S HDMS with Z‐spray ESI source
• Ion mobility cell with nitrogen T-Wave technology
• TransOmics Informatics and HDMS Compare Software for data processing

Main Results and Discussion

HILIC separation effectively resolved lipid classes by polar head groups, yielding stable retention windows for fatty acids, ceramides, phospholipids, and sphingolipids.
Ion mobility provided an additional separation based on collision cross section, enabling discrimination of isobaric species and revealing low‐abundance lipids within complex extracts.
HDMSE acquisition produced clean fragmentation spectra by aligning fragment and precursor ions by drift time, improving confidence in structural assignments.
Multidimensional molecular maps combining retention time, drift time, accurate mass, and intensity facilitated clear visualization of tissue‐specific lipidomes and identification of differential features via multivariate statistics.

Benefits and Practical Applications

The multidimensional method increases peak capacity and signal‐to‐noise ratio compared to conventional LC‐MS.
Combining chromatography, ion mobility, and HDMSE yields rich structural information without sacrificing throughput.
HDMS Compare and TransOmics enable high‐throughput comparative studies, supporting biomarker discovery and quality control workflows in metabolomics and pharmaceutical research.

Future Trends and Applications

Further integration with higher‐resolution MS and improved ion mobility devices will expand lipidome coverage.
Automation of sample preparation, data acquisition, and AI‐driven analysis will streamline workflows for clinical and regulatory environments.
Extension of multidimensional approaches to other omics layers (e.g., glycomics, proteomics) can drive systems‐level understanding of biological processes.

Conclusion

The combination of HILIC‐UPLC, ion mobility spectrometry, and time‐of‐flight MS provides a powerful platform for detailed lipidome characterization in complex biological matrices.
This orthogonal separation strategy, coupled with advanced informatics, enhances resolution, specificity, and quantitative confidence, unlocking new opportunities in lipidomics research.

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