COMPREHENSIVE DISCOVERY LIPIDOMIC WORKFLOW WHICH UTILIZES A NOVEL, MULTI-REFLECTING TOF WITH INTEGRATED INFORMATICS, PROVIDING HIGHLY CONFIDENT LIPID CHARACTERIZATION AND QUANTIFICATION

Significance of the Topic

Lipidomics remains analytically challenging due to lipid diversity and complexity. Lipids are essential in energy storage, membrane structure, and signaling pathways implicated in cancer progression. Colorectal cancer is among the leading causes of cancer mortality worldwide, motivating refined lipid profiling methods to uncover disease biomarkers.

Objectives and Study Overview

This work presents a comprehensive lipidomic workflow integrating the novel Waters Xevo MRT multi-reflecting QTof mass spectrometer with Lipostar2 informatics. The study compares plasma lipid profiles from colorectal cancer patients and healthy controls, aiming to demonstrate high-confidence lipid identification and quantification in data-independent acquisition mode.

Methodology and Instrumentation

A high-throughput reversed-phase UPLC method was employed (12 min gradient) using an ACQUITY Premier UPLC I-Class with CSH C18 column. The Xevo MRT mass spectrometer features a gridless 4 m flight path, resolution up to 100 000 FWHM, long-life detector, and ESI source. Data were acquired at 10 Hz in DIA mode via waters_connect, then transferred as mzML to Lipostar2. Plasma samples (6 healthy, 6 CRC) underwent protein precipitation with IPA in the presence of EquiSPLASH internal standards. Triplicate injections were randomized and interspersed with QC samples.

Main Results and Discussion

Supervised PLS-DA models separated controls from CRC samples and distinguished colon versus rectum subtypes after QC removal. QC replicates clustered tightly, indicating strong reproducibility. Multivariate analysis and ANOVA identified Cer(36:1) adducts as significantly altered. Lipostar2 provided rule-based lipid identification with confidence scoring; an example Cer(42:1) showed low mass error and robust fragmentation. Box plots revealed upregulated ceramides and dysregulated lysophosphatidylcholines in CRC. Pathway mapping using related cancer pathways illustrated LPC decreases and ceramide increases consistent with literature reports.

Benefits and Practical Applications of the Method

The combined UHPLC–MS workflow delivers rapid lipidomic profiling with high mass accuracy in a compact footprint. Integrated Lipostar2 software streamlines data processing, identification, and statistical analysis, enabling robust biomarker discovery and biological interpretation in large cohort studies.

Future Trends and Potential Applications

Advances may include deeper structural lipid elucidation, broader integration of AI-driven data analysis, expansion to multi-omic workflows, real-time quality control, and application to diverse disease models. The compact high-resolution platform is poised for large-scale clinical lipidomics and automated diagnostic pipelines.

Conclusion

This study demonstrates that coupling multi-reflecting TOF MS with an intelligent informatics platform enables confident, high-throughput lipid characterization and quantification. The workflow shows reproducible separation of disease cohorts and supports biomarker discovery in colorectal cancer.

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