APPLICATION NOTEBOOK - UNTARGETED METABOLOMICS AND LIPIDOMICS

Significance of the Topic

Global research in systems biology increasingly relies on comprehensive profiling of small molecules, lipids, and proteins to understand phenotypic changes under diverse conditions. Untargeted metabolomics and lipidomics enable hypothesis‐generating discovery of biomarkers and pathway perturbations in health, disease, and environmental studies. Advances in chromatographic separations (HILIC, RP-UPLC, UPC², 2D-LC) coupled with high-resolution mass spectrometry (QTof, ion mobility) and powerful informatics are vital to resolve thousands of molecular species, maximize specificity, and accelerate identification and quantification in complex biological and food matrices.

Objectives and Overview of the Studies

• Develop and integrate advanced chromatographic strategies (HILIC-UPLC, RP-UPLC with Charged Surface Hybrid C18, UPC², orthogonal 2D-LC, atmospheric pressure GC) coupled to high-resolution exact mass QTof and ion mobility spectrometers for untargeted metabolomic and lipidomic profiling.
• Create robust workflows for high-throughput screening of polar metabolites, complex lipids, and small molecules in diverse samples (cell extracts, plasma, plant tissues, food products).
• Demonstrate multi-omics data-independent acquisition (MS⁽ᴱ⁾/HDMS⁽ᴱ⁾) to yield comprehensive precursor and fragment ion information in a single run, supporting both qualitative and quantitative analysis.
• Leverage informatics platforms (Progenesis QI, TransOmics, ProteinLynx GlobalSERVER, MassLynx) for data alignment, peak picking, statistical analysis (PCA, OPLS-DA, correlation), and high-confidence identification using orthogonal parameters (accurate mass, retention time, drift time, MS/MS spectra).
• Apply these approaches to real-world applications: human platelet and hepatocyte metabolomics, pediatric urine multi-omics, broccoli sprout metabolomics, rice authenticity screening, food fraud detection, and drug metabolizing cell models.

Methodology and Instrumentation

• Chromatographic separations: HILIC-UPLC (ACQUITY BEH Amide), RP-UPLC CSH C18, UPC² BEH, 2D-LC combining HILIC and C18, atmospheric pressure GC with SPME headspace sampling.
• Mass spectrometry: High-resolution QTof (SYNAPT G2, G2-S, Xevo G2-S, QTof-MS) operated in data-independent acquisition (MS⁽ᴱ⁾/HDMS⁽ᴱ⁾) for concurrent low-energy precursor and high-energy fragment ion collection, enhanced by ion mobility (T-Wave IM) for size/shape separation.
• Multi-omics integration: nanoACQUITY UPLC for proteomics, with label-free quantification and DIA MS⁽ᴱ⁾ workflows in TransOmics for peptides and proteins; APGC-TOF MS⁽ᴱ⁾ for non-polar metabolite molecular ions in plant metabolomics.
• Informatics: Progenesis QI for metabolite and lipid data processing, alignment, deconvolution, statistical filtering, and database searching; TransOmics for multi-omics comparison and multivariate analysis; ProteinLynx GlobalSERVER for proteomics identification; EVO as complementary statistical package.

Main Results and Discussion

• Polar Metabolite Profiling: HILIC-UPLC/QTof-MS resolved sugars, amino acids, nucleotides, and organic acids in platelet extracts under acidic and basic conditions with retention time RSD < 0.14% and linearity R² >0.99 for 80+ metabolites.
• Lipid Separation: CSH C18 UPLC improved class separation (PC vs. SM) and cis/trans isomer resolution for 67 standard lipid mixtures; lipid extracts from plasma and liver showed enhanced peak capacity and dynamic range.
• 2D-LC Strategy: Orthogonal 2D-UPLC coupling HILIC with CSH C18 maximized peak capacity and specificity in phospholipid separation, enabling class and intra-class quantification in urine samples.
• Ion Mobility Multi-Omics: HDMS⁽ᴱ⁾ provided four-dimensional maps (RT, m/z, drift time, intensity) in multi-omics workflows for urine and broccoli sprouts, facilitating isobaric separation and structural characterization of metabolites, lipids, and proteins.
• UPC² Metabolite Class Profiling: UPC²-C18/MS rapidly separated neutral to amphipathic lipids in a single method, demonstrating inter- and intra-class resolution in biological and plant extracts.
• Metabolite Identification: Progenesis QI’s MetaScope integrated accurate mass, RT, drift time, and fragmentation matching against in-house and public libraries to reduce false positives and enhance identification confidence in wine and broccoli sprout studies.
• Multi-Omics in Drug Metabolism: THLE hepatocytes transfected with CYP2E1 profiled by LC/HDMS⁽ᴱ⁾ multi-omics revealed altered proteins (EIF2 signaling) and metabolites (thiamines, glutamate) linked to stress pathways.
• Broccoli Sprout Metabolomics: UPLC/ion mobility LC/HDMS⁽ᴱ⁾ distinguished dark, light, and sucrose-treated sprouts, identifying chlorophyll derivatives and polar metabolites that reflect growth conditions.
• GC-MS Authenticity Screening: APGC-TOF MS⁽ᴱ⁾ headspace profiling of basmati, jasmine, and long grain rice, analyzed by Progenesis QI multivariate statistics (PCA, OPLS-DA, correlation) revealed markers for potential food fraud investigations.

Benefits and Practical Applications of the Methods

• Comprehensive coverage: Unbiased, four-dimensional data capture in a single run reduces analysis time and sample consumption.
• Increased specificity and sensitivity: Ion mobility separation and HDMS E reduce spectral complexity, improve quantification, and resolve isobaric species.
• High throughput: UPLC and UPC² separations enable rapid profiling across large sample sets, supporting drug discovery, nutrition, and QC workflows.
• Integrated informatics: Progenesis QI and TransOmics provide scalable data alignment, statistical analysis, and compound identification with minimal manual effort.
• Multi-omics integration: Simultaneous profiling of metabolites, lipids, and proteins yields deeper biological insights into disease, toxicology, and environmental responses.
• Food authenticity and fraud detection: APGC-TOF MS⁽ᴱ⁾ fingerprinting and multivariate analysis enable screening of rice varieties and other foods for authenticity and quality control.
• Future-proof: Configurable methods (HILIC, CSH, UPC², 2D-LC, APGC) adapt to diverse analyte classes, ensuring broad applicability in analytical laboratories.

Future Trends and Potential Applications

• Deep learning and AI: Integration of machine learning for automated marker selection, identification, and predictive modeling across multi-omics datasets.
• High-density 2D and 3D separations: Coupling ion mobility with advanced chromatography for ultra-high peak capacity profiling of cellular and environmental extracts.
• Real-time metabolomics and lipidomics: Ambient and miniature ion mobility-MS platforms for rapid biochemical monitoring and diagnostics.
• Expanded foodomics: Comprehensive profiling for quality, authenticity, and safety control, including allergen detection and microbiome interactions.
• Translational pipelines: Clinical and regulatory adoption of HDMS E multi-omics assays for personalized medicine, biomarker validation, and therapeutic monitoring.

Conclusion

Combining orthogonal chromatographic separations, soft ionization, ion mobility, and data-independent high-definition MS provides unprecedented resolution and confidence in metabolomics and lipidomics analyses. HDMS E workflows, supported by Progenesis QI and TransOmics informatics, enable simultaneous high-throughput, qualitative, and quantitative profiling across metabolites, lipids, and proteins in a single experiment. These integrated platforms are transforming biomarker discovery, food authenticity testing, and multi-omics research for diverse applications in health, agriculture, and industry.

References

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• Fu W. et al. “UPLC-UV MSE analysis for carotenoid and chlorophyll species in algae.” Anal Bioanal Chem. (2012) 404:3145–3154.