An End-to-End Targeted Metabolomics Workflow

Significance of the Topic

The metabolome comprises all small molecules produced or modified by cells, reflecting an organism’s physiological state. Targeted metabolomics focuses on predefined metabolites of interest, offering high sensitivity, reproducibility, and quantitative accuracy. Such workflows are essential for applications ranging from biomarker validation to quality control in pharmaceutical and clinical research.

Study Objectives and Overview

This application note describes an end-to-end targeted metabolomics workflow optimized for plasma and mammalian cell samples. The aims are to demonstrate automated sample preparation, robust chromatographic separation, sensitive detection of up to 500 metabolites, and streamlined data analysis suitable for users at varied expertise levels.

Methodology

• Sample Preparation: Automated on the Agilent Bravo Metabolomics Sample Prep platform with Captiva EMR–Lipid plates to deplete proteins and lipids and isolate polar metabolites from 20 µL plasma or 1×10^6 cells.
• Chromatographic Separation: Polar analytes separated on an Agilent 1290 Infinity II Bio UHPLC system equipped with a Poroshell 120 HILIC-Z column using 20 mM ammonium acetate (pH 9.3) and acetonitrile in a nonlinear gradient over a 16 min window plus re-equilibration.
• Mass Spectrometric Detection: Agilent 6495C Triple Quadrupole LC/MS operating in dynamic multiple reaction monitoring (dMRM) with 1 ms dwell time, enabling reproducible quantification of 500 targeted metabolites in both positive and negative ion modes.

Instrumentation Used

• Agilent Bravo Metabolomics Sample Prep platform
• Agilent Captiva EMR–Lipid solid phase extraction plates
• Agilent 1290 Infinity II Bio LC system with MP35N alloy and Poroshell 120 HILIC-Z column (2.1×150 mm, 2.7 µm)
• Agilent 6495C Triple Quadrupole LC/MS with Agilent Jet Stream ion source and ion funnel
• Agilent MassHunter Acquisition, MassHunter Optimizer, MassHunter Quantitative Analysis 10, and Mass Profiler Professional software

Main Results and Discussion

• Identification of 266 metabolites in plasma and 274 in cell extracts, covering amino acids, coenzymes, TCA cycle intermediates, glycolysis metabolites, and more.
• Reproducible separation of polar and isomeric compounds (e.g., leucine vs. isoleucine) across column lots and users, with retention time deviations within the dMRM window over 400 injections.
• High analytical sensitivity demonstrated by detection limits as low as 20 amol for 15N5-ADP (10% RSD) and 1.2 amol for 13C-Phe (1% RSD) at 5 ms dwell times, with linear calibration (R2 ≥ 0.98).
• Rapid data processing using MassHunter Quantitative Analysis “Compounds-at-a-Glance” grid and statistical interpretation via Mass Profiler Professional, including PCA clustering and heat mapping of metabolite spike-in levels.

Benefits and Practical Applications

• Fully packaged, automated workflow reduces hands-on time, minimizes user error, and ensures high reproducibility for routine targeted analyses.
• Customizable database and dMRM method allow profiling of all metabolites or targeted subsets, as well as quantitative assays using stable isotope standards.
• High throughput capability and robust performance across laboratories make it suitable for biomarker validation, nutritional studies, disease research, and industrial QC.

Future Trends and Opportunities

• Integration with multi-omics platforms (proteomics, lipidomics) to deliver comprehensive biological insights.
• Enhanced automation and miniaturization for single-cell metabolomics and high-throughput clinical screening.
• Expansion of curated metabolite databases and machine-learning algorithms to improve annotation and pathway analysis.

Conclusion

The Agilent end-to-end targeted metabolomics workflow delivers efficient sample preparation, robust HILIC separation, and sensitive triple quadrupole detection of hundreds of metabolites. Its reproducibility, flexibility, and streamlined data analysis enable laboratories with varying expertise to obtain quantitative metabolite profiles for diverse applications.

References

• Zamboni N, Saghatelian A, Patti GJ. Defining the Metabolome: Size, Flux, and Regulation. Mol Cell. 2015;58(4):699–706.
• Roberts LD, Souza AL, Gerszten RE, Clish CB. Targeted Metabolomics. Curr Protoc Mol Biol. 2012; Chapter 30.
• Van de Bittner G, Sartain M, Chang D, Apffel A, Bernick K, Gomez M. An Automated Dual Metabolite + Lipid Sample Preparation Workflow for Mammalian Cell Samples. Agilent Technologies Technical Overview. 2022.
• Spivia WR, Raedschelders K, Gomez M, Van Eyk JE. Automated Metabolite Extraction for Plasma Using the Agilent Bravo Platform. Agilent Technologies Technical Overview. 2019.
• Sartain M, Gomez M, Van de Bittner G, Shu H. Enabling Automated, Low-Volume Plasma Metabolite Extraction with the Agilent Bravo Platform. Agilent Technologies Application Note. 2022.
• Stone P, Glauner T, Kuhlmann F, Schlabach T, Miller K. New Dynamic MRM Mode Improves Data Quality and Triple quad Quantification in Complex Analyses. Agilent Technologies Technical Overview. 2009.
• Banu Mohsin S, Batoon P. Absolute Quantitation of Fragile Metabolites by Isotope Dilution Mass Spectrometry on the Agilent 6495 Triple Quadrupole LC/MS. Agilent Technologies Application Note. 2022.
• Yannell KE, Cuthbertson D, Simmermaker C, Van de Bittner G, Parry E. Improvements to HILIC Robustness – a Targeted HILIC Metabolomics Method for Routine Analysis. Agilent Technologies Poster. 2021.