Tips and tricks for LC–MS-based metabolomics and lipidomics analysis

Importance of the topic

Metabolomics and lipidomics provide detailed insights into low-molecular-weight and lipid molecules in biological systems, enabling biomarker discovery, clinical study design, and understanding of physiology and disease. Liquid chromatography–mass spectrometry (LC–MS) remains the most versatile and widely applied platform for both polar and nonpolar compound profiling, offering high sensitivity, broad coverage, and molecular specificity.

Study objectives and overview

The article aims to guide researchers—especially newcomers—through designing and optimizing LC–MS workflows for metabolomics and lipidomics. It presents step-by-step recommendations covering statistical planning, sample collection/preparation, chromatographic separation, mass spectrometric detection, data processing, quality control, statistical evaluation, and data sharing, highlighting common pitfalls and practical “tips and tricks.”

Methodology and instrumentation

Power analysis and sample size:

• Perform power calculations before experiments to balance statistical power and resource constraints, using tools such as G*Power, DSD, MetSizeR or SSizer.
• For untargeted studies, pilot data or general guidelines (e.g., 5–10 replicates for fluids/tissues, 20–30 cases per human cohort) inform sample numbers.

Sample collection and extraction:

• Quench metabolism immediately using liquid nitrogen or dry ice and store at −80 °C.
• Choose appropriate anticoagulants for plasma; balance between serum and plasma considering clotting variability.
• Apply single- or biphasic extractions (e.g., methanol, MTBE/methanol/water) tailored to target analytes, ensuring robust protein removal.
• Optimize sample amounts (e.g., 10–30 µL biofluids, 5–20 mg tissue) and consumables (glass vs. plastic), monitoring background contaminants.

Chromatographic separation:

• Use reversed-phase LC (C18, C8, C30 columns) for mid-to nonpolar compounds and HILIC (amide, silica, zwitterionic phases) for polar metabolites.
• Adopt UHPLC with sub-2 µm particles in microbore columns for improved speed and resolution; high-throughput (< 10 min) methods support large-scale studies with trade-offs in resolution.

Mass spectrometry and detection:

• Employ electrospray ionization (ESI) in positive/negative mode, controlling adduct formation by mobile phase modifiers (formate, acetate, ammonium salts).
• Choose high-resolution analyzers (Orbitrap, TOF, QTOF) for untargeted profiling and low-resolution triple quadrupole for targeted MRM quantitation.
• Balance isolation window width (narrow for selectivity, wide for sensitivity) in tandem MS; use DDA with exclusion lists or DIA and deconvolution tools to maximize MS/MS coverage.

Instrumental Setup

Representative instrumentation includes UHPLC systems coupled to Q Exactive or Orbitrap Exploris mass spectrometers. Typical mobile phases consist of water/acetonitrile or isopropanol/acetonitrile mixtures with volatile buffers (ammonium formate/acetate) and formic or acetic acid modifiers.

Key results and discussion

Optimizing mobile-phase composition and column selection markedly improves retention, peak shape, and isomer separation. Examples include superior HILIC performance for very polar metabolites compared to RPLC and tailored RPLC conditions for lipid subclasses. Instrument drift, ion suppression, adducts, and background contaminants (e.g., silicone from vial caps) can be identified and mitigated through solvent/vendor screening, column maintenance, and quality control injections.

Benefits and practical applications

The comprehensive guide supports diverse research areas—clinical biomarker studies, nutritional phenotyping, fluxomics—by providing:

• Standardized protocols for reliable data acquisition.
• Strategies to reduce false positives (artifact and adduct annotation, in-source fragments).
• Data-processing workflows using tools such as MS-DIAL, XCMS, MZmine for feature detection, alignment, and annotation.
• Quality control measures with pooled/QC samples, blanks, and dilution series for reproducibility.
• Statistical analysis guidance within MetaboAnalyst for univariate/multivariate testing and pathway mapping.

Future trends and opportunities

Emerging developments promise further integration of untargeted and targeted workflows through faster high-resolution MS acquisitions and expanded spectral libraries. Advanced informatics—including AI/ML–driven annotation, retention time and CCS prediction, in-silico fragmentation, and network analyses—will automate data processing and improve identification rates. Community-driven standardization and atlas projects will enhance data sharing and comparability.

Conclusion

The multilayered guidance offers a structured roadmap to design robust LC–MS–based metabolomics and lipidomics experiments. By applying rigorous quality control, informed method choices, and advanced data-analysis strategies, researchers can generate reproducible, high-coverage molecular profiles to drive discoveries in biomedical and life-science research.

References

Hoang C. et al. Tandem MS across platforms, Anal. Chem. 2024.
Tsugawa H. et al. Lipidome atlas in MS-DIAL 4, Nat. Biotechnol. 2020.
Dunn W. et al. Procedures for large-scale profiling, Nat. Protoc. 2011.
Liebisch G. et al. LIPID MAPS classification update, J. Lipid Res. 2020.
Sumner L.W. et al. MSI reporting standards, Metabolomics 2007.