Comprehensive workflow for targeted cell metabolomics using automated sample preparation, HILIC chromatography, LC/TQ, and a statistical analysis software suite

Significance of the topic

The development of robust and sensitive targeted metabolomics workflows is critical for deciphering cellular metabolism in health and disease. High-throughput approaches with reliable sample preparation and chromatography enable researchers to generate reproducible data across large studies, enhancing insights into metabolic pathways and biomarker discovery.

Study Objectives and Overview

This work presents a turnkey solution for targeted cell metabolomics that integrates automated sample preparation, hydrophilic interaction chromatography, triple quadrupole mass spectrometry, and streamlined statistical analysis. The aim is to create a transferable protocol suitable for diverse laboratory settings and capable of quantifying hundreds of polar metabolites with high sensitivity.

Methodology

A comprehensive workflow was designed consisting of the following steps:

• Cell lysis and quenching of one million K562 cells per sample.
• Automated solid-phase extraction using a Bravo platform to isolate polar metabolites and remove lipids and proteins.
• Sample drying and reconstitution in 80% acetonitrile, with post-spiking of common intracellular standards to generate distinct concentration groups.
• HILIC chromatography on a Poroshell 120 HILIC-Z column for retention of polar analytes in both ionization modes.
• Targeted detection using a 6495C triple quadrupole MS in dynamic MRM mode, leveraging a custom database of over 500 metabolites.

Instrumentation

• Bravo Sample Prep Automation Platform
• Agilent 1290 Infinity II Bio LC with MP35N coating
• Poroshell 120 HILIC-Z analytical column
• Agilent 6495C Triple Quadrupole LC/TQ with ion funnel

Main Results and Discussion

The optimized method achieved:

• Detection of 274 cell-relevant metabolites with retention time repeatability (RSD<5%) over 11 days.
• Limits of quantitation in the femtomole range for isotopically labeled standards, with linear calibration from 0.5 to 10,000 ng/mL.
• High analytical sensitivity and throughput enabled by dwell times below 5 ms in dynamic MRM.
• Minimal variability (RSD<20% for most features) in pooled quality controls, supporting consistent performance.
• Data analysis in MassHunter Quant and Mass Profiler Professional provided rapid compound integration, normalization, statistical testing (ANOVA), and visualization (PCA, heat maps).

Benefits and Practical Applications

This workflow delivers:

• Automated, reproducible sample prep to minimize manual handling errors.
• Comprehensive metabolite coverage for glycolysis, TCA cycle, amino acid, nucleotide, and lipid-related pathways.
• Transferable protocols for chromatography that ensure interlaboratory consistency.
• Scalable data analysis enabling both qualitative profiling and absolute or semi-quantitative measurements.

Future Trends and Potential Applications

Emerging opportunities include:

• Integration of real-time data acquisition and adaptive MRM scheduling to further increase throughput.
• Expansion of the target database to include novel metabolites and pathway-specific markers.
• Coupling with advanced bioinformatics for multi-omics integration and systems biology studies.
• Application in pharmaceutical screening, clinical biomarker validation, and metabolic flux analysis.

Conclusion

The presented workflow offers a fully automated, sensitive, and reproducible solution for targeted cell metabolomics. By combining standardized sample preparation, robust HILIC separation, dynamic MRM acquisition, and powerful statistical tools, researchers can efficiently profile hundreds of metabolites and derive meaningful biological insights.

References

1. Van de Bittner G C et al. A comprehensive workflow for routine automated metabolite and lipid analysis of mammalian cells. Metabolomics Conference Poster, 2020, #74.
2. Yannell K E et al. Improvements to HILIC robustness – a targeted HILIC metabolomics method for routine analysis. ASMS, 2021.