LC-MS and GC-MS metabolite data processing using Mass Profiler Professional, a chemometric data analysis and visualization tool to determine metabolomic pathways

Importance of the Topic

Metabolomics provides a comprehensive snapshot of small-molecule dynamics in cells, complementing proteomic and genomic data. Combining liquid chromatography–mass spectrometry (LC-MS) and gas chromatography–mass spectrometry (GC-MS) extends metabolite coverage, enabling deeper insights into drug effects. This multi-platform approach is critical for deciphering complex biological responses and identifying novel biomarkers.

Study Objectives and Overview

The primary aim was to assess the impact of the immunosuppressant rapamycin on HEK 293 cell metabolism. By integrating metabolomic profiles with parallel proteomic and genomic analyses, the study sought to pinpoint pathways altered by rapamycin treatment and to build a holistic view of its cellular mechanisms.

Methodology and Instrumentation

The metabolomics workflow comprised untargeted data acquisition in both positive and negative electrospray ionization modes and complementary GC-MS analysis. Key steps included:

• Metabolite extraction from one million cells using a methanol–water–chloroform mixture and phase separation.
• LC-MS analysis on a high-resolution QTOF system with a sub-2 µm reversed-phase column and a 13 min gradient.
• Two-step chemical derivatization for GC-MS, targeting carbonyl and hydroxyl groups, followed by separation on a DB-5MS column.
• Data processing with Mass Profiler Professional (MPP) for feature detection, alignment, normalization (using internal standards), baseline correction, and multivariate statistics.

Instrumentation

• Agilent 1290 Infinity LC coupled to Agilent 6520 ESI-QTOF
• Agilent 7890 GC with 5975C MSD detector
• Agilent MassHunter Qualitative and GC Chemstation software
• AMDIS deconvolution tool
• Mass Profiler Professional for chemometric analysis

Key Results and Discussion

Over 700 features were detected in LC-MS positive mode and approximately 200 in GC-MS. Statistical filtering yielded a panel of differential metabolites, including significant up-regulation of oxidized glutathione, nicotinamide derivatives, and lactic acid, alongside down-regulation of glucose-6-phosphate and ribose. Principal component analysis clearly separated treated and control groups, confirming robust metabolic shifts. Pathway enrichment highlighted alterations in glycolysis/gluconeogenesis, purine metabolism, ABC transporters, and lipid metabolism. Integration with genomics and proteomics data refined the list of candidate pathways, revealing coherent multi-omic signatures of rapamycin action.

Benefits and Practical Applications

This multi-platform metabolomics strategy delivers extensive metabolite coverage and reliable quantification, supporting biomarker discovery and mechanistic studies. The streamlined MPP workflow accelerates data interpretation, while cross-validation with other omics layers enhances confidence in biological inferences. Such an approach is valuable for drug evaluation, nutritional research, and quality control in industrial and clinical laboratories.

Future Trends and Potential Applications

Advances in high-resolution instrumentation, automated data processing with artificial intelligence, and expanded spectral libraries will further improve metabolite identification. Real-time metabolomics, integrated single-cell analyses, and predictive modeling are emerging trends. Enhanced multi-omic integration promises more accurate pathway reconstructions and personalized therapeutic insights.

Conclusion

The combined LC-MS and GC-MS workflow, supported by robust chemometric analysis in MPP, effectively captured rapamycin-induced metabolic changes in HEK cells. Integrating metabolomic data with genomic and proteomic profiles provided a comprehensive view of affected pathways, underscoring the power of multi-omics in systems biology.

Reference

• S. Baumann et al. Non-targeted GC/MS metabolomics using a large-volume inlet, mid-column backflushing, and a retention time locked spectral library, ASMS poster, 2010.
• T. R. Sana et al. Metabolomic and transcriptomic analysis of the rice response to Xanthomonas oryzae, Metabolomics, 6:451–465, 2010.
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• T. Peng, T. R. Golub, D. M. Sabatini. The Immunosuppressant Rapamycin Mimics a Starvation-Like Signal Distinct from Amino Acid and Glucose Deprivation, Mol. Cell. Biol., 22(15):5575–5584, 2002.