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

Significance of the Topic

Integrating metabolomics with proteomics and genomics enhances our understanding of complex biological processes and drug effects. Rapamycin, an immunosuppressant with emerging anti-cancer properties, alters cellular metabolism. Profiling small-molecule changes induced by rapamycin in HEK 293 cells provides insights into its mechanism and uncovers potential biomarkers for therapeutic monitoring.

Aims and Study Overview

This work aims to characterize metabolic alterations following rapamycin treatment in HEK 293 cells by combining untargeted LC-MS and GC-MS data. Key objectives include comprehensive metabolite detection across multiple platforms, statistical identification of differentially abundant compounds, and integration with parallel genomics and proteomics data to highlight affected biological pathways.

Methodology

• Cell Culture and Treatment: HEK 293 cells treated with rapamycin alongside untreated controls; five biological replicates pooled for analysis.
• Metabolite Extraction: Cells quenched and extracted with methanol–water–chloroform; aqueous and organic phases processed separately.
• LC-MS Analysis: Aqueous extract analyzed on Agilent 6520 QTOF coupled to a 1290 UPLC with SB-Aq column; gradient from 2% to 98% methanol over 13 min; ESI in positive and negative modes; epicatechin internal standard for normalization.
• GC-MS Analysis: Organic extract derivatized in two steps (methoxyamine and silylation); analyzed on Agilent 7890 GC with DB-5MS column and 5975C MSD; d27-myristic acid internal standard used.
• Data Processing: Initial peak detection and deconvolution via Agilent MassHunter Qualitative (LC-MS) and AMDIS (GC-MS); results imported into Mass Profiler Professional (MPP) for alignment, normalization, and baseline correction.

Used Instrumentation

• Agilent 6520 QTOF mass spectrometer with 1290 UPLC system
• Agilent 7890 GC coupled to 5975C quadrupole MSD
• Agilent MassHunter Qualitative Software and AMDIS for initial deconvolution
• Agilent Mass Profiler Professional for chemometric analysis

Main Results and Discussion

• Feature Detection: LC-MS positive mode detected >700 entities; LC-MS negative ~400; GC-MS ~200 features after quality control.
• Differential Metabolites: Statistical analysis (t-test, p<0.05, fold-change) identified key compounds upregulated by rapamycin (e.g., oxidized glutathione, nicotinamide N-oxide, palmitoleic acid, lactic acid, fumaric acid) and downregulated species (e.g., N-acetylserine, glucose-6-phosphate, D-ribose).
• Multivariate Analysis: PCA revealed clear separation of treated versus control groups (component 1 explains 81% variance), confirming robust metabolic shifts.
  
• Pathway Enrichment: Combined LC-MS and GC-MS data highlighted enriched pathways including ABC transporters, glycolysis/gluconeogenesis, purine metabolism, and glycerophospholipid metabolism.
• Omics Integration: Overlay with genomics and proteomics pathway lists identified common processes impacted by rapamycin, reinforcing system-level insights into amino acid transport and energy metabolism.

Benefits and Practical Applications

• Comprehensive Coverage: Multi-platform approach increases metabolite detection across diverse chemical classes.
• Statistical Rigor: MPP enables reliable feature alignment, normalization, and significance testing for high-throughput datasets.
• Systems Biology: Integration with genomics and proteomics facilitates holistic pathway mapping and identification of candidate biomarkers.
• Drug Mechanism Elucidation: Profiling metabolic changes uncovers cellular responses to rapamycin, aiding therapeutic development and monitoring.

Future Trends and Applications

• Enhanced Libraries and Annotation: Expansion of spectral databases (METLIN, NIST) will improve metabolite identification confidence.
• Automated Workflows: Advances in software automation and AI-driven feature annotation will accelerate data processing and interpretation.
• Single-Cell Metabolomics: Emerging techniques may enable metabolic profiling at single-cell resolution to reveal cellular heterogeneity.
• Integrated Multi-Omics Platforms: Unified pipelines combining metabolomics, proteomics, transcriptomics, and fluxomics will deepen system-level understanding.
• Clinical Translation: Robust biomarkers discovered through such workflows may be applied in diagnostics and personalized medicine.

Conclusion

This study demonstrates the effectiveness of combining LC-MS and GC-MS metabolomics with Agilent Mass Profiler Professional to characterize rapamycin-induced metabolic alterations in HEK 293 cells. Integration with genomics and proteomics data underscores key pathways modulated by treatment and highlights the value of multi-omics strategies for drug mechanism studies and biomarker discovery.

Reference

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4. T. Peng et al. Rapamycin mimics a starvation-like signal distinct from amino acid and glucose deprivation. Mol. Cell. Biol., 2002.