Multi-omics Analysis Packag

Significance of the Topic

High-throughput mass spectrometry generates vast volumes of metabolomic, proteomic, and fluxomic data that require integrated analysis within biochemical pathways.
The ability to automatically map quantitative changes onto metabolic networks accelerates discovery in drug development, functional food design, and bioengineering.

Objectives and Study Overview

• Simplify the workflow from data import to pathway visualization for LC-MS and GC-MS experiments.
• Provide statistical tools to extract significant metabolites across multiple sample groups.
• Offer ready-to-use templates compatible with Shimadzu method packages covering primary metabolites, lipid mediators, bile acids, and other compound classes.

Methodology

The software automates key steps in multi-omics data processing:

• Import of LC-MS, LC-MS/MS, and GC-MS datasets in Shimadzu formats.
• Selection of a metabolic map template or custom layout for compound visualization.
• Application of statistical modules—volcano plotting for pairwise comparison, principal component analysis, hierarchical clustering, and box plots for multi-group analysis.
• Interactive exploration by linking statistical plots and pathway maps for compound confirmation.

Instrumentation

• LC-QTOF/MS systems for accurate mass profiling of primary and secondary metabolites.
• Triple quadrupole LC-MS/MS and GC-MS/MS for targeted quantification across diverse compound classes.
• Multi-omics Analysis Package software (Shimadzu) running on the GARUDA platform with integrated gadgets (Volcano Plot, EasyStats, VANTED, Cytoscape).

Main Results and Discussion

• Time-course analysis of fatty acid-derived lipid mediators in human plasma revealed dynamic metabolic shifts visualized on a lipid pathway map.
• Comparative profiling of primary metabolites from E. coli and yeast culture supernatants highlighted key pathway divergences related to carbon metabolism.
• Temporal tracking of metabolites in iPS cell culture identified biomarkers of cellular differentiation stages.
• Rapid bile acid profiling in mouse feces and human plasma demonstrated the utility for high-throughput routine screening.
• Combined LC-MS and GC-MS analysis of genetically modified Drosophila enabled comprehensive pathway-level insights by overlaying both datasets on a unified metabolic map.

Benefits and Practical Applications

• Streamlines data visualization by eliminating manual graph creation and map annotation.
• Facilitates hypothesis generation through integrated statistical analysis and pathway context.
• Enhances throughput and reproducibility in metabolomics, proteomics, and flux analysis workflows.
• Supports multidisciplinary research in pharmaceuticals, nutrition, microbiology, and biotechnology.

Future Trends and Potential Applications

• Expansion to incorporate transcriptomics and lipidomics datasets for holistic multi-omics integration.
• Development of AI-driven pattern recognition to predict pathway alterations and biomarker candidates.
• Cloud-based collaborative platforms enabling remote data sharing and joint pathway curation.
• Customized visualization templates for emerging compound classes and novel metabolic reactions.

Conclusion

The Multi-omics Analysis Package from Shimadzu delivers an integrated solution for automated metabolic mapping and statistical evaluation of complex mass spectrometry datasets, driving efficiency and insight in life science research.