LC/MS, GC/MS Data Analysis Software - Multi-omics Analysis Package

Importance of the Topic

The integration of metabolomics, proteomics and flux analysis generates large and complex datasets that require efficient visualization and interpretation. Multi-omics data integration is crucial in metabolic engineering, drug discovery and life sciences research to identify pathway bottlenecks, optimize bioprocesses and support decision making.

Objectives and Overview

The Multi-omics Analysis Package aims to streamline the analysis of metabolic, proteomic and flux data by automatically generating metabolic maps and supporting a variety of statistical and network analyses. Developed by Shimadzu, the software connects data processing and visualization tools via the GARUDA platform to provide seamless operations from data acquisition to result interpretation.

Methodology

Data acquired by LC-MS, LC-MS/MS or GC-MS/MS systems is imported into the package using ready-to-use methods from Shimadzu databases for sample pretreatment and analytical conditions. The workflow includes data import, pre-processing, statistical evaluation with volcano plots and correlation coefficient calculation, and network analysis via Cytoscape or VANTED. Quantitative changes in metabolites and proteins are mapped onto metabolic pathways in Graph Markup Language format to reveal dynamic responses and correlations.

Instrumentation Used

• Q-TOF LC-MS system for high resolution mass spectrometry
• Triple quadrupole LC-MS/MS system for targeted quantitation
• GC-MS/MS system for volatile metabolite profiling
• Shimadzu Smart Metabolites Database with ready-to-use method packages
• GARUDA platform gadgets including Volcano Plot, Cytoscape and VANTED modules

Main Results and Discussion

In a time course study of MCF-7 cell culture medium, metabolic shifts were visualized over 24 hours. Lactic acid levels increased continuously, indicating enhanced glycolysis. Malic acid exhibited a gradual rise while citric and isocitric acids showed accumulative trends. Succinic acid temporarily accumulated at intermediate time points. Mapping these changes onto a central metabolic map highlighted pathway bottlenecks and temporal correlations, demonstrating the package ability to reveal dynamic metabolic regulation.

Benefits and Practical Applications

• Automated visualization reduces manual mapping effort for complex datasets
• Integrated workflow from data acquisition to pathway mapping enhances efficiency
• Compatibility with GARUDA gadgets enables multidimensional statistical and network analyses
• Ready-to-use method packages ensure reproducible and standardized procedures
• Supports applications in metabolic engineering, drug discovery and quality control

Future Trends and Possibilities

Emerging directions include integration of machine learning algorithms for predictive pathway modeling, real-time data processing for continuous bioprocess monitoring, and cloud-based collaboration for large-scale multi-omics projects. Expansion of method libraries and incorporation of additional omics layers such as lipidomics and glycomics will further enhance the scope of integrated analyses.

Conclusion

The Shimadzu Multi-omics Analysis Package delivers a unified platform for efficient processing, statistical evaluation and graphical representation of large-scale omics data. By automating metabolic map generation and leveraging GARUDA-connected gadgets, it addresses critical challenges in multi-omics data interpretation and supports diverse research and industrial applications.

Reference

None provided