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

Significance of Topic

Modern life science research increasingly relies on integrated analysis of metabolomics proteomics and fluxomics data to drive discoveries in drug development functional foods and bioengineering. High-throughput LC-MS and GC-MS platforms produce vast data sets that require intuitive visualization and robust statistical analysis to extract biologically meaningful patterns and metabolic pathway insights.

Study Objectives and Overview

The Multi-omics Analysis Package by Shimadzu is designed to streamline downstream processing of mass spectrometry data across multiple omics layers. Key aims include automated metabolic map generation multivariate statistical analysis and seamless integration with Shimadzu’s method packages for primary metabolites cell culture profiling lipid mediators and bile acids.

Methods and Instrumentation

The software supports data import from LC-QTOF/MS LC-MS/MS and GC-MS/MS systems. It leverages GARUDA-based gadgets for statistical visualization and pathway mapping. Core tools include:

• Volcano Plot for paired group comparisons using fold-change and t-test metrics
• EasyStats for PCA HCA and box plot generation in a unified view
• VANTED for network-level data overlay on metabolic pathways
• Cytoscape for correlation network visualization and integration of gene expression profiles

Key Results and Discussion

Case studies illustrate display of concentration changes on customizable metabolic maps and identification of significant features via linked PCA HCA and box plots. Examples include lipid mediator profiling in human plasma primary metabolite flux in E coli and yeast cell extracts time-course tracking of iPS cell culture supernatants and bile acid composition in plasma and fecal samples. These demonstrations validate the package’s ability to reduce manual charting effort accelerate biomarker discovery and support hypothesis generation.

Benefits and Practical Applications

The Multi-omics Analysis Package offers:

• Rapid visual mapping of quantitative changes in metabolic networks
• Statistical identification of key features across experimental groups
• Preconfigured templates aligned to Shimadzu method packages for streamlined workflows
• Customization of visualization templates for specific research needs

These capabilities enhance throughput in drug discovery QA/QC laboratories metabolic engineering and systems biology investigations.

Future Trends and Opportunities

Integration of additional omics layers such as transcriptomics and lipidomics data sets promises even deeper insights into cellular phenotypes. Cloud-based deployment and AI-driven pattern recognition could further automate pathway annotation and predictive modeling. Expansion of community-driven template libraries and tighter coupling with electronic lab notebooks will improve reproducibility and data sharing.

Conclusion

The Shimadzu Multi-omics Analysis Package addresses the growing need for comprehensive interpretation of complex mass spectrometry data. By combining automated map generation with advanced statistical modules and method-specific templates it accelerates metabolic engineering research and supports a range of life science applications.

Instrumentation Used

Supported MS platforms

• LC-QTOF/MS
• LC-MS/MS
• GC-MS/MS

References

Method packages Primary Metabolites C146-E437 Cell Culture Profiling C146-E408 Lipid Mediators C146-E381 Bile Acids C146-E428