Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology

Area of Science:

• Biochemistry
• Bioinformatics
• Systems Biology

Background:

• Metabolomic data from multiple mass spectrometry (MS) techniques offer comprehensive insights.
• Integrating multi-omics data (metabolomics, proteomics, transcriptomics) presents significant bioinformatic challenges.
• Automated analysis is needed to extract biologically relevant conclusions from complex datasets.

Purpose of the Study:

• To develop and validate a data processing approach for automated prediction of dysregulated metabolic pathways.
• To integrate diverse metabolomic data types and contextualize them with other omics data.
• To streamline the extraction of biological information from multi-omics studies.

Main Methods:

• Developed a platform for autonomous integration of multiple MS-based metabolomics data types.
• The platform handles variations in sample preparation, chromatography, and MS detection.
• Applied a multimodal analysis approach to colorectal cancer (CRC) data, integrating LC-MS, proteomic, and transcriptomic datasets.

Main Results:

• Achieved a comprehensive overview of colon cancer metabolic dysregulation.
• Reported an average 17% increase in detected dysregulated metabolites per pathway.
• Demonstrated high concordance (95%) between altered metabolic pathways and dysregulated genes/proteins, validating findings at a systems level.

Conclusions:

• The developed platform effectively automates the prediction of dysregulated metabolic pathways from integrated multi-omics data.
• This approach enhances the biological interpretation and confidence in metabolomic analyses.
• The platform is accessible via XCMS Online, facilitating broader research applications.