Multiplatform untargeted metabolomics.

Area of Science:

• Metabolomics
• Analytical Chemistry
• Bioinformatics

Background:

• Untargeted metabolomics aims to comprehensively profile metabolites but faces challenges with low metabolite coverage and identification uncertainty.
• Individual analytical techniques detect only a fraction of the thousands of metabolites present in biological samples like urine or serum.
• Single-platform methods often suffer from poor reproducibility, limiting the reliability of findings.

Purpose of the Study:

• To review the advantages, limitations, and recent advancements of multiplatform approaches in untargeted metabolomics.
• To highlight strategies for improving metabolite detection and identification accuracy.
• To discuss solutions for data analysis challenges in multiplatform metabolomics.

Main Methods:

• Employing multiple analytical techniques (multiplatform approach) for broader metabolite coverage.
• Utilizing synergistic sample preparation and combinatorial or sequential techniques (non-destructive and destructive).
• Implementing probabilistic approaches for enhanced peak detection and metabolite identification.

Main Results:

• Multiplatform strategies significantly increase the number of reliably detected and correctly assigned metabolites compared to single platforms.
• Synergistic sample preparation and advanced identification algorithms improve data quality and reduce ambiguity.
• Addressing challenges in processing large, disparate datasets and applying appropriate multivariate statistical models (e.g., multiblock analysis).

Conclusions:

• A multiplatform approach is crucial for advancing untargeted metabolomics, offering improved coverage, accuracy, and reproducibility.
• Overcoming data analysis hurdles with specialized statistical methods is key to fully leveraging multiplatform data.
• This strategy provides a more robust foundation for biological discovery through comprehensive metabolic profiling.