crossmetabolome

Crossmetabolome is a conceptual framework in metabolomics that focuses on integrating and analyzing metabolite profiles across multiple biological systems, conditions, or environments to reveal shared metabolic patterns, metabolite exchange, and interaction networks. It emphasizes connections that transcend individual studies or sample types.

The approach encompasses cross-sectional and longitudinal data, cross-species comparisons, and host–microbiome or environmental interactions, aiming to identify universal or context-specific metabolites and pathways. It seeks to understand how metabolite landscapes relate across different biological contexts and how metabolites influence each other in complex systems.

Methods used in crossmetabolome research combine high-throughput metabolomics (such as LC-MS, GC-MS, and NMR), data normalization and alignment across datasets, standardized metabolite annotation, and network or machine-learning analyses to detect cross-dataset correlations and modules. This often involves integrating disparate data types and applying comparative or integrative analyses to reveal cross-system patterns.

Applications include studying microbial–host metabolite exchange, ecological metabolomics, and biomarker discovery with signatures that generalize across populations or environments, as well as integration with other omics such as transcriptomics or proteomics to obtain a more holistic view of metabolism.

Key challenges include inconsistent metabolite identification across platforms, batch effects, variability in sample types, limited reference libraries, and difficulties in reproducibility and standardization. The term appears in publications describing multi-source or cross-environment metabolomics and is used to emphasize systemic metabolic interactions and integrative interpretation beyond single-study analyses.