Single-platform ‘multi-omic’ profiling: unified mass spectrometry and computational workflows for integrative proteomics–metabolomics analysis
The objective of omics studies is to globally measure the different classes of cellular biomolecules present in a biological specimen (e.g. proteins, metabolites) as accurately as possible in order to investigate the corresponding ‘states’ of biological systems. High throughput omics technologies are emerging as an increasingly powerful toolkit in the rapidly advancing field of systems biology, enabling the systematic study of dynamic molecular processes that drive core cell functions like growth, sensing, and environmental adaptation. Advances in high resolution mass spectrometry, in particular, now allow for the near comprehensive study of cellular proteins and metabolites that underlie physiological homeostasis and disease pathogenesis. Yet while the expression levels, modification states, and functional associations of diverse molecular species are now measurable, existing proteomic and metabolomic data generation and analysis workflows are often specialized and incompatible. Hence, while there are now many reports of ad hoc combinations of unimolecular proteomic and metabolomic workflows, only a limited number of multi-omic profiling approaches have been reported for obtaining different molecular measurements (proteins, metabolites, nucleic acids) in parallel from a single biological sample. Moreover, elucidating how the myriad of measured cellular components are linked together functionally within the metabolic processes, signal transduction pathways, and macromolecular interaction networks central to living systems remains a massive, complicated, and uncertain endeavor. Presented here is a review of convergent mass spectrometry-based multi-omic methodologies, with a focus on notable recent advances and remaining challenges in terms of efficient sample preparation, biochemical separations, data acquisition, and integrative computational strategies. We outline a unifying network-based integrative framework to better derive biological knowledge from integrated profiling studies with the goal of realizing the full potential of multi-omic data sets.