Single-platform ‘multi-omic’ profiling: unified mass spectrometry and computational workflows for integrative proteomics–metabolomics analysis

Abstract

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.

Multi-Omic Analysis Reveals Disruption of Cholesterol Homeostasis by Cannabidiol in Human Cell Lines

“…Recent advances in mass spectrometry-based omics has enabled nearly comprehensive identification and quantification of cellular proteomes and metabolomes (Blum et al, 2018). Multi-omic profiling strategies that combine mass spectrometry based proteomics with next generation sequencing-based transcriptomics can reveal critical and unexpected insights into the mechanisms of drug action in human cell lines (Hafner et al, 2019;Norris et al, 2017).…”

Systems level profiling of arginine starvation reveals MYC and ERK adaptive metabolic reprogramming

“…Systems level analysis pairing global, unbiased, and integrative proteomic and metabolic analyses have recently been performed in models of plants (Wienkoop et al , 2008; Faddetta et al , 2018; Blachowicz et al , 2019), parasites, and antibiotic resistance (Akpunarlieva et al , 2017; Park et al , 2016). However, utilization of integrative proteomic-metabolomic analysis has been limited with respect to modeling drug resistance and metabolism in cancer models (Feng et al , 2018; Blum et al , 2018; A.L. et al , 2015; Cai et al , 2010).…”

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

“…In the context of data explosion at every omics level and accumulated knowledge of complex interactions between those molecular layers, a better paradigm integrating multi-omics is a potential solution to comprehend the underlying biological processes as well as to give better predictions on the outcome of interest (e.g., diagnosis, prognosis) [157][158][159]. For this exact reason, the idea of a single platform for a unifying proteomics-metabolomics analysis has been proposed [160]. We hereby provide some excellent examples to demonstrate the potential of multi-omics approaches.…”