Chapter 2—Other Omics Sciences and Metabolomics
In the first chapter of this guide, we provided a high-level overview of metabolites and metabolomics, the study of all of the metabolites in a particular organism or system. In this chapter, we take a deeper dive into metabolomics by exploring how it fits into the overall omics landscape.
What are Omics?
Advances in scientific technology have made it possible to survey living systems at the tissue and cellular levels by measuring the thousands of molecules that comprise those systems. The high-throughput measurement of these molecules is collectively referred to as “omics.”1 While there are several types of omics approaches, there are four main omics disciplines that reflect the ever-increasing complexity of living systems, from genetic code to phenotype: genomics, transcriptomics, proteomics, and metabolomics.
Each of these omics disciplines can be studied individually, but biological systems don’t act in a vacuum. Combining different omics disciplines through multi-omics studies2 provides a holistic picture of living organisms and systems. Below, we define each of the four major omics disciplines, compare metabolomics to each of the other three, and discuss how studying them synergistically can drive scientific discovery.
Genomics and Metabolomics
Genomics and metabolomics sit at opposite ends of the biological spectrum. Genomics is the study, typically via sequencing, of all of the genetic material in an organism, including single nucleotide polymorphisms (SNPs), indels, gene loss and amplification, and copy number variations (CNVs). In other words, genomics is the study of an organism’s blueprint and any changes to that blueprint that impact the genetic potential. Metabolomics, on the other hand, is the complete set of small molecules present in an organism at a single point in time, measured via mass spectrometry (most commonly) or nuclear magnetic resonance (NMR). In essence, metabolomics is genetic potential in action and can be impacted by literally anything: food, drugs, medications, stress, etc.
It isn’t difficult to imagine how metabolomics and genomics datasets can add important contextual information to one another to elucidate why specific genetic anomalies are associated with specific phenotypes. The integration of genomic and metabolomic data has been extensively used to elucidate metabolite chemical structures, functions, and biosynthetic origins, and several tools have been developed3 to aid such efforts. Genome-metabolome integrations have also proven particularly useful in oncology research, as cancer has critical genetic and metabolomic characteristics. One study4 has even used genomics-metabolomics integration to help elucidate why genetic aberrations can be observed in both healthy and cancerous cells and why specific environmental and nutrient cues act as “selectors” during oncogenesis.
Transcriptomics and Metabolomics
While genomics represents an organism’s genetic potential, not all of that potential is enacted. In fact, in humans, only 1.5% to 2% of the genome represents protein-coding genes. Cataloging the complete set of RNA transcripts (typically via microarrays or RNA sequencing) from DNA in a cell, tissue, or organism provides a better idea of which genetic elements have been activated.
Transcriptomics is a powerful tool for cataloging not just expressed genes, but also noncoding RNA elements (ribosomal RNA, messenger RNA, transfer RNA, microRNA, and long noncoding RNA) that may impact gene expression, and how these elements differ in healthy and diseased states. Transcripts often reveal signatures that cannot be detected through genomics approaches alone, such as RNA regulators of driver genes5 in oncology. But the presence of a transcript doesn’t always equate to mature, functional protein. Metabolomics, on the other hand, reveals critical information about which genetic instructions have not only been transcribed into RNA but have actually been converted into measurable phenotypes.
Integrated transcriptomics-metabolomics datasets can help demystify the bidirectional and multi-faceted interactions between DNA and RNA elements that lead to observable phenotypes and provide actionable insights into what is going on in a biological system. For example, combining transcriptomics and metabolomics data can make molecular analysis of a blood sample even more comprehensive6 when diagnosing disease, and has been proposed as a powerful approach to drive personalized and precision medicine across a range of conditions.
Proteomics and Metabolomics
Proteomics is the most closely related omics technology to metabolomics. It adds another layer of complexity to genomics and transcriptomics by revealing not genetic potential, but which gene products (ie, proteins) have actually been synthesized by an organism. There are several things that can impact whether or not a mature, functional protein is produced by an organism, such as post-translational modifications or environmental toxins and nutrients, which is why combining genomic/transcriptomic and proteomic datasets can reveal important differences between what an organism might be doing and what an organism is actually doing.
Metabolomics is the study of the small molecules in an organism, and some of these are derived from proteins. Yet, there are some key differences between metabolomics and proteomics. First and foremost, proteomics captures information about all proteins produced by an organism, while metabolomics focuses on metabolites alone. Second, while proteomics seeks to understand and describe protein structure and function, metabolomics studies metabolites under given sets of conditions—for example, during drug treatment.
Combined, proteomics and metabolomics provide a complete phenotypic picture. Proteomics reveals which RNA transcripts have yielded mature, functional proteins while metabolomics drills deeper and explores how those proteins act differently under divergent circumstances. Integrated datasets combining proteomics and metabolomics have been used to identify cancer biomarkers7 and characterize mechanisms of action8 of anti-tumor agents.
Why Multi-omics Approaches Need Metabolomics
As more and more research combines various omic approaches to elucidate the basics of human biology and demystify disease progression, multi-omics is driving advances in human health and disease research. The U.S. market size of single-cell multi-omics alone is expected to exceed $7 billion USD by 2027—and metabolomics plays a critical role in enabling this rapid growth. Metabolomics is the study of specific phenotypes that result from the intricate and complex interaction between the genome, transcriptome, proteome, and the environment. It is the final and critical piece of the puzzle because only it can reveal the result of these complex interactions.
Lipidomics—the large-scale study of pathways and networks of cellular lipids—and glycomics—the systematic study of all glycan (ie, sugar) structures of a given cell, tissue, or organism—are metabolomics “specializations” that are driving a significant proportion of metabolomics’ influence on the overall contribution of multi-omics studies to advancing our understanding of health and disease. These specific metabolites often play critical roles in disease pathogenesis or serve as biomarkers. Sophisticated tools, such as the iKnife9, are being developed to aid in diagnosing, treating, and monitoring disease. For example, the iKnife can inform surgeons about the disease status of tissue during procedures by both heating the tissue then performing real-time lipodomics analysis of the resulting smoke.
The iKnife is just one very unique example of the many ways metabolomics is ushering in a new era in our understanding of health and disease. Multi-omics approaches centered on metabolomics as a key component are poised to revolutionize healthcare. Metabolon is positioned at the forefront of this new era in clinical research. The global metabolomics platform, which leverages Metabolon’s unmatched in-house database of over 5,400 small molecules, helps researchers identify pharmacodynamic, efficacy and response biomarkers and reveals changes in key biological pathways. Several companies have already leveraged this platform to improve their clinical trials and ensure successful trials of a variety of therapeutics.
Now that you understand where metabolomics fits into the multi-omics puzzle, this guide will further explore metabolomics as a technology. In the next chapter, you’ll learn how metabolites are detected and the basic method of generating metabolomics data. Then, in subsequent chapters, we’ll explore some of the most promising academic, industrial, and clinical applications of metabolomics so you can get a better idea of how metabolomics might help propel your own research efforts.
1. Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials; Board on Health Care Services; Board on Health Sciences Policy; Institute of Medicine; Micheel CM, Nass SJ, Omenn GS, editors. Evolution of Translational Omics: Lessons Learned and the Path Forward. Washington (DC): National Academies Press (US); 2012 Mar 23. 2, Omics-Based Clinical Discovery: Science, Technology, and Applications. Available from: https://www.ncbi.nlm.nih.gov/books/NBK202165/?report=classic
2. Subramanian I, Verma S, Jere A, Anamika K. Multi-omics Data Integration, Interpretation, and Its Application. Bioinformatics and Biology Insights. 01/31 2020;14:117793221989905. doi:10.1177/1177932219899051
3. Schorn MA, Verhoeven S, Ridder L, et al. A community resource for paired genomic and metabolomic data mining. Nature Chemical Biology. 2021/04/01 2021;17(4):363-368. doi:10.1038/s41589-020-00724-z
4. Gonçalves E, Frezza C. Genome and metabolome: chance and necessity. Genome Biology. 2021/09/23 2021;22(1):276. doi:10.1186/s13059-021-02501-0
5. Dai X, Kaushik AC, Zhang J. The Emerging Role of Major Regulatory RNAs in Cancer Control. Front Oncol. 2019;9:920. Published 2019 Sep 24. doi:10.3389/fonc.2019.00920
6. Li S, Todor A, Luo R. Blood transcriptomics and metabolomics for personalized medicine. Computational and Structural Biotechnology Journal. 2016/01/01/ 2016;14:1-7. doi:10.1016/j.csbj.2015.10.005
7. Ma Y, Zhang P, Wang F, Liu W, Yang J, Qin H. An Integrated Proteomics and Metabolomics Approach for Defining Oncofetal Biomarkers in the Colorectal Cancer. Annals of Surgery. 2012;255(4). doi:10.1097/SLA.0b013e31824a9a8b
8. Chen Y, Ni J, Gao Y, et al. Integrated proteomics and metabolomics reveals the comprehensive characterization of antitumor mechanism underlying Shikonin on colon cancer patient-derived xenograft model. Scientific Reports. 2020/08/24 2020;10(1):14092. doi:10.1038/s41598-020-71116-5
9. Tzafetas M, Mitra A, Paraskevaidi M, et al. The intelligent knife (iKnife) and its intraoperative diagnostic advantage for the treatment of cervical disease. Proc Natl Acad Sci U S A. Mar 31 2020;117(13):7338-7346. doi:10.1073/pnas.1916960117
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