Chapter 1 — Introduction to Multiomics

What is Multiomics?

Historically, our understanding of how organisms work has been largely facilitated by studying individual aspects: the genome, proteins, metabolites, or even epigenetic signatures. While these approaches provide important insights that have led to the development of incredible medicines, advances in food production, and environmental protection strategies, they represent only a small piece of the puzzle.

Multiomics is the combination of multiple single omic methodologies (e.g., genomics, transcriptomics, proteomics, epigenomics, and metabolomics) to gain a more holistic understanding of biological mechanisms and genotype-to-phenotype relationships¹.

Multiomics studies can help identify causal mechanisms and thus, diagnostic and therapeutic biomarkers². Such studies are increasingly facilitated by advances in high throughput technologies and advanced bioinformatics approaches³.

Types of Omics Data

Omics studies provide comprehensive (i.e., “global”) assessments of the molecule in question². For example, while the field of genetics may interrogate single genes or variants, the field of genomics studies the entire genome of an organism (including non-coding regions) and how different genes interact with each other and with the environment⁴. This comprehensive approach enables the identification of important correlations that may otherwise be missed.

There are multiple different omics approaches, but generally they can be categorized as follows:

• Genomics: Genomics is the study of an organism’s or environmental sample’s complete set of DNA, the genome⁵ through (most often) high-throughput sequencing, assembly and analysis.
• Epigenomics: Epigenomics is the study of all of the chemical compounds and proteins that attach to DNA and modify gene expression and downstream protein production6. DNA modifications can be split into two groups: methylation and histone modification.
• Transcriptomics: Transcriptomics is the study of an organism’s or environmental sample’s complete set of RNA transcripts (i.e., gene readouts)⁷. Transcriptomics is the next level up from genomics, moving beyond the genes that are present to those genes that are transcribed.
• Proteomics: Proteomics is the study of the structure, function, composition, and interactions of the proteins present in an organism or environmental sample at a certain time^8,9.
• Metabolomics: Metabolomics is the study of all metabolites present in an organism or environmental sample, particularly in relation to genetic and environmental influences¹⁰. Lipidomics — the study of all lipid molecules present in a sample — is a specialized branch of metabolomics¹¹.

Figure 1. The relationship between multiple omics types in multiomics research². Each omics layer can impact each other omics layer in multiple, complex, and bidirectional ways (arrows). Multiomics analyses can start with the genome and add layers, or start with phenotypic readouts and add layers.

A Brief History of Multiomics Research

Although many give credit to genomics as the first omics approach to be developed, a close inspection of history reveals that genomic, transcriptomics, proteomics, and even metabolomics techniques were developed largely in parallel (although epigenomics and microbiome research did appear later)^8,16-19.

Nevertheless, the seeds for multiomics research were planted with the completed sequence²⁰ of the human genome by the International Human Genome Sequencing Consortium (an idea that was born in the early 90s¹⁶). The enabling technology and data analysis approaches underpinning this herculean effort—which revealed enormous potential for biological discovery and subsequent implications for human health—became the foundation for combining multiple omics datasets.

Combining multiple omics datasets isn’t straightforward, however, and requires a significant amount of time, skill, and acumen with various analytical tools and techniques^2,21. As with any scientific study, successful multiomics research studies require rigorous study design. In the next chapter of this guide, we discuss some of the challenges associated with multiomics research and steps you can take to design your study.

The Future of Multiomics

Even as techniques for “first-generation” multiomics research are being polished, the development of the next-generation of multiomics techniques is well underway. The future of multiomics and, arguably, human medicine as a whole, lies in two promising areas (which we’ll discuss in greater detail in the final chapter of this guide):

• Single cell multiomics: Single-cell analyses²² enable researchers to study cells at the finest resolution possible. By focusing on individual cells, important discoveries such as rare cell types implicated in disease or better therapeutic targets, can be identified. Single cell DNA and RNA sequencing were named “2013 Method of the Year” by Nature²³ and since then have made important contributions to our understanding of biology and various disease mechanisms²⁴. As single-cell genomics/transcriptomics, proteomics, metabolomics, and other techniques are matured, single-cell multiomics studies will become increasingly common.
• Spatial multiomics: Just as single omics techniques are unable to provide a complete picture of a biological mechanism, single-cell analyses are necessarily limited. Spatial analyses put cells in the context of one another, providing a more holistic view of health and disease processes. Spatial transcriptomics, for example, has already provided key clues to tumor microenvironment-specific characteristics that affect treatment responses^25-26. Just as with single-cell analyses, the combination of multiple spatial omics approaches has an important future in scientific research²⁷.

Conclusions

The history of multiomics is a testament to the rapid evolution of biomedical science. What began with the mapping of the human genome has evolved into an integrated approach that promises to revolutionize our understanding of biology. While challenges remain, the future of multiomics holds immense potential for advancing science and improving human health. In the next chapter, we’ll dig deeper into the challenges associated with multiomics analysis and how to design a robust multiomics research study.

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