Chapter 8 — The Future of Multiomics

In this guide, we’ve explored the incredible potential of multiomics analysis to deepen our understanding of organisms, ecosystems, and health and disease by providing a holistic view single-omics studies cannot achieve. As technology advances, multiomics research will become increasingly transformative across various domains from basic biological research to personalized and precision medicine.

Although the applications of multiomics research span a broad range from human healthcare to agricultural practices, some of the most transformative applications lie in the future of medicine. In fact, some of the biggest modern buzzwords, such as “precision medicine” and “personalized medicine” depend on multiomics research. In the following sections, we discuss how multiomics can drive the next revolution in human medicine.

Multiomics and the Future of Medicine

Genomics, transcriptomics, metabolomics, and other omics modalities, when applied individually, have revealed the enormous complexity of human health and disease¹. A wide array of enabling technologies (Figure 1)² have introduced this transformation in our understanding of human biology—and have opened the door to detecting and treating disease more accurately and precisely than ever.

The biological complexity uncovered by individual omics modalities not only underpins the push for multiomics analyses but is also the underlying concept of precision medicine and personalized medicine.

Personalized and precision medicine

Personalized medicine and precision medicine are closely related and have been used interchangeably³. While both take the approach of deeply characterizing individuals at the molecular level, their applications are slightly different.

Precision medicine aims to predict, prevent, and cure disease more precisely¹ and can be used to predict disease risk or the best treatments for specific groups of patients based on omics data collected from groups of individuals with the same biological and clinical characteristics³. Precision medicine drives and enables personalized medicine⁴, which considers factors specific to an individual, including their environment, when considering how to treat their specific disease³.

Multiomics analyses have made considerable contributions in the precision and personalized medicine spaces already. For example, researchers have found correlations in a cohort of Egyptian people between certain genetic variations and serum vitamin D levels, which can serve as a risk factor for myocardial infarction⁵. In oncology research, multiomics analyses have been used to identify cancer subgroups with distinct survival rates⁶, to link gene copy-number variations to post-translational modifications and clinical outcomes⁷, and many other significant discoveries².

These are just a few examples of the advanced, precise, and personalized discoveries enabled by multiomics analysis. As technology advances and computational methods for analyzing multiomics data improve (see Chapter 2 of this guide), multiomics analyses will continue to drive the future of precision healthcare.

Disease diagnostics and prognostics

Biomarker detection is one of the most promising applications of multiomics analysis, as discussed in Chapter 1 of this guide. With accurate, strong biomarkers, researchers can develop diagnostic and prognostic approaches⁸ to identify better therapeutic targets for improved treatment (see next section).

There has been significant research using multiomics approaches to identify diagnostic and prognostic biomarkers in oncology. Researchers have identified diagnostic metabolic biomarkers in pancreatic ductal carcinoma⁹ and genomic, transcriptomic, proteomic, and metabolic biomarkers for early ovarian cancer diagnosis¹⁰.

Diagnostic and prognostic biomarkers for other diseases have also been identified, including stroke¹¹, obesity and its comorbidities¹², and even COVID-19¹³. Additionally, researchers utilized a multiomics approach to diagnose rare diseases and inform ongoing medical care changes in a cohort of over 200 infants¹⁴.

Drug discovery and development

The natural follow-up to disease diagnosis is treatment. As with diagnosis and prognosis, drug discovery and development can be informed and aided by multiomics analysis. By revealing the molecular mechanisms behind drug responses, researchers can identify new therapeutic targets, which can be genes, transcription factors, metabolites or metabolic pathways, or other molecular signatures. For example, researchers identified two H3 histone modifications, H3K27ac and H3K9ac, as potential therapeutic targets in Alzheimer’s disease¹⁵. In meningiomas, a type of benign brain tumor, the molecular cross-talk between ubiquitin ligase TRAF7 and transcription factor KLF4 was identified as a potential target for the development of novel treatments¹⁶. In hypertrophic cardiomyopathy, altered energy metabolism and mitochondrial function suggest that therapies developed to improve metabolic function and reduce mitochondrial injury may help decrease disease¹⁷.

Enabling Technologies for the Future of Multiomics

Many of the multiomic discoveries driving precision and personalized medicine, disease diagnosis, and drug discovery are likely to depend on two enabling technologies: single-cell and spatial omics. These approaches consider the cellular and tissue-specific complexities of disease, placing biological discoveries in context at the cellular, tissue, and organismal level, enabling a more precise and accurate understanding of disease.

Single-cell omics

Single-cell analyses¹⁸ enable researchers to study cells at the finest resolution possible, enabling the discoveries of rare cell types involved in disease or which cells a certain therapeutic is effective against (or not), for example. Single cell DNA and RNA sequencing were named “2013 Method of the Year” by Nature¹⁹ and since then have made important contributions to our understanding of biology and various disease mechanisms²⁰.

Recently, researchers used single-cell analysis to identify a new type of cell called a SWAT cell²¹, which shares a progenitor with adipocytes—suggesting that toggling the balance between different cellular developmental states could inform precise new therapeutic targets for cardiometabolic disease.

Single-cell analyses are also the foundation for comprehensive maps such as the Human Cell Atlas²² and Tabula Sapiens²³, providing foundational reference points of human biology. By comparing new datasets to these baselines, researchers can better understand disease origins and identify biologically and clinically significant alterations (see Figure 2).

Spatial omics

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^24-25. It has also identified and spatially located a subtype of dopaminergic neurons that are highly susceptible to degeneration and enriched for heritable risk in Parkinson’s disease²⁶.

Concluding Remarks

Multiomics research represents a paradigm shift in our approach to understanding biology and the molecular changes that occur when things go wrong. It offers unprecedented opportunities to not only understand biology at its most basic level, but to more completely understand disease, enabling a future where more effective, safer, and tailored treatments for a wide range of diseases are available.

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