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Multiomics: Bridging the Gaps in Biological Research

Expanding beyond genomics and transcriptomics can produce richer data sets and generate deeper biological insights

Morgana Moretti, PhD

Morgana Moretti, PhD, is an active scientist and freelance medical writer with more than 12 years of research and writing experience. She holds a doctoral degree in biochemistry, has published...

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Multiomics leverages data from various omics methods—genomics, epigenomics, transcriptomics, proteomics, metabolomics, and so on—to provide a more comprehensive understanding of biological processes.

Genomics, driven by advancements in sequencing technology, enabled researchers to examine the entire genetic code rather than individual loci. Transcriptomics, employing similar next-generation sequencing (NGS) methods, extended this capability to RNA, providing additional insights into gene activity. Disciplines like proteomics and metabolomics delve into other biologically significant molecules. 

While individual omics methodologies have numerous applications, a single-omics technique will detect biomolecules of one type and thus can only capture changes in a small subset of the biological cascade. Therefore, single-omics cannot provide a systemic understanding of biological processes and their intricate interplay.

Why multiomics?

Understanding the big picture

Biological processes are highly dynamic, and their regulation and functionality involve many interactions between the genome, epigenome, transcriptome, proteome, and metabolome. Thus, to comprehensively understand a process of biological importance, it is critical not only to understand these biological layers as separate elements, but to dissect how they interact.

Instead of viewing biology through a single lens, multiomics encourages a holistic perspective. This involves capturing the complex interactions between genes, their expression, and the proteins that execute their instructions. By combining omic layers in a multiomic dataset, scientists can uncover interlayer mechanisms that would be difficult to observe by examining each type of data separately. 

This integration of biological information across diverse physiological levels positions multiomics as an indispensable tool in the fields of systems and mechanistic biology.

Linking genotype to phenotype

While each level of omics data has individual advantages, multiomics facilitates cross-referencing genetic variations with changes in gene expression and translation. This approach goes beyond identifying genetic changes; it involves mapping out a cascade of events to understand how a specific change affects the biological system. By doing so, researchers can discover functional relationships, understand regulatory mechanisms, and establish meaningful connections between genotype and phenotype.

There are already examples of how this approach has been applied in scientific research. In a study conducted in 2023, Ding and colleagues identified diagnostic biomarkers for two groups of rheumatoid arthritis patients. They achieved this by conducting metabolomic and transcriptomic profiling on patient samples and integrating this data with clinical information (phenome) and experimental rodent models mimicking arthritis. Beyond enhancing our comprehension of rheumatoid arthritis, the outcomes of this research can enhance diagnostic tools and enable personalized treatment strategies.

ProteomicsDB illustrates how researchers can leverage multiomics data to explore the relationships between genomics, transcriptomics, proteomics, and phenomics. The tool allows the real-time exploration and retrieval of protein abundance values across different tissues, cell lines, and body fluids via interactive expression heat maps and body maps. Moreover, users can upload their expression datasets and analyze them alongside with data stored in ProteomicsDB. This enables the analysis of complex biological systems, supports innovative research ventures, and facilitates the investigation of dose-dependent drug effects on multiple cell lines.<h3> Accelerating drug discovery and development

Through bioinformatics, artificial intelligence, and multiomics, scientists can create models of biological systems in healthy, diseased, and treated states. This has significant implications for drug discovery as it empowers the identification of novel drug targets, the prediction of toxicity, and the development of biomarkers for diagnostic applications. These findings can then be validated using vertebrate models (e.g., mice or zebrafish).

Through multiomics, researchers can also identify genetic and molecular changes that cause drug resistance. By comparing profiles of drug-sensitive and drug-resistant cells or organisms, they can detect mutations, altered gene expression patterns, or changes in protein post-translational modifications that lead to resistance. 

Additionally, multiomics can aid in categorizing patients into subgroups based on their molecular profiles, allowing for more precise targeting of therapies. This approach is particularly valuable in cancer treatment, where patient responses to drugs vary widely.

Adding proteins to the mix

Genomics and transcriptomics have long been favored in multiomics approaches due to their speed and accessibility in sequencing. However, scientists must bring proteins into the equation to truly comprehend biological function and phenotypic diversity. 

Yet, incorporating proteins into multiomics approaches comes with unique challenges. Unlike DNA and RNA, proteins are not as straightforward to study due to their complex structures and functions. To delve into the protein world, researchers traditionally rely on specialized techniques such as mass spectrometry or antibody-based assays. However, both methods have limitations

Genomics and transcriptomics scientists have embraced platforms like NGS, driven by the demand for scalable, low-cost tools offering comprehensive and unbiased characterization capabilities. The focus of NGS-based research now extends beyond genomics and transcriptomics.

The introduction of NGS technologies for protein sequencing gives proteomics a higher profile. By using NGS technologies, scientists can work with very low sample concentrations and still capture distinct protein expression patterns within individual cells—a crucial factor in understanding biological processes in health and disease. Moreover, advances in NGS technology for de novo protein sequencing close an existing gap to other technologies and enable the discovery of a vast amount of proteomic information.

A new direct protein sequencing benchtop instrument brings NGS to proteins with single-molecule resolution. The protein sequencer detects single amino acids and is highly sensitive to protein variants and post-translational modifications. The introduction of a low-cost benchtop solution with simplified sample preparation and analytical workflows makes protein sequencing more accessible for multiomics applications in both large genomics projects and small laboratories.

Although it is still early days for multiomics—and challenges like the heterogeneity across omics technologies and the difficulty of interpreting multilayered systems models still need to be addressed—this technology can uncover new biological understandings that would otherwise remain out of reach.