Molecular Biology ⎯ A Commonality between Various Omes in Multi-omics Approaches

Learn about how molecular biology is fundamental to multi-omics and supports the rising interest in this approach and its varied applications. In this article, we discuss:

The multi-omics approach to biological studies, which leverages data from various omics methods—genomics, transcriptomics, proteomics, epigenomics, and metabolomics—provides complimentary read-outs, resulting in deeper insights. When analyzed together, the diverse data sets provide a narrower set of results and potential biomarker signatures that would not have been found from a single omics data set alone. 

Recently, this multi-omics approach has been increasingly utilized for applications such as understanding basic cellular biology and diseases and identifying and verifying drug targets. A common thread in numerous omics methods is molecular biology.

There are various terms for multi-omics, including pan-omics, integrative omics, trans-omics, and vertical omics. The types of ‘omes’ analyzed for multi-omics studies include genomes, proteomes, transcriptomes, epigenomes, and metabolomes (Table 1).

These studies often probe through the layers of the central dogma of molecular biology from core code (i.e., DNA) to RNA and protein with the addition of metabolites. Therefore, multi-omics studies provide the most comprehensive picture of the current state and processes occurring in the body or even a single cell.

Combining the outputs of omics like genomics, transcriptomics, and proteomics has provided a way to study the biological “big picture,” but delivers massive amounts of data that is often difficult to decipher and draw definitive conclusions from. Within one data set, correlations are generally made instead of conclusions because it is difficult to establish causality from single omics data sets. Single omics results show a piece of the full biological picture without shining a light on the upstream causes or the downstream implications of those states.

Combining multiple omics data sets can provide a more holistic picture, allowing us to draw more specific conclusions from the data generated and uncover causative changes, revealing disease etiology and potential treatment targets. By taking this multi-layered approach—comparing datasets and finding overlapping results—multi-omics strategies have informed us in ways that single-omic approaches alone cannot. Fields as broad as oncology, Alzheimer's disease research, drug discovery, cellular biology, and infectious disease research have benefited from a multi-omics approach.

Due to the clear benefits of analyzing multiple omics data sets rather than single omics, multi-omics approaches are gaining significant traction. Over the past 11 years, the number of multi-omic-related publications on PubMed rose from 7 to 2,195, representing a 69% compound annual growth rate (CAGR). Therefore, these approaches will continue to become more prevalent in research studies, and molecular biology remains foundational to the success of multi-omics.

Multi-omics approaches to understanding biological processes are trending because of recent accessibility. The fundamental methods, especially molecular biology and next-generation sequencing (NGS) are increasingly becoming more approachable, common, and affordable. Additionally, analysis tools to help interpret and make sense of these huge relational data sets are also improving and becoming increasingly accessible. Data sharing is playing a huge role, with research groups making results available to the public so others can analyze it as well, allowing any lab to access omics data that would not have been available to them otherwise.

An intriguing aspect of multi-omics is how it’s being used to understand cellular biology, disease, and drug discovery at new levels. It is being used in numerous areas of research, leading to novel discoveries that otherwise would not have been found from a single omics data set.

Fields with novel findings stemming from the use of multi-omics approaches include the following:

• Combining proteomic, genomic, and transcriptomic data uncovered genes that are significant contributors to colon and rectal cancer. [1]
• Leveraging genomics, proteomics, and transcriptomics data revealed a potential therapeutic target for colon cancer. [2]

• Using transcriptomic, epigenomics, then genomic data, researchers were able to propose distinct differences between genetic predisposition and environmental contributions to Alzheimer’s disease. [3]

•  In drug discovery, defining the mechanism of action is a crucial step. Multi-omics has helped in the identification and verification of drug targets. [4]

• Our understanding of basic cellular biology has increased due to multi-omics—an entirely new cell type was discovered through the technique REAP-seq through which expression of RNA and proteins can be measured on a single-cell level. [5]

• Transcriptomics, proteomics, and antigen receptor analyses were combined to reveal insights into the immune response to COVID-19 infection and potential therapeutic targets. [6]

The multiple layers of multi-omics technologies are centered around nucleic acid detection and quantification. Genomics, epigenomics, and transcriptomics are all nucleic acid-based methods, probing DNA and RNA. Therefore, molecular biology techniques and tools are foundational to multi-omics.

NGS is commonly used and offers discovery power since it is a hypothesis-free technique of detection and quantification. PCR and qPCR are fundamental for genomics, epigenomics, and transcriptomics. These methods require basic PCR and qPCR for various steps and are often used in tandem with RT-PCR. PCR, qPCR, and RT-PCR also provide viable alternatives to NGS as they are more accessible and affordable. Even NGS methods rely on PCR for library preparation.

Genomics at its core is DNA analysis detection and quantification through PCR and qPCR. Therefore, it requires the use of molecular biology tools such as DNA polymerases, dNTPs, master mixes, oligonucleotide primers, and more.

Epigenomics techniques, which involve DNA sequencing, require PCR and qPCR enzymes that are either methylation-sensitive or insensitive. These methods also require controls and standards which are created through the methylation of nucleic acids, so DNA polymerases and modifying enzymes capable of methylation and demethylation (methyltransferase and epigenetic products) are necessary.

Transcriptomics employs RT-PCR and qPCR for gene expression analysis and therefore requires reverse transcriptases for reverse transcription, followed by DNA polymerase and other PCR reagents.

Some multi-omic analysis techniques use non-nucleic acids-based methods and tools. For instance, proteomics employs protein-specific methods, such as mass spectrometry, western blotting, and ELISA. While metabolomics utilizes methods such as mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, and HPLC to analyze small molecules.

It is clear that a multi-omics strategy for understanding complex biological processes is increasingly being adopted and will likely continue to be a favored approach, given the promise these omics techniques show when combined. They have allowed for discoveries in basic research and applied research spanning fields as diverse as oncology, infectious diseases, neurology, cell biology, and drug discovery and have provided insight that would have otherwise remained elusive. Moreover, work in this space depends on fundamental molecular biology skills, techniques, and tools. PCR, DNA modification, and electrophoresis all play roles at various steps in the multi-omics techniques genomics, epigenomics, and transcriptomics. 

1. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics Data Integration, Interpretation, and Its Application. Bioinform Biol Insights. 2020;14:1177932219899051.doi:10.1177/1177932219899051 
2. Menyhárt O, Győrffy B. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput Struct Biotechnol J. 2021;19:949-960. doi:10.1016/j.csbj.2021.01.009 
3. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biology. 2017;18(1). doi:10.1186/s13059-017-1215-1 
4. Application of omics- and multi-omics-based techniques for natural product target discovery. Biomed Pharmacother. 2021;141:111833. doi:10.1016/j.biopha.2021.111833 
5. Hu Y, An Q, Sheu K, Trejo B, Fan S, Guo Y. Single Cell Multi-Omics Technology: Methodology and Application. Frontiers in Cell and Developmental Biology. 2018;6. doi:10.3389/fcell.2018.00028 
6. Stephenson E, Reynolds G, Botting RA, et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat Med. 2021;27(5):904-916. doi:10.1038/s41591-021-01329-2