The Omics Revolution: Multiomics and the Future of Biomedical Research
Multiomics is advancing our understanding of cell biology and disease, paving the way for new diagnostics and targeted therapies.
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Traditional approaches to understanding biological systems have predominantly focused on studying individual molecular components, such as genes, proteins or metabolites, in isolation. However, this reductionist strategy fails to capture the complexity of how genetic variations affect RNA expression and protein function, and how environmental factors influence regulatory pathways and cellular metabolism.
“It’s extremely valuable to generate whole genome sequencing data, but it’s not telling us the current situation,” said Adil Mardinoglu, professor of systems biology at Kings College London, UK, and KTH-Royal Institute of Technology in Sweden.
Combining different types of omics data – such as genomics, epigenomics, transcriptomics, proteomics and metabolomics – offers tantalizing opportunities to gain a more holistic and comprehensive overview of cellular systems. Characterizing the microbiome through metagenomics to reveal host-microbiota interactions is also crucial – as disturbances to the oral, gut, vaginal and skin microbiota have been associated with a wide range of diseases.
“Multiomics is essentially taking different layers of omics and putting them together in a single analysis,” said Guillaume Pare, professor of pathology and molecular medicine at McMaster University in Canada.
A multidimensional perspective is essential to fully understand the interactions and influences of molecular components and environmental factors on biological processes and disease development. By connecting genotype to phenotype, multiomics is poised to fuel the discovery of new biomarkers and therapeutic targets, advancing precision medicine.
Dissecting complexity
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Single omics technologies encompass high-throughput assays that comprehensively and simultaneously measure molecules of the same type from a biological sample. Rapid advances in technologies, such as next-generation sequencing and mass spectrometry, have expanded researchers' capabilities to study whole genomes, transcriptomes, epigenomes, proteomes, metabolomes, metagenomes and beyond.
“In recent years, our capability to perform high-throughput biology and experiment has increased exponentially,” said Pare. “These tools just keep getting significantly cheaper and better, allowing us to consider research that would have been unthinkable just a few years ago.”
But while single-level omics can offer new insight into molecular alterations at the DNA, RNA, protein or metabolic level, they only provide a narrow view of cellular functions. They fail to uncover the causal connections between molecular signatures and disease development and progression.
“Biological systems are complex and driven by interactions between different omics layers,” said Mardinoglu. “And this complexity is getting even more complicated, considering the effect of genetics, the diet, the microbiome, etc.”
Enter multiomics analysis, which is becoming increasingly accessible due to the increasing affordability of omics assays, enhanced computational power and improved bioinformatic tools. This powerful approach can reveal novel insights unattainable through single omics methods alone, revealing the interconnected networks that shape cell behavior (Figure 1) and impact human health and disease.
Figure 1: The “core” omics disciplines and how they are used to help understand complex biological systems. Credit: Technology Networks.
“I believe this type of experiment will become unavoidable at some point for all research,” predicts Pare. “Whether the research is driven by multiomics or it’s an add-on, it will become a requirement that people will want to see.”
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One of the ways that multiomics can offer a deeper insight into the causes of disease is the ability to connect genotype with phenotype. For example, Mendelian randomization is a powerful approach that enables researchers to integrate genomics and proteomics data to identify causal relationships between genetic variants and protein levels.
“This technique takes advantage of the random allocation of alleles during meiosis, essentially creating nature’s randomized controlled trial,” explained Pare. “We can now use it to examine each protein that we can measure in the blood and test for evidence of their involvement in specific diseases.”
Pare’s team recently demonstrated the effectiveness of this approach by identifying early and sensitive diagnostic biomarkers of chronic kidney disease in patients with type 2 diabetes.
“This framework has gained widespread popularity – and not just for proteomics, but also for transcriptomics, metabolomics and beyond,” enthused Pare.
Another exciting area is the emergence of single-cell multiomics technologies and methods – including spatial transcriptomics – which enable the deep characterization of cell states and activities in biological samples. These advanced techniques offer unique opportunities to explore the complexity of cellular systems, including tracing cell lineages, generating cell-type atlases of organs and dissecting disease mechanisms.
“This is really exciting,” said Pare. “We can determine the identity of cell populations in different tissues to a high degree of granularity and then link this with disease outcome.”
For example, single-cell multiomics technologies have revolutionized cancer research by allowing scientists to dissect tumor heterogeneity and identify rare subpopulations of cells crucial for tumor growth, metastasis and treatment resistance. Similarly, these tools are enabling the mapping of the brain’s inner circuitry in unprecedented detail.
Overcoming challenges
Despite the immense promise of multiomics, significant obstacles remain. A big challenge is eliminating false positives and negatives, which are common in multiomics datasets.
“High-throughput multiomics data presents challenges because we don’t fully understand the transition between different omics data,” said Mardinoglu. “Not every genetic mutation or variant will lead to changes in the protein or metabolite or even transcript levels.”
Integrating and interpreting vast datasets of different types is another significant hurdle. A lack of standardized experimental protocols, data formats and quality control measures also impedes the reproducibility and comparability of omics data generated across different studies.
“Data integration and interpretation require a multidisciplinary approach with collaboration between researchers from different fields,” said Mardinoglu. “It’s not something that one company or a small group of researchers can solve alone.”
International consortia and collaborative initiatives can help to encourage this cross-disciplinary collaboration by providing centralized resources, including databases, tools and protocols to support multiomics research worldwide.
For example, the Human Protein Atlas (HPA), aims to map all the human proteins in cells, tissues and organs by integrating various omics datasets. Initiated in 2003, the HPA has become the largest and most comprehensive database for the spatial distribution of proteins in human tissues and cells, offering an invaluable resource for exploring expression patterns at single cell resolution.
“It’s one of the most visited biological websites in the world,” stated Mardinoglu. “Over the years, we’ve incorporated additional information – transcriptomics, single-cell proteomics and single-cell transcriptomics data – and recently expanded to include the oral and gut microbiome.”
In the Pathology Atlas, researchers have employed a systems-level approach to analyze the human genome in relation to clinical outcomes based on genome‐wide mRNA and protein expression data from 17 types of cancer. This makes it possible to link the presence or absence of specific proteins in tumors to patient survival.
“We anticipate that we will be generating multiomics data for hundreds of different diseases – from both cross-sectional and longitudinal studies,” predicts Mardinoglu. “This will allow the characterization of rare and common diseases at the molecular level – and lead to the identification of very effective targets for drug development.”
In this context, the team has generated longitudinal multiomics data for healthy individuals and patients with various metabolic diseases in the US, Sweden and other regions.
The future of multiomics
Researchers are increasingly adopting multiomics techniques to gain deeper insights into the molecular changes contributing to normal development, cellular development and disease. Innovations in data analysis, such as sophisticated new bioinformatics tools and AI-based techniques, will aid in extracting meaningful insights from both new and existing datasets.
“What’s remarkable about multiomics is that we generate these huge datasets, rich in information, which become, in a sense, eternal,” said Pare. “While the initial investment is substantial, the long-term payoff is tremendous, as these data may be used in research for decades to come.”
The impact of multiomics is poised to be profound, revolutionizing our understanding of the biological mechanisms underlying different diseases. Ultimately, it could be employed in clinical settings to individualize the prevention, diagnosis and treatment of health conditions informed by molecular changes occurring within a person’s body in real-time – ushering in a new era of precision medicine.
About the interviewees:
Adil Mardinoglu is a professor of systems biology at Kings College London, UK, and KTH-Royal Institute of Technology in Sweden. He leads a team of 25 researchers working in the area of computational biology, experimental biology and drug development.
Guillaume Pare is a professor of pathology and molecular medicine at McMaster University in Canada. His research combines high-throughput biomarker screens with genetics, bioinformation and epidemiology to identify novel disease biomarkers.
