Multiomics: A 360° View into Biology and Medicine

In the ever-evolving field of healthcare, the way we understand and treat disease is undergoing a seismic shift. Traditionally, research in biology and medicine has approached complex health challenges from a single layer, focusing either on genetics, proteomics, metabolomics or observable phenotypes. But this siloed view is no longer sufficient. 

Biology doesn’t operate in isolation; a single pathway or molecule does not dictate the majority of phenotypes. They emerge from a complex interplay of genetic, proteomic, transcriptomic and environmental factors. Only by integrating these layers – through a multiomics approach – can we begin to see the full picture.

Why multiomics matters

Multiomics refers to the integration of various “omics” data types – genomics, transcriptomics, proteomics, metabolomics and more – to provide a holistic understanding of biological systems. By analyzing data from different biological levels simultaneously, researchers gain a 360° view of cellular behavior, disease progression and drug response.

This comprehensive perspective is vital because often the mechanism behind a treatment’s effectiveness is not fully understood. A therapy may appear successful in rescuing a phenotype, but without insight into the molecular cascade it triggers, we cannot truly improve, personalize or optimize it. Multiomics allows researchers to identify mechanisms of action, discover biomarkers for early disease detection and develop more targeted interventions.

The industry shift toward integration

Thanks to technological advances and decreasing costs, the healthcare industry is quickly pivoting toward multiomics. The first human genome project was a billion-dollar endeavor; today, sequencing the genome takes  a matter of a hours and costs less than a thousand dollars. This affordability has made high-resolution, multiplexed experiments, such as single-cell sequencing and spatial transcriptomics, not only possible but scalable.

Single-cell resolution used to be unscalable due to input and cost limitations. Now, it's driving state-of-the-art research across pharma and biotech. We’re seeing a clear shift from single-omics studies to multiomics investigations in drug discovery and disease modeling to drive actionable insights.

Real-world examples

Leveraging multiomics to advance lung cancer research at the University of Michigan

At the forefront of lung cancer research, Dr. Mukesh Nyati and his team at the University of Michigan are pushing the boundaries of molecular analysis through the integration of multiomics. Their research aims to uncover how depletion of the epidermal growth factor receptor (EGFR) affects lung cancer models, an area critical for understanding tumor progression and therapeutic response.

The collaboration between Signios Bioscuebces and Dr. Nyati began with single-cell transcriptomics, which enabled the team to capture a high-resolution view of the tumor microenvironment. This approach provided detailed insights into shifts in immune cell populations and gene expression patterns in response to EGFR depletion.

Recognizing the importance of spatial context in understanding tumor biology, the team recently expanded their study to include spatial transcriptomics. By mapping gene expression data back to its location within the tissue, this technique added a fundamental layer of spatial information, revealing how molecular changes are organized within the tumor architecture.

This multiomic approach, merging single-cell and spatial transcriptomics, is enabling Dr. Nyati’s team to build a more comprehensive picture of how lung tumors respond to molecular interventions. Their work exemplifies how integrated multiomics can drive deeper biological understanding and potentially guide the development of more effective, targeted cancer therapies.

Advancing traumatic brain injury research at Virginia Tech

Another major project that is incorporating multiomics approaches involves Dr. Michelle Theus from Virginia Tech. Dr. Theus and her team are exploring traumatic brain injury (TBI) using different mouse models. Starting with bulk RNA sequencing, the project evolved to single-cell and spatial transcriptomics to understand how different cell types react to injury and whether certain genetic modifications can promote recovery.

Spatial transcriptomics is especially powerful here, as it allowed the researchers to study the brain injury in situ – offering insights into how cells near the trauma site change, how they interact and whether neuroregeneration is occurring.

The bottleneck: From data to insight

As multiomics generates increasingly large and complex datasets, the challenge shifts from collection to interpretation. This is where many companies are outsourcing critical expertise in this area.

There’s often a gap between the data and the question. Many researchers and scientists may not have the vocabulary or the tools to decode what the data is telling them. That’s where technology in bioinformatics and machine learning models are coming in, bridging the gap by turning raw data into biological understanding.

Looking ahead

With a distinctive blend of technological innovation and scientific rigor, multiomics is propelling companies into a new era of integrative biology. Whether collaborating with universities or biopharma partners, their mission remains consistent: to drive discovery through data. In a world where answers lie buried in biological complexity, these companies are beginning to uncover them, one layer at a time.