June 11, 2025
Multi-omic technologies have expanded rapidly over the past two decades. Researchers now have access to whole genome sequencing, spatial transcriptomics, proteomics, metabolomics, and more—all capable of capturing different aspects of disease biology. These tools have led to important discoveries, but they’ve also introduced new challenges: data fragmentation, inconsistent interpretation, and findings that fail to translate into meaningful clinical outcomes.
The problem isn’t lack of data. It’s that the data often stops short of clinical relevance. Research has progressed from basic sequencing to siloed advanced technologies, to multi-omics, to integrated multi-omics—and now to Clinical OMICS.
At the McDonnell Genome Institute, we refer to our approach as Clinical OMICS—a deliberate shift from exploratory multi-omics to a framework designed to support clinical trials, therapeutic development, and patient-level insight. It’s not simply an extension of integrated multi-omics. Clinical OMICS connects molecular data to disease mechanisms, treatment response, and clinical endpoints—and is built to produce results that hold up in validation.
We’ve spent years developing the infrastructure to support this model—organizing, interpreting, and applying complex datasets to move beyond discovery toward clinical impact.
What Makes Clinical OMICS Work
Combining data types is no longer enough. Multi-omic studies that rely on correlation alone often produce signals that fail to replicate—leading to missed targets and stalled clinical programs. Clinical OMICS is our effort to close that gap.
At the McDonnell Genome Institute, we’ve built the infrastructure to support Clinical OMICS at scale. Our approach starts with well-defined biological questions and applies the appropriate technologies and analysis tools to interpret complex datasets in a way that aligns with clinical outcomes. It’s a model grounded in biology, shaped by clinical context, and structured to support study design and therapeutic development.
The core of this work is a coordinated set of services that includes:
- Whole genome sequencing, targeted approaches, and spatial transcriptomics
- Mass spectrometry for metabolites, lipids, and post-translational modifications
- SomaLogic-based proteomics for serum and CSF profiling
- iPSC generation and CRISPR-edited cell lines
- Bioinformatic analysis through the Center for Translational BioInformatics (CTBI), using mixed-method AI
These services are integrated—not siloed—to provide a clearer, more complete view of disease biology and therapeutic response.
Part of an Integrated Research Infrastructure
Clinical OMICS is possible because of the broader research infrastructure in place at WashU Medicine. MGI works in close coordination with the Center for Translational Bioinformatics (CTBI) and the WashU Medicine BioStore to support workflows that span from biospecimen storage to data generation to analysis.
This model reflects a new research paradigm—one that connects traditionally siloed capabilities into a coordinated infrastructure designed to move omics data toward clinical application.
CTBI brings together advanced analytical methods and mixed-method AI to help researchers interpret complex multi-omics datasets in the context of clinical outcomes. The BioStore extends this model by providing centralized access to well-annotated biospecimens, improving cohort definition and powering studies with strong phenotypic and clinical linkage.
Together, MGI, CTBI, and the BioStore form a translational ecosystem designed to support the development of clinically relevant biomarkers, mechanistic insights, and data that inform therapeutic strategy.
Want to discuss working with MGI, CTBI and/or the WashU Medicine BioStore about your next project? Contact us!
