SomaSCAN® Proteomics
What could you discover with the world’s most impactful protein analysis platform?
join our customers
Alkahest
Bristol-Myers Squibb
Louisiana State University
Novartis
Henry Ford Health System
Leeds Centre for Personalised Medicine and Health
Stanford
NEC
Otsuka
…and more
2 ways to engage, 1 powerful platform
Biomarker discovery and clinical insights from a single sample
SomaSignalTM Tests
18 clinically validated metrics derived from proteomic data on just 55 µl blood
Why proteomics?
SAME GENOTYPE. DIFFERENT PHENOTYPE.
PROTEIN ASSAYS COMPLEMENT GENOMICS TO IDENTIFY:
- Patient subpopulations
- Novel therapeutic targets
- New disease applications for approved drugs
- Possible safety concerns
- Mechanisms of action
key disease areas
CVD
Circulating proteins are powerful indicators of cardiovascular disease. Explore >25 CVD publications featuring SomaScan data.
ONCOLOGY
Identify and characterize cancers and predict response to immunotherapy by measuring circulating proteins.
NASH & NAFLD
The SomaScan Assay can be used to detect biomarkers associated with nonalcoholic steatohepatitis and nonalcoholic fatty liver disease.
infectious disease
Proteomics can shed light on the pathology of infectious disease, discover predictors of disease progression, or demonstrate treatment response.
TECHNOLOGY
The SomaScan platform is optimized for clinical proteomics
Largest Menu
The largest commercial proteomic assay on the market, providing over 7,000 protein measurements.
680 samples/day
Our workflow is massively multiplexed with considerable controls to yield fast, accurate data.
LOWEST Coefficient
of variation
With average CVs of ~5%, SomaScan reagents provide reproducible results for patient samples and healthy controls.
10 log dynamic range
Our unique approach detects very rare proteins and highly abundant proteins from the same sample simultaneously.
featured publications
Comprehensive
liquid health check
Protein signatures from 17,000 samples were compared with traditional health indicators to generate 13 predictive models.
linking genetics
to disease
Circulating proteins were used to identify 27 network modules associated with CVD, metabolic diseases, and overall survival.
building a
proteomic atlas
profiling aging-related disease
Waves of proteomic changes across lifespan reflect distinct pathways, particularly in the seventh and eighth decades of life.
