discovery alliances

Alkahest

Bristol-Myers Squibb

Louisiana State University

Henry Ford Health System

Cambridge University

Stanford

…and more

Program overview

how it works

The SomaScan Discovery Alliance was developed to accelerate health research.

Proteomics + clinical data

You provide a few key clinical data elements, we provide 5,000 highly reproducible measurements for each sample

Supporting Your Research

The SomaScan Platform provides, by far, the most comprehensive picture of the human proteome available to date

Furthering our mission

To deliver health and wellness information that ensures future patient access to emerging therapies

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.

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NASH & NAFLD

The SomaScan Assay can be used to detect biomarkers associated with nonalcoholic steatohepatitis and nonalcoholic fatty liver disease. 

DIABETES

Models built from SomaScan data have been used to predict progression from pre-diabetes to diabetes.

TECHNOLOGY

The SomaScan platform is optimized for clinical proteomics​

Largest Menu

The largest commercial proteomic assay on the market, providing over 5,000 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.

8 log dynamic range

Our unique approach detects very rare proteins and highly abundant proteins from the same sample simultaneously.

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

Quantitative trait loci (QTLs) were compared with protein QTLs to identify links and highlight biomarkers with causal roles.

profiling
aging-related disease

Waves of proteomic changes across lifespan reflect distinct pathways, particularly in the seventh and eighth decade of life.

somascan technology: a breakthrough in proteomics