Seer
Hundreds of millions of genetic variations have been identified through large-scale genomic sequencing efforts and only around one percent have functional context. Proteomics data complements genomics data by providing the necessary annotation to achieve that context. The richness of proteomics includes protein variants, or proteoforms, which match or exceed genomics in potential scale and diversity and are driven by response to the environment, thus indicating physiological status. Despite this potential, there have been few tools to analyze proteins at large scale.
Seer’s combination of nanotechnology, protein chemistry, and machine learning platform – Proteograph – interrogates hundreds of thousands of protein variants at unprecedented, depth, scale and speed. Seer’s panels of engineered nanoparticles selectively and reproducibly interrogate proteins in an unbiased way, obviating the need to compromise between depth of information and size of study. With the Proteograph, researchers can now conduct large proteomic studies that will achieve breakthroughs such as novel biomarkers for early stage disease, as shown in Seer’s recent Nature Communications paper (Blume et al., published online July 22, 2020) which showed how the platform can classify patient samples of early stage NSCLC from healthy controls.