Seer Proteograph Enables Unprecedented Genetic Marker Mapping for Proteogenomics Studies to Advance Drug and Biomarker Discovery

Study led by Weill Cornell Medicine demonstrates identification of protein altering variants for population-scale protein quantitative trait loci (pQTL) studies

Proteograph Product Suite enables scalable deep, unbiased proteomics by mass spectrometry and overcomes the limitations of affinity-based methods

REDWOOD CITY, Calif., Feb. 06, 2024 (GLOBE NEWSWIRE) -- Seer, Inc. (NASDAQ: SEER), a leading life sciences company commercializing a disruptive new platform for proteomics, today announced a publication in Nature Communications from a study led by Weill Cornell Medicine showing Seer’s Proteograph workflow to potentially unveil novel proteogenomic insights into genetics-based drug and biomarker discovery for precision medicines. Designed to address the challenges with affinity-based proteomic approaches for protein quantitative trait loci (pQTL) studies, the scalable, high-resolution Proteograph workflow enables scientists to link genetic variation with protein abundance with peptide level resolution.

The manuscript, “Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping,” was published in Nature Communications from the laboratory of Karsten Suhre, Ph.D., Professor of Biophysics and Physiology, Director of Bioinformatics & Virtual Metabolomics Core at Weill Cornell Medicine-Qatar and lead author of the article, along with Seer scientists.

In the paper, the researchers used Seer’s first-generation Proteograph Assay workflow upstream of mass spectrometry to quantify over 18,000 peptides from approximately 3,000 proteins in more than 320 blood samples to detect and quantify blood-circulating proteins in the presence of protein-altering variants (PAVs). The study found 184 PAVs in 137 genes, confirmed by their variant peptides in mass spectrometry data, known as MS-PAV. Most MS-PAVs were aligned with known genetic markers (cis-pQTLs), validating the target specificity of the method. Some MS-PAVs overlapped with trans-pQTLs, shedding light on potential causal proteins. Lastly, the study revealed proteins overlooked by traditional methods, like the incretin pro-peptide (GIP) linked to type 2 diabetes and cardiovascular disease.