12 March 2020
US HUPO: Deep, Unbiased Plasma Protein Profiling and Protein-Protein Interaction vs Fractionation
US HUPO 16th Annual Conference – April 2020
Patrick A. Everley¹; Shadi Ferdosi¹; Daniel Hornburg¹; William C. Manning¹; Asim Siddiqui¹; Greg Troiano¹; Omid C. Farokhzad¹; Matthew E. K. Chang²; Mark R. Flory²; John E. Blume¹*
¹Seer, Inc., Redwood City, CA – *email@example.com ²OHSU – Knight Cancer Institute- Cancer Early Detection Advanced Research Center (CEDAR), Portland, OR
The proteomic complexity of biological samples limits the scale and speed of robust biomarker discovery studies. Complex workflows for deep profiling of the plasma proteome have limited study size and compromised validation and replication potential, while necessitating trade-offs among depth and breadth of coverage, efficiency, precision, and accuracy.
Herein we describe Proteograph, an efficient, automated multinanoparticle (NP) platform for deep and unbiased proteomic profiling. NPs reproducibly form protein coronas driven by their engineered biophysicochemical properties, and those protein signatures provide insights into protein-protein interaction (PPI)in a biological sample. Specific and reproducible protein-NP binding is the product of binding affinities and a sample’s protein concentrations. The result is compression of concentration range that improves detection of low-abundance proteins without loss of linearity (demonstrated by spike-recovery experiments) or measurement precision. As proof-of-concept, we used our panel of 10 specifically engineered superparamagnetic NPs to identify >2,000 protein groups (1% protein and peptide FDR, 84% with 2 or more peptides) from 16 individual plasma samples.
Proteograph allows parallel processing of 80 NP-sample combinations, from sample to purified peptides, in less than 8 hours. We also compared Proteograph to a typical workflow including high-pH reverse-phase fractionation, and found higher protein group detection (1,949 vs 658) with better precision (27% vs 41% CVs). In mapping the 1949 and 658 protein groups to a PPI map derived from the STRING database, we identified, respectively, 15/21 and 1/21 interaction clusters with >10 protein members where >10% of the members were covered.
Compared to typical fractionation, the Proteograph platform has superior coverage, depth, precision, and accuracy in unbiased proteomic profiling. Furthermore, this NP-based system has the potential to discover PPIs at a level not possible using conventional fractionation. Proteograph may enable the large-scale studies necessary for protein biomarker discovery and yield new biological insights into the human proteome.