Last Updated
22 June 2020

AACR: Analytical validation of the multi-nanoparticle Proteograph platform for rapid and deep proteomic profiling

AACR Virtual Meeting II – American Association for Cancer Research –  June 2020


John E Blume1, William C Manning1, Gregory Troiano1, Asim Siddiqui1, Philip Ma1, Robert Langer2, Vivek Farias2, Omid C. Farokhzad1

1Seer, Redwood City, CA, United States
2Massachusetts Institute of Technology, Boston, MA, United States


Introduction: Plasma proteins should be useful biomarkers for disease detection, yet few proteins (~120) are FDA approved. Productive biomarker discovery studies are resource-limited due to the complex biochemical fractionation methods used to address the inherent challenges in unbiased plasma profiling such as the large dynamic range. Herein, we describe Proteograph, a novel platform that leverages the nano-bio interactions of nanoparticles (NPs) for deep and unbiased proteomic sampling. NPs reproducibly bind subsets of proteins from biofluids as a function of protein concentration, protein-NP affinity, and protein-protein interactions to form a corona on the NP surface. The corona composition is directly a function of the NPs’ biophysicochemical properties and requires no prior knowledge of the proteins that might be selected by each distinctly engineered NP. With an optimized panel of 10 NPs, we can broadly and deeply interrogate the plasma proteome and rapidly quantify potential biomarkers. The highly parallel NP workflow makes large studies practicable and should improve biomarker discovery and validation.

Methods and Results: We have screened 200+ NPs with different biophysicochemical properties and selected a panel of 10 based on protein detection. A fully automated assay workflow was developed that can process the 10 NP panel across 8 plasma samples in a 7 hr assay. Evaluation of this panel across 16 individual plasma samples detected 2,009 protein groups (1% protein FDR, 84% with 2 or more peptides). Accuracy was demonstrated with spike-recovery experiments using 4 NPs in which CRP, Angiogenin and S100a8/9 were added to plasma at 2X, 5X, 10X, and 100X of measured endogenous levels. Linear model fits for NP corona MS signal vs. ELISA were created with mean slopes of 1.06 ± 0.22 and mean adjusted-rof 0.95 ± 0.05. Precision was demonstrated across 3 NPs using three assay replicates in which the mean of the median CVs for each NP is 24%. The depth of plasma proteome coverage for the 10 NP panel using a pooled plasma sample was determined by comparison of the NP-detected proteins to published MS intensities and spanned nearly the entire reported range. Examining protein annotations (e.g., GO Cellular Compartment and Biological Process, KEGG and Pfam) within each NP corona reveals correlations by 1D-enrichment analysis between protein annotations and NP biophysicochemical properties suggesting specific relationships at the nano-bio surface.

Discussion: We have demonstrated the selection and optimization of a panel of 10 NPs for plasma proteome profiling. We have also demonstrated the breadth and depth of this panel’s ability to accurately and precisely quantify proteins from plasma. We believe the robustness and scalability of the Proteograph platform could enable population-scale deep and unbiased proteomics analysis previously not feasible using existing workflows.


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