Ferdosi, S. | bioRxiv Science

bioRxiv2023

Protein Coronas on Functionalized Nanoparticles Enable Quantitative and Precise Large-Scale Deep Plasma Proteomics

BackgroundThe wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics. MethodsInvestigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments. ResultsWe show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts. ConclusionsThe automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.

biochemistry↗

bioRxiv2022

Enhanced competitive protein exchange at the nano-bio interface enables ultra-deep coverage of the human plasma proteome

We have developed a scalable system that leverages protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Introducing proprietary engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. Here we demonstrate the importance of tuning the protein to NP-surface ratio (P/NP), which determines the competition between proteins for binding. We demonstrate how optimized P/NP ratio affects protein corona composition, ultimately enhancing performance of a fully automated NP-based deep proteomic workflow (Proteograph). By limiting the available binding surface of NPs and increasing the binding competition, we identify 1.2 - 1.7x more proteins with only 1% false discovery rate on the surface of each NP, and up to 3x compared to a standard neat plasma proteomics workflow. Moreover, increased competition means proteins are more consistently identified and quantified across replicates, yielding precise quantification and improved coverage of the plasma proteome when using multiple physicochemically distinct NPs. In summary, by optimizing NPs and assay conditions, we capture a larger and more diverse set of proteins, enabling deep proteomic studies at scale.

biophysics↗