Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano–bio interactions

Autor: Shadi Ferdosi, Behzad Tangeysh, Tristan R. Brown, Patrick A. Everley, Michael Figa, Matthew McLean, Eltaher M. Elgierari, Xiaoyan Zhao, Veder J. Garcia, Tianyu Wang, Matthew E. K. Chang, Kateryna Riedesel, Jessica Chu, Max Mahoney, Hongwei Xia, Evan S. O’Brien, Craig Stolarczyk, Damian Harris, Theodore L. Platt, Philip Ma, Martin Goldberg, Robert Langer, Mark R. Flory, Ryan Benz, Wei Tao, Juan Cruz Cuevas, Serafim Batzoglou, John E. Blume, Asim Siddiqui, Daniel Hornburg, Omid C. Farokhzad
Rok vydání: 2022
Předmět:
Zdroj: Proceedings of the National Academy of Sciences. 119
ISSN: 1091-6490
0027-8424
DOI: 10.1073/pnas.2106053119
Popis: Significance Deep profiling of the plasma proteome at scale has been a challenge for traditional approaches. We achieve superior performance across the dimensions of precision, depth, and throughput using a panel of surface-functionalized superparamagnetic nanoparticles in comparison to conventional workflows for deep proteomics interrogation. Our automated workflow leverages competitive nanoparticle–protein binding equilibria that quantitatively compress the large dynamic range of proteomes to an accessible scale. Using machine learning, we dissect the contribution of individual physicochemical properties of nanoparticles to the composition of protein coronas. Our results suggest that nanoparticle functionalization can be tailored to protein sets. This work demonstrates the feasibility of deep, precise, unbiased plasma proteomics at a scale compatible with large-scale genomics enabling multiomic studies.
Databáze: OpenAIRE
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