Open AccessEngineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano–bio interactions
Author(s) -
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 D. Goldberg,
Robert Langer,
Mark R. Flory,
Ryan W. Benz,
Wei Tao,
Juan C. Cuevas,
Serafim Batzoglou,
John E. Blume,
Asim Siddiqui,
Daniel Hornburg,
Omid C. Farokhzad
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2106053119
Subject(s) - proteomics , workflow , proteome , nanoparticle , computer science , profiling (computer programming) , nanotechnology , computational biology , chemistry , bioinformatics , materials science , biology , biochemistry , database , gene , operating system
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.