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

Proc Natl Acad Sci U S A. 2022 Mar 15;119(11):e2106053119. doi: 10.1073/pnas.2106053119. Epub 2022 Mar 11.

Abstract

SignificanceDeep 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.

Keywords: machine learning; mass spectrometry; nanoparticle; nano–bio interaction; proteomics.

MeSH terms

  • Blood Proteins* / chemistry
  • Deep Learning*
  • Nanoparticles* / chemistry
  • Protein Corona / chemistry
  • Proteome
  • Proteomics* / methods

Substances

  • Blood Proteins
  • Protein Corona
  • Proteome