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

Journal: Proceedings of the National Academy of Sciences of the United States of America
Published Date:

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.

Authors

  • Shadi Ferdosi
    Seer, Inc., Redwood City, CA 94065.
  • Behzad Tangeysh
    Seer, Inc., Redwood City, CA 94065.
  • Tristan R Brown
    Seer, Inc., Redwood City, CA 94065.
  • Patrick A Everley
    Seer, Inc., Redwood City, CA 94065.
  • Michael Figa
    Seer, Inc., Redwood City, CA 94065.
  • Matthew McLean
    Seer, Inc., Redwood City, CA 94065.
  • Eltaher M Elgierari
    Seer, Inc., Redwood City, CA 94065.
  • Xiaoyan Zhao
    Seer, Inc., Redwood City, CA 94065.
  • Veder J Garcia
    Seer, Inc., Redwood City, CA 94065.
  • Tianyu Wang
    State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University; University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University.
  • Matthew E K Chang
    CEDAR Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239-3098.
  • Kateryna Riedesel
    Seer, Inc., Redwood City, CA 94065.
  • Jessica Chu
    Seer, Inc., Redwood City, CA 94065.
  • Max Mahoney
    Seer, Inc., Redwood City, CA 94065.
  • Hongwei Xia
    Seer, Inc., Redwood City, CA 94065.
  • Evan S O'Brien
    Seer, Inc., Redwood City, CA 94065.
  • Craig Stolarczyk
    Seer, Inc., Redwood City, CA 94065.
  • Damian Harris
    Seer, Inc., Redwood City, CA 94065.
  • Theodore L Platt
    Seer, Inc., Redwood City, CA 94065.
  • Philip Ma
    Seer, Inc., Redwood City, CA 94065.
  • Martin Goldberg
    Seer, Inc., Redwood City, CA 94065.
  • Robert Langer
    Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
  • Mark R Flory
    CEDAR Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239-3098.
  • Ryan Benz
    Seer, Inc., Redwood City, CA 94065.
  • Wei Tao
    Center for Nanomedicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115.
  • Juan Cruz Cuevas
    Seer, Inc., Redwood City, CA 94065.
  • Serafim Batzoglou
    Department of Computer Science, Stanford University, Stanford, CA, USA.
  • John E Blume
    Seer, Inc., Redwood City, CA 94065.
  • Asim Siddiqui
    Seer, Inc., Redwood City, CA 94065.
  • Daniel Hornburg
    Seer, Inc., Redwood City, CA 94065.
  • Omid C Farokhzad
    Seer, Inc., Redwood City, CA 94065.