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Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona

Author

Listed:
  • John E. Blume

    (Seer, Inc.)

  • William C. Manning

    (Seer, Inc.)

  • Gregory Troiano

    (Seer, Inc.)

  • Daniel Hornburg

    (Seer, Inc.)

  • Michael Figa

    (Seer, Inc.)

  • Lyndal Hesterberg

    (Seer, Inc.)

  • Theodore L. Platt

    (Seer, Inc.)

  • Xiaoyan Zhao

    (Seer, Inc.)

  • Rea A. Cuaresma

    (Seer, Inc.)

  • Patrick A. Everley

    (Seer, Inc.)

  • Marwin Ko

    (Seer, Inc.)

  • Hope Liou

    (Seer, Inc.)

  • Max Mahoney

    (Seer, Inc.)

  • Shadi Ferdosi

    (Seer, Inc.)

  • Eltaher M. Elgierari

    (Seer, Inc.)

  • Craig Stolarczyk

    (Seer, Inc.)

  • Behzad Tangeysh

    (Seer, Inc.)

  • Hongwei Xia

    (Seer, Inc.)

  • Ryan Benz

    (Seer, Inc.)

  • Asim Siddiqui

    (Seer, Inc.)

  • Steven A. Carr

    (Broad Institute of MIT and Harvard)

  • Philip Ma

    (Seer, Inc.)

  • Robert Langer

    (Massachusetts Institute of Technology)

  • Vivek Farias

    (Sloan School and Operations Research Center, Massachusetts Institute of Technology)

  • Omid C. Farokhzad

    (Seer, Inc.
    Harvard Medical School)

Abstract

Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liquid chromatography-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochemical properties of engineered NPs translates to distinct protein corona patterns enabling differential and reproducible interrogation of biological samples, including deep sampling of the plasma proteome. Spike experiments confirm a linear signal response. The median coefficient of variation was 22%. We screened 43 NPs and selected a panel of 5, which detect more than 2,000 proteins from 141 plasma samples using a 96-well automated workflow in a pilot non-small cell lung cancer classification study. Our streamlined workflow combines depth of coverage and throughput with precise quantification based on unique interactions between proteins and NPs engineered for deep and scalable quantitative proteomic studies.

Suggested Citation

  • John E. Blume & William C. Manning & Gregory Troiano & Daniel Hornburg & Michael Figa & Lyndal Hesterberg & Theodore L. Platt & Xiaoyan Zhao & Rea A. Cuaresma & Patrick A. Everley & Marwin Ko & Hope L, 2020. "Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17033-7
    DOI: 10.1038/s41467-020-17033-7
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    Cited by:

    1. Hassan Gharibi & Ali Akbar Ashkarran & Maryam Jafari & Elizabeth Voke & Markita P. Landry & Amir Ata Saei & Morteza Mahmoudi, 2024. "A uniform data processing pipeline enables harmonized nanoparticle protein corona analysis across proteomics core facilities," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

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