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Automation and low-cost proteomics for characterization of the protein corona: experimental methods for big data

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Abstract

Nanoparticles used in biological settings are exposed to proteins that adsorb on the surface forming a protein corona. These adsorbed proteins dictate the subsequent cellular response. A major challenge has been predicting what proteins will adsorb on a given nanoparticle surface. Instead, each new nanoparticle and nanoparticle modification must be tested experimentally to determine what proteins adsorb on the surface. We propose that any future predictive ability will depend on large datasets of protein-nanoparticle interactions. As a first step towards this goal, we have developed an automated workflow using a liquid handling robot to form and isolate protein coronas. As this workflow depends on magnetic separation steps, we test the ability to embed magnetic nanoparticles within a protein nanoparticle. These experiments demonstrate that magnetic separation could be used for any type of nanoparticle in which a magnetic core can be embedded. Higher-throughput corona characterization will also require lower-cost approaches to proteomics. We report a comparison of fast, low-cost, and standard, slower, higher-cost liquid chromatography coupled with mass spectrometry to identify the protein corona. These methods will provide a step forward in the acquisition of the large datasets necessary to predict nanoparticle-protein interactions.

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Acknowledgments

The authors thank Dhanya T. Jayaram and Gustavo Sosa Macias for their assistance with the experiments. The protein NP TEM work was performed at the Georgia Tech Institute for Electronics and Nanotechnology, a member of the National Nanotechnology Coordinated Infrastructure, which is supported by the National Science Foundation (ECCS-1542174). We thank the Duke University School of Medicine for the use of the Proteomics and Metabolomics Shared Resource, which provided proteomics service, with special thanks to Erik Soderblom for the discussion and technical advice. Thomas Pho was supported by a fellowship from the American Chemical Society Bridge Program.

Funding

This study received funding from the NSF (CBET-1901579) and NIH (NIAID 2R01AI101047-06A1).

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Authors and Affiliations

  1. Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, 27708, USA

    Karsten M. Poulsen & Christine K. Payne

  2. School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Thomas Pho & Julie A. Champion

  3. Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Thomas Pho & Julie A. Champion

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  1. Karsten M. Poulsen
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  2. Thomas Pho
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Correspondence to Julie A. Champion or Christine K. Payne.

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Poulsen, K.M., Pho, T., Champion, J.A. et al. Automation and low-cost proteomics for characterization of the protein corona: experimental methods for big data. Anal Bioanal Chem 412, 6543–6551 (2020). https://doi.org/10.1007/s00216-020-02726-1

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