Enhanced Competition at the Nano–Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes

Shadi Ferdosi; Alexey Stukalov; Moaraj HasanBehzad TangeyshTristan R. BrownTianyu WangEltaher M. ElgierariXiaoyan ZhaoYingxiang HuangAmir AlaviBrittany Lee-McMullenJessica ChuMike FigaWei TaoJian WangMartin GoldbergEvan S. O’BrienHongwei XiaCraig StolarczykRalph WeisslederVivek FariasSerafim BatzoglouAsim SiddiquiOmid C. FarokhzadDaniel Hornburg

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Enhanced Competition at the Nano–Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes

机译：Enhanced Competition at the Nano–Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes

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Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle–protein interface, driven by the relationship between protein– NP affinity and protein abundance. This enables scalable systems that leverage protein–nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NPsurface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2–1.7× more proteins with 1 false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.

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《Advanced Materials》 |2022年第44期|2206008.1-2206008.11|共11页
作者
Shadi Ferdosi; Alexey Stukalov; Moaraj HasanBehzad TangeyshTristan R. BrownTianyu WangEltaher M. ElgierariXiaoyan ZhaoYingxiang HuangAmir AlaviBrittany Lee-McMullenJessica ChuMike FigaWei TaoJian WangMartin GoldbergEvan S. O’BrienHongwei XiaCraig StolarczykRalph WeisslederVivek FariasSerafim BatzoglouAsim SiddiquiOmid C. FarokhzadDaniel Hornburg;
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Sloan School and Operations Research Center Massachusetts Institute of Technology Cambridge, MA 02139, USA;

Seer, Inc. Redwood City, CA 94065, USA;

Seer, Inc. Redwood City, CA 94065, USA,Center for Nanomedicine and Department of Anesthesiology Brigham and Women’s Hospital Harvard Medical School Boston, MA 02115, USACenter for Nanomedicine and Department of Anesthesiology Brigham and Women’s Hospital Harvard Medical School Boston, MA 02115, USADepartment of Systems Biology Harvard Medical School 200 Longwood Ave, Boston, MA 02115, USA,Center for Systems Biology Massachusetts General Hospital 185 Cambridge St, CPZN 5206, Boston, MA 02114, USA;

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正文语种英语
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machine learning; nano–bio interactions; nanoparticles; protein corona; proteomics; Vroman effect;

Enhanced Competition at the Nano–Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes

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