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, USA.
Adv Mater. 2022 Nov;34(44):e2206008. doi: 10.1002/adma.202206008. Epub 2022 Sep 20.
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 NP-surface 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.
将工程纳米粒子 (NPs) 引入血液等生物流体中,会在粒子-蛋白质界面处形成一种选择性且可重现的蛋白质冠,这是由蛋白质-NP 亲和力与蛋白质丰度之间的关系驱动的。这种方法可利用蛋白质-纳米相互作用来克服目前在大样本中进行深度血浆蛋白质组学研究的局限性。本研究证明了蛋白质与 NP 表面比(P/NP)的重要性,并对蛋白质冠形成动力学进行了建模,该动力学决定了蛋白质之间的结合竞争。调整 P/NP 比可显著调节蛋白质冠的组成,增强基于 NP 的全自动深度蛋白质组学工作流程(Proteograph)的深度和精度。通过增加对工程 NPs 的结合竞争,可以在每个 NP 的表面上鉴定到 1.2-1.7 倍更多的具有 1%假发现率的蛋白质,与标准的血浆蛋白质组学工作流程相比,甚至可以鉴定到多达 3 倍的蛋白质。此外,这些数据表明 P/NP 在确定生物医学应用中纳米材料的体内命运方面起着重要作用。总之,该研究展示了 P/NP 作为生物材料和纳米医学体内的关键设计元素以及用于准确、大规模基于 NP 的深度蛋白质组学研究的强大调节策略的重要性。