Department of NanoEngineering, Chemical Engineering Program, and Moores Cancer Center, University of California San Diego, La Jolla, CA, USA.
Nat Nanotechnol. 2024 Mar;19(3):345-353. doi: 10.1038/s41565-023-01533-w. Epub 2023 Oct 30.
Since their initial development, cell membrane-coated nanoparticles (CNPs) have become increasingly popular in the biomedical field. Despite their inherent versatility and ability to enable complex biological applications, there is considerable interest in augmenting the performance of CNPs through the introduction of additional functionalities. Here we demonstrate a genetic-engineering-based modular approach to CNP functionalization that can encompass a wide range of ligands onto the nanoparticle surface. The cell membrane coating is engineered to express a SpyCatcher membrane anchor that can readily form a covalent bond with any moiety modified with SpyTag. To demonstrate the broad utility of this technique, three unique targeted CNP formulations are generated using different classes of targeting ligands, including a designed ankyrin repeat protein, an affibody and a single-chain variable fragment. In vitro, the modified nanoparticles exhibit enhanced affinity towards cell lines overexpressing the cognate receptors for each ligand. When formulated with a chemotherapeutic payload, the modularly functionalized nanoparticles display strong targeting ability and growth suppression in a murine tumour xenograft model of ovarian cancer. Our data suggest genetic engineering offers a feasible approach for accelerating the development of multifunctional CNPs for a broad range of biomedical applications.
自最初开发以来,细胞膜包覆的纳米颗粒(CNPs)在生物医学领域越来越受欢迎。尽管它们具有固有多功能性,并且能够实现复杂的生物学应用,但人们仍然非常关注通过引入额外的功能来增强 CNPs 的性能。在这里,我们展示了一种基于基因工程的 CNP 功能化模块化方法,可以将各种配体包被到纳米颗粒表面。细胞膜涂层经过工程设计,表达 SpyCatcher 膜锚,该锚可以与任何用 SpyTag 修饰的部分轻易形成共价键。为了证明该技术的广泛适用性,使用三种不同类型的靶向配体生成了三种独特的靶向 CNP 制剂,包括设计的锚蛋白重复蛋白、亲和体和单链可变片段。在体外,修饰后的纳米颗粒对每种配体的高表达细胞系表现出增强的亲和力。当与化疗有效载荷联合使用时,模块化功能化的纳米颗粒在卵巢癌的小鼠肿瘤异种移植模型中显示出强烈的靶向能力和生长抑制作用。我们的数据表明,遗传工程为开发广泛应用于生物医学的多功能 CNPs 提供了一种可行的方法。