Automation and low-cost proteomics for characterization of the protein corona: experimental methods for big data
Automation and low-cost proteomics for characterization of the protein corona: experimental methods for big data
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DOI：
10.1007/s00216-020-02726-1
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发表时间：
2020-06-04
影响因子：
4.3
通讯作者：
Payne, Christine K.
中科院分区：
文献类型：
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作者：
Poulsen, Karsten M.;Pho, Thomas;Payne, Christine K.
在生物环境中使用的纳米颗粒暴露于吸附在表面上形成蛋白冠的蛋白质。这些吸附的蛋白质决定了随后的细胞反应。一个主要的挑战是预测哪些蛋白质将吸附在给定的纳米颗粒表面上。相反，每种新的纳米颗粒和纳米颗粒修饰都必须经过实验测试，以确定哪些蛋白质吸附在表面上。我们认为，任何未来的预测能力都将取决于蛋白质-纳米颗粒相互作用的大型数据集。作为实现这一目标的第一步，我们开发了一种自动化工作流程，使用液体处理机器人来形成和分离蛋白质冠。由于该工作流程取决于磁性分离步骤，因此我们测试了将磁性纳米颗粒嵌入蛋白质纳米颗粒内的能力。这些实验表明，磁力分离可用于任何类型的可嵌入磁芯的纳米颗粒。更高通量的电晕表征还需要更低成本的蛋白质组学方法。我们报告了快速、低成本和标准、较慢、成本较高的液相色谱与质谱的比较，以识别蛋白质冠。这些方法将在获取预测纳米颗粒-蛋白质相互作用所需的大型数据集方面向前迈出一步。
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.