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基于活细胞成像的实验方法,用于追踪网格蛋白介导的胞吞作用中颗粒的摄取。

A live cell imaging-based assay for tracking particle uptake by clathrin-mediated endocytosis.

机构信息

Department of Biomedical Engineering, The University of Texas at Austin.

Department of Biomedical Engineering, The University of Texas at Austin; Department of Chemical Engineering, The University of Texas at Austin.

出版信息

Methods Enzymol. 2024;700:413-454. doi: 10.1016/bs.mie.2024.02.010. Epub 2024 Mar 22.

Abstract

A popular strategy for therapeutic delivery to cells and tissues is to encapsulate therapeutics inside particles that cells internalize via endocytosis. The efficacy of particle uptake by endocytosis is often studied in bulk using flow cytometry and Western blot analysis and confirmed using confocal microscopy. However, these techniques do not reveal the detailed dynamics of particle internalization and how the inherent heterogeneity of many types of particles may impact their endocytic uptake. Toward addressing these gaps, here we present a live-cell imaging-based method that utilizes total internal reflection fluorescence microscopy to track the uptake of a large ensemble of individual particles in parallel, as they interact with the cellular endocytic machinery. To analyze the resulting data, we employ an open-source tracking algorithm in combination with custom data filters. This analysis reveals the dynamic interactions between particles and endocytic structures, which determine the probability of particle uptake. In particular, our approach can be used to examine how variations in the physical properties of particles (size, targeting, rigidity), as well as heterogeneity within the particle population, impact endocytic uptake. These data impact the design of particles toward more selective and efficient delivery of therapeutics to cells.

摘要

一种将治疗药物封装到细胞内的常用策略是通过胞吞作用将治疗药物包裹在颗粒中。胞吞作用中颗粒的摄取效率通常通过流式细胞术和 Western blot 分析进行批量研究,并通过共聚焦显微镜进行确认。然而,这些技术并不能揭示颗粒内化的详细动力学过程,以及许多类型颗粒的固有异质性如何影响其胞吞摄取。为了解决这些差距,我们在这里提出了一种基于活细胞成像的方法,该方法利用全内反射荧光显微镜平行跟踪大量单个颗粒的摄取,因为它们与细胞内吞机制相互作用。为了分析得到的数据,我们使用开源跟踪算法和自定义数据滤波器。这种分析揭示了颗粒与内吞结构之间的动态相互作用,从而确定了颗粒摄取的概率。特别是,我们的方法可以用来研究颗粒的物理性质(大小、靶向性、刚性)的变化以及颗粒群体内的异质性如何影响内吞摄取。这些数据影响了颗粒的设计,使其更具选择性,从而更有效地将治疗药物递送到细胞中。

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