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首页> 外文期刊>Current Protocols in Cell Biology >QuoVadoPro, an Autonomous Tool for Measuring Intracellular Dynamics Using Temporal Variance
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QuoVadoPro, an Autonomous Tool for Measuring Intracellular Dynamics Using Temporal Variance

机译：Quovadopro，一种使用时间方差测量细胞内动态的自主工具

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摘要

Trafficking of intracellular cargo is essential to cellular function and can be defective in pathological states including cancer and neurodegeneration. Tools to quantify intracellular traffic are thus necessary for understanding this fundamental cellular process, studying disease mechanisms, and testing the effects of therapeutic pharmaceuticals. In this article we introduce an algorithm called QuoVadoPro that autonomously quantifies the movement of fluorescently tagged intracellular cargo. QuoVadoPro infers the extent of intracellular motility based on the variance of pixel illumination in a series of time‐lapse images. The algorithm is an unconventional approach to the automatic measurement of intracellular traffic and is suitable for quantifying movements of intracellular cargo under diverse experimental paradigms. QuoVadoPro is particularly useful to measure intracellular cargo movement in non‐neuronal cells, where cargo trafficking occurs as short movements in mixed directions. The algorithm can be applied to images with low temporal or spatial resolutions and to intracellular cargo with varying shapes or sizes, like mitochondria or endoplasmic reticulum: situations in which conventional methods such as kymography and particle tracking cannot be applied. In this article we present a stepwise protocol for using the QuoVadoPro software, illustrate its methodology with common examples, discuss critical parameters for reliable data analysis, and demonstrate its use with a previously publishedexample.
机译：贩运细胞内含量对细胞功能至关重要，并且在病态状态下可能有缺陷，包括癌症和神经变性。因此，为了了解这种基本的细胞过程，研究疾病机制，以及测试治疗药物的影响所必需的工具。在本文中，我们介绍了一种称为Quovadopro的算法，该算法自主地量化荧光标记的细胞内货物的运动。基于一系列时间间隔图像中的像素照明的变化，Quovadopro仅在细胞内运动程度上。该算法是对细胞内交通自动测量的非常规方法，适用于在不同实验范式下量化细胞内货物的运动。 Quovadopro在非神经元细胞中测量植物舱的颅骨运动特别有用，其中货运贩运发生在混合方向的短暂运动中。该算法可以应用于具有低时间或空间分辨率的图像，以及具有不同形状或尺寸的细胞内货物，如线粒体或内质网：不能施加常规方法的常规方法，例如键谱系和颗粒跟踪。在本文中，我们介绍了使用quovadopro软件的逐步协议，说明其与常见示例的方法，讨论了可靠的数据分析的关键参数，并展示其与先前发布的示例的使用。

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