Abstract：
Lysosomes are highly dynamic degradation/recycling organelles that harbor sophisticated molecular sensors and signal transduction machinery through which they control cell adaptation to environmental cues and nutrients. The movements of these signaling hubs comprise persistent, directional runs-active, ATP-dependent transport along the microtubule tracks-interspersed by short, passive movements and pauses imposed by cytoplasmic constraints. The trajectories of individual lysosomes are usually obtained by time-lapse imaging of the acidic organelles labeled with LysoTracker dyes or fluorescently-tagged lysosomal-associated membrane proteins LAMP1 and LAMP2. Subsequent particle tracking generates large data sets comprising thousands of lysosome trajectories and hundreds of thousands of data points. Analyzing such data sets requires unbiased, automated methods to handle large data sets while capturing the temporal heterogeneity of lysosome trajectory data. This chapter describes integrated and largely automated workflow from live cell imaging to lysosome trajectories to computing the parameters of lysosome dynamics. We describe an open-source code for implementing the continuous wavelet transform (CWT) to distinguish trajectory segments corresponding to active transport (i.e., \"runs\" and \"flights\") versus passive lysosome movements. Complementary cumulative distribution functions (CDFs) of the \"runs/flights\" are generated, and Akaike weight comparisons with several competing models (lognormal, power law, truncated power law, stretched exponential, exponential) are performed automatically. Such high-throughput analyses yield useful aggregate/ensemble metrics for lysosome active transport.
摘要：
溶酶体是高度动态的降解/回收细胞器，具有复杂的分子传感器和信号转导机制，通过它们控制细胞对环境线索和营养的适应。这些信令集线器的移动包括持久性，定向运行-活动，沿着微管轨道的ATP依赖性运输-由短，细胞质约束施加的被动运动和停顿。单个溶酶体的轨迹通常是通过用LysoTracker染料或荧光标记的溶酶体相关膜蛋白LAMP1和LAMP2标记的酸性细胞器的延时成像获得的。随后的粒子跟踪生成包括数千个溶酶体轨迹和数十万数据点的大数据集。分析这样的数据集需要无偏见，处理大型数据集的自动化方法，同时捕获溶酶体轨迹数据的时间异质性。本章描述了从活细胞成像到溶酶体轨迹再到计算溶酶体动力学参数的集成和基本上自动化的工作流程。我们描述了一个用于实现连续小波变换（CWT）的开源代码，以区分与主动运输相对应的轨迹段（即，“运行”和“飞行”）与被动溶酶体运动。生成“运行/航班”的互补累积分布函数(CDF)，和Akaike重量与几个竞争模型的比较(对数正态，幂律,截断幂律，拉伸指数，指数)自动执行。这种高通量分析产生用于溶酶体主动转运的有用的聚集/集合度量。
