Label-free imaging techniques such as Differential Interference Contrast (DIC) allow the observation of cells and large sub-cellular structures in their native, unperturbed states with minimal exposure to light. The development of robust computational image-analysis routines is vital to quantitative label-free imaging. The reliability of quantitative analysis of time-series microscopy data based on single particle tracking relies on accurately detecting objects as distinct from the background, i.e. segmentation. Typical approaches to segmenting DIC images either involve converting images to those resembling phase contrast, mimicking the optics of DIC object formation or using the morphological properties of objects. Here, we describe MATLAB based single particle tracking tool with a GUI for mobility analysis of objects from in vitro and in vivo DIC time-series microscopy. The tool integrates contrast enhancement with multiple modified Gaussian filters, automated threshold detection for segmentation and minimal distance based 2D single particle tracking. We compare the relative performance of multiple filters and demonstrate the utility of the tool for DIC object tracking (DICOT). We quantify sub-cellular dynamics of a time-series of Caenorhabditis elegans embryos in the one-celled stage, by detecting birefringent yolk granules in the cytoplasm with high precision. The resulting 2D map of oscillatory dynamics of granules quantifies the cytoplasmic flows driven by anaphasic spindle oscillations. The frequency of oscillations across the anterior-posterior (A-P) and transverse axes of the embryo correspond well with the reported frequency of spindle oscillations. We validate the quantitative accuracy of our method by tracking the in vitro diffusive mobility of micron-sized beads in glycerol solutions. Estimates of the diffusion coefficients of the granules are used to measure the viscosity of a dilution series of glycerol. Thus, our computational method is likely to be useful for both intracellular mobility and in vitro microrheology.

中文翻译：

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通过 DIC 对象跟踪量化细胞内粒子流

诸如微分干涉对比 (DIC) 等无标记成像技术允许观察细胞和大型亚细胞结构，使其处于天然、未受干扰的状态，并且暴露在光线下最少。开发强大的计算图像分析程序对于定量无标记成像至关重要。基于单粒子跟踪的时间序列显微数据定量分析的可靠性依赖于准确检测与背景不同的对象，即分割。分割 DIC 图像的典型方法包括将图像转换为类似相衬的图像、模拟 DIC 对象形成的光学或使用对象的形态特性。这里，我们描述了基于 MATLAB 的单粒子跟踪工具和 GUI，用于对体外和体内 DIC 时间序列显微镜中的物体进行移动性分析。该工具将对比度增强与多个修改后的高斯滤波器、用于分割的自动阈值检测和基于最小距离的 2D 单粒子跟踪集成在一起。我们比较了多个过滤器的相对性能，并展示了 DIC 对象跟踪 (DICOT) 工具的实用性。我们通过高精度检测细胞质中的双折射蛋黄颗粒来量化单细胞阶段秀丽隐杆线虫胚胎的时间序列的亚细胞动力学。由此产生的颗粒振荡动力学的二维图量化了由迟发性纺锤体振荡驱动的细胞质流动。胚胎前后（AP）和横轴的振荡频率与报告的纺锤体振荡频率很好地对应。我们通过跟踪甘油溶液中微米级珠子的体外扩散迁移率来验证我们方法的定量准确性。颗粒扩散系数的估计值用于测量甘油稀释系列的粘度。因此，我们的计算方法可能对细胞内流动性和体外微流变学都有用。颗粒扩散系数的估计值用于测量甘油稀释系列的粘度。因此，我们的计算方法可能对细胞内流动性和体外微流变学都有用。颗粒扩散系数的估计值用于测量甘油稀释系列的粘度。因此，我们的计算方法可能对细胞内流动性和体外微流变学都有用。