Kursawe Jochen, Bardenet Rémi, Zartman Jeremiah J, Baker Ruth E, Fletcher Alexander G
Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK
CNRS and CRIStAL, Université de Lille, 59651 Villeneuve d'Ascq, France.
J R Soc Interface. 2016 Nov;13(124). doi: 10.1098/rsif.2016.0725.
Tracking of cells in live-imaging microscopy videos of epithelial sheets is a powerful tool for investigating fundamental processes in embryonic development. Characterizing cell growth, proliferation, intercalation and apoptosis in epithelia helps us to understand how morphogenetic processes such as tissue invagination and extension are locally regulated and controlled. Accurate cell tracking requires correctly resolving cells entering or leaving the field of view between frames, cell neighbour exchanges, cell removals and cell divisions. However, current tracking methods for epithelial sheets are not robust to large morphogenetic deformations and require significant manual interventions. Here, we present a novel algorithm for epithelial cell tracking, exploiting the graph-theoretic concept of a 'maximum common subgraph' to track cells between frames of a video. Our algorithm does not require the adjustment of tissue-specific parameters, and scales in sub-quadratic time with tissue size. It does not rely on precise positional information, permitting large cell movements between frames and enabling tracking in datasets acquired at low temporal resolution due to experimental constraints such as phototoxicity. To demonstrate the method, we perform tracking on the embryonic epidermis and compare cell-cell rearrangements to previous studies in other tissues. Our implementation is open source and generally applicable to epithelial tissues.
在活细胞成像显微镜视频中追踪上皮细胞层中的细胞，是研究胚胎发育基本过程的有力工具。表征上皮细胞中的细胞生长、增殖、插入和凋亡，有助于我们理解诸如组织内陷和延伸等形态发生过程是如何在局部进行调节和控制的。准确的细胞追踪需要正确解决帧间进入或离开视野的细胞、细胞邻居交换、细胞移除和细胞分裂问题。然而，当前用于上皮细胞层的追踪方法对大的形态发生变形并不稳健，并且需要大量的人工干预。在这里，我们提出了一种用于上皮细胞追踪的新算法，利用“最大公共子图”的图论概念来追踪视频帧间的细胞。我们的算法不需要调整组织特异性参数，并且随组织大小呈亚二次时间尺度变化。它不依赖于精确的位置信息，允许帧间细胞的大幅移动，并能够在由于光毒性等实验限制而以低时间分辨率获取的数据集中进行追踪。为了演示该方法，我们对胚胎表皮进行了追踪，并将细胞间重排与之前在其他组织中的研究进行了比较。我们的实现是开源的，并且普遍适用于上皮组织。
