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Robust cell tracking in epithelial tissues through identification of maximum common subgraphs

机译:通过识别最大共同子图对上皮组织中的细胞进行可靠的跟踪

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摘要

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 Drosophila embryonic epidermis and compare cell–cell rearrangements to previous studies in other tissues. Our implementation is open source and generally applicable to epithelial tissues.
机译:跟踪上皮片的实时成像显微视频中的细胞是研究胚胎发育中基本过程的有力工具。表征上皮细胞的生长,增殖,嵌入和凋亡有助于我们理解形态发生过程,例如组织内陷和延伸,是如何局部调控的。准确的细胞跟踪要求正确解析进入或离开帧,细胞邻居交换,细胞去除和细胞分裂之间视野的细胞。但是,当前的上皮片层追踪方法对于大的形态发生变形并不稳健,需要大量的人工干预。在这里,我们提出了一种新颖的上皮细胞跟踪算法,它利用“最大共同子图”的图论概念来跟踪视频帧之间的细胞。我们的算法不需要调整组织特定参数,并且可以在亚二次时间内随组织大小缩放。它不依赖于精确的位置信息,它允许帧之间的大单元移动,并且由于诸如光毒性之类的实验约束而能够在以低时间分辨率获取的数据集中进行跟踪。为了证明该方法,我们对果蝇胚胎表皮进行跟踪,并将细胞-细胞重排与以前在其他组织中的研究进行比较。我们的实现是开源的,并且通常适用于上皮组织。

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