Live cell-lineage tracing and machine learning reveal patterns of organ regeneration.
Live cell-lineage tracing and machine learning reveal patterns of organ regeneration.
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DOI：
10.7554/elife.30823
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发表时间：
2018-03-29
期刊：
影响因子：
7.7
通讯作者：
López-Schier H
中科院分区：
文献类型：
--
作者：
Viader-Llargués O;Lupperger V;Pola-Morell L;Marr C;López-Schier H
尽管损伤具有内在的随机性，但感觉器官在修复过程中会重现正常的结构以维持功能。在这里，我们提出了一种定量的方法，结合活细胞谱系跟踪和多因素分类的机器学习，揭示细胞的身份和定位是如何协调在器官再生。我们使用的是斑马鱼幼鱼的浅表神经乳突，其中包含三个径向对称的细胞类和一个单一的平面极性轴。高时间分辨率下细胞命运转变的可视化显示，神经瘤各向同性地再生，以恢复几何顺序，比例和极性，具有异常的准确性。我们确定生长组织内的中外侧位置作为细胞命运获取的最佳预测因子。我们提出了一个自我调节机制，指导再生过程中相同的结果与最小的外部信息。我们开发的综合方法简单且适用广泛，应该有助于在复杂组织的构建过程中定义细胞行为的预测特征。
Despite the intrinsically stochastic nature of damage, sensory organs recapitulate normal architecture during repair to maintain function. Here we present a quantitative approach that combines live cell-lineage tracing and multifactorial classification by machine learning to reveal how cell identity and localization are coordinated during organ regeneration. We use the superficial neuromasts in larval zebrafish, which contain three cell classes organized in radial symmetry and a single planar-polarity axis. Visualization of cell-fate transitions at high temporal resolution shows that neuromasts regenerate isotropically to recover geometric order, proportions and polarity with exceptional accuracy. We identify mediolateral position within the growing tissue as the best predictor of cell-fate acquisition. We propose a self-regulatory mechanism that guides the regenerative process to identical outcome with minimal extrinsic information. The integrated approach that we have developed is simple and broadly applicable, and should help define predictive signatures of cellular behavior during the construction of complex tissues.