Multiscale feature analysis of salivary gland branching morphogenesis.
Multiscale feature analysis of salivary gland branching morphogenesis.
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DOI：
10.1371/journal.pone.0032906
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Yener B
中科院分区：
文献类型：
--
作者：
Bilgin CC;Ray S;Baydil B;Daley WP;Larsen M;Yener B
发育中组织的模式形成涉及细胞组织的动态时空变化和随后的功能成人结构的进化。分枝形态发生是一种在许多发育器官中产生图案的发育机制，它受潜在的分子途径控制。要了解分子信号、细胞行为和由此引起的形态变化之间的关系，就需要对细胞行为进行量化和分类。在这项研究中，通过构建细胞图来计算在多个尺度上捕捉结构属性的数学特征，来模拟发育中的唾液腺在岩石介导的信号被破坏时的组织水平和细胞变化。这些特征被用来生成未经处理和岩石信号破坏的唾液腺器官外植体的多尺度细胞图签名。从小鼠颌下腺器官外植体上皮和间充质核标记的共聚焦图像中，导出了一个多尺度特征集，该特征集捕捉了组织的整体结构属性、局部结构属性、光谱和形态属性。六种特征选择算法和数据的多路建模被用来识别细胞图特征的不同子集，这些特征子集可以唯一地分类和区分不同的细胞群体。多尺度细胞图分析在组织状态分类中最为有效。细胞和组织结构，如细胞图特征的多尺度子集所定义的，在存在和不存在ROCK抑制剂的情况下，在上皮细胞和间充质细胞类型中都是定量不同的。虽然张量分析表明上皮组织受ROCK信号抑制的影响最大，但该分析发现间充质组织发生了重大的多尺度变化，这在以前的生物学研究中是没有发现的。我们在这里展示了如何定义和计算多尺度特征集，作为一种有效的计算方法来识别和量化多个生物尺度上的变化，并区分正在发育的组织中的不同状态。
Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues.
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