Characterizing Cell–Cell Interactions Induced Spatial Organization of Cell Phenotypes: Application to Density-Dependent Protein Nucleocytoplasmic Distribution
Characterizing Cell–Cell Interactions Induced Spatial Organization of Cell Phenotypes: Application to Density-Dependent Protein Nucleocytoplasmic Distribution
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DOI：
10.1007/s12013-012-9412-8
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发表时间：
2012-08
影响因子：
2.6
通讯作者：
Fujun Han;Biliang Zhang
中科院分区：
文献类型：
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作者：
Fujun Han;Biliang Zhang
细胞间的相互作用在多细胞生物发育过程中的空间组织（模式形成）中起着重要作用。了解这些生物学作用需要确定细胞表型，细胞间相互作用的调控和表征的表型的空间组织。然而，用于测定细胞-细胞相互作用的常规方法主要适用于细胞群体水平。这些措施无法阐明表型的空间组织，导致细胞与细胞相互作用的不完整视图。为了克服这个问题，我们开发了一种自动化的基于图像的方法来研究基于细胞空间定位的细胞-细胞相互作用。我们在培养的细胞中使用β-连环蛋白和芳烃受体的细胞密度依赖性核质分布作为表型证明了这种方法。通过与传统的基于群体的方法进行比较，验证了该方法的有效性，并证明了该方法的灵敏度和可靠性。该方法的应用表征了表型在培养细胞群体中的空间组织方式。我们进一步表明，空间组织是由细胞密度和蛋白质特异性。这种自动化的方法非常简单，将适用于研究从原核菌落到多细胞生物的不同系统中的细胞-细胞相互作用。我们设想，提取和解释细胞间相互作用如何决定细胞表型的空间组织的能力将为传统的群体平均研究可能错过的生物学提供新的见解。
Cell–cell interactions play an important role in spatial organization (pattern formation) during the development of multicellular organisms. An understanding of these biological roles requires identifying cell phenotypes that are regulated by cell–cell interactions and characterizing the spatial organizations of the phenotypes. However, conventional methods for assaying cell–cell interactions are mainly applicable at a cell population level. These measures are incapable of elucidating the spatial organizations of the phenotypes, resulting in an incomplete view of cell–cell interactions. To overcome this issue, we developed an automated image-based method to investigate cell–cell interactions based on spatial localizations of cells. We demonstrated this method in cultured cells using cell density-dependent nucleocytoplasmic distribution of β-catenin and aryl hydrocarbon receptor as the phenotype. This novel method was validated by comparing with a conventional population-based method, and proved to be more sensitive and reliable. The application of the method characterized how the phenotypes were spatially organized in a population of cultured cells. We further showed that the spatial organization was governed by cell density and was protein-specific. This automated method is very simple, and will be applicable to study cell–cell interactions in different systems from prokaryotic colonies to multicellular organisms. We envision that the ability to extract and interpret how cell–cell interactions determine the spatial organization of a cell phenotype will provide new insights into biology that may be missed by traditional population-averaged studies.