Evo-devo Helsinki community, Center of Excellence in Experimental Computational Developmental Biology, Institute of Biotechnology, University of Helsinki, Helsinki, Finland.
Evo-devo Helsinki community, Center of Excellence in Experimental Computational Developmental Biology, Institute of Biotechnology, University of Helsinki, Helsinki, Finland; Genomics, Bioinformatics and Evolution, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain.
Mech Dev. 2017 Apr;144(Pt B):113-124. doi: 10.1016/j.mod.2017.02.001. Epub 2017 Feb 9.
The increase in complexity in an embryo over developmental time is perhaps one of the most intuitive processes of animal development. It is also intuitive that the embryo becomes progressively compartmentalized over time and space. In spite of this intuitiveness, there are no systematic attempts to quantify how this occurs. Here, we present a quantitative analysis of the compartmentalization and spatial complexity of Ciona intestinalis over developmental time by analyzing thousands of gene expression spatial patterns from the ANISEED database. We measure compartmentalization in two ways: as the relative volume of expression of genes and as the disparity in gene expression between body parts. We also use a measure of the curvature of each gene expression pattern in 3D space. These measures show a similar increase over time, with the most dramatic change occurring from the 112-cell stage to the early tailbud stage. Combined, these measures point to a global pattern of increase in complexity in the Ciona embryo. Finally, we cluster the different regions of the embryo depending on their gene expression similarity, within and between stages. Results from this clustering analysis, which partially correspond to known fate maps, provide a global quantitative overview about differentiation and compartmentalization between body parts at each developmental stage.
胚胎在发育过程中复杂性的增加,也许是动物发育过程中最直观的过程之一。胚胎随着时间和空间的推移逐渐分隔成不同的区域,这也是直观的。尽管如此,人们并没有系统地尝试量化这种情况是如何发生的。在这里,我们通过分析来自 ANISEED 数据库的数千个基因表达空间模式,对海鞘肠道的分隔和空间复杂性进行了定量分析。我们通过两种方式来衡量分隔:基因表达的相对体积和身体部位之间基因表达的差异。我们还使用了 3D 空间中每个基因表达模式的曲率来衡量。这些指标随着时间的推移呈现出相似的增长趋势,最显著的变化发生在 112 细胞期到早期尾芽期。这些指标综合表明,海鞘胚胎的复杂性呈整体增加趋势。最后,我们根据基因表达的相似性,在各个阶段对胚胎的不同区域进行聚类。这种聚类分析的结果,部分与已知的命运图谱相对应,为每个发育阶段的身体部位之间的分化和分隔提供了全局的定量概述。