De novo inference of systems-level mechanistic models of development from live-imaging-based phenotype analysis.
De novo inference of systems-level mechanistic models of development from live-imaging-based phenotype analysis.
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DOI：
10.1016/j.cell.2013.11.046
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发表时间：
2014-01-16
期刊：
影响因子：
64.5
通讯作者：
Bao Z
中科院分区：
文献类型：
--
作者：
Du Z;Santella A;He F;Tiongson M;Bao Z
阐明复杂表型以获得机制见解对系统生物学提出了重大挑战。我们报告了一种根据实时成像数据自动推断细胞命运分化机制模型的策略。我们使用细胞谱系追踪和组织特异性标记表达的组合来测定祖细胞命运并检测遗传扰动时的命运变化。基于细胞表型，我们进一步构建了祖细胞命运分化如何进展的模型，并预测了调节一系列命运选择的细胞特异性基因模块和细胞间信号传导事件。我们通过干扰 300 多个胚胎中的 20 个基因来验证我们的在线虫胚胎发生方法。结果不仅概括了当前的知识，而且还提供了对基因功能和受调控的命运选择的见解，包括意想不到的自我更新。我们的研究为复杂的体内信息的自动和定量解释提供了一种强大的方法。
Elucidation of complex phenotypes for mechanistic insights presents a significant challenge in systems biology. We report a strategy to automatically infer mechanistic models of cell fate differentiation based on live-imaging data. We use cell lineage tracing and combinations of tissue-specific marker expression to assay progenitor cell fate and detect fate changes upon genetic perturbation. Based on the cellular phenotypes, we further construct a model for how fate differentiation progresses in progenitor cells and predict cell-specific gene modules and cell-to-cell signaling events that regulate the series of fate choices. We validate our approach in C. elegans embryogenesis by perturbing 20 genes in over 300 embryos. The result not only recapitulates current knowledge but also provides insights into gene function and regulated fate choice, including an unexpected self-renewal. Our study provides a powerful approach for automated and quantitative interpretation of complex in vivo information.
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