Deciphering Genetic and Epigenetic Regulatory Logic of Germ Layer Differentiation with Manifold Learning

项目摘要： 深入了解控制人类早期发育的遗传和表观遗传调控逻辑， 人工免疫系统对于揭示发育性疾病的机制和设计新的治疗方案至关重要 用于再生医学应用。虽然多年来许多发育重要的基因， 目前还没有系统地了解这些基因如何动态地相互作用， 和生物体表型。为此，我们建议将联合收割机的实验和计算相结合 人胚胎干细胞早期胚层发育预测模型的建立 细胞（hESC）。在我们的初步工作中，我们生成了一个单细胞RNA测序（scRNA-seq）数据集， 31，000个hESC，作为胚状体（EB）生长27天以观察分化为 不同的细胞谱系我们开发并应用了一种新的降维和可视化方法 我把PHATE称为这个系统，发现PHATE生成了一个全面的、可解释的 差异化的图像。它涵盖了早期发育的所有分支，包括胚胎干细胞，神经嵴细胞， 及其衍生物、神经祖细胞以及中胚层和内胚层的细胞。基础上 这些发现，我们建议将这项研究延长到60天的时间过程，并使PHATE更具规模， 能够捕获分化为更成熟的谱系。然后，我们建议整合scRNA-seq和 表观遗传数据，通过将分选群体上的批量CHIP-seq测量值内插到伪单 细胞分辨率最后，为了理解引导分化的基因调控逻辑，沿着 具体的血统，我们将训练一个新的神经网络架构称为DyMon（动力学建模 网络），通过数据流形学习预测计算模型的胚芽层去， 隐藏在其隐藏层中。因此，我们将把基因调控逻辑重新布线与发育 细胞表型，并提供在此过程中重新编程的见解。

Project Summary: A deep understanding of the genetic and epigenetic regulatory logic that controls early development in hu- mans is essential for uncovering the mechanisms of developmental diseases and designing new protocols for regenerative medicine applications. Although over the years many developmentally important genes, there has not been a systematic understanding of how these genes interact dynamically to create cellular and organismal phenotype. For this purpose, we propose to combine experimental and computational approaches to develop predictive models of early germ layer development from human embryonic stem cell (hESC). In our preliminary work, we generated a single-cell RNA-sequencing (scRNA-seq) dataset of 31,000 hESCs, grown as embryoid bodies (EBs) over a period of 27 days to observe differentiation into diverse cell lineages. We developed and applied a new dimensionality reduction and visualization method called PHATE to this system and discovered that PHATE generates a comprehensive and interpretable picture of differentiation. It captures all branches of early development, including ESCs, neural crest cells and their derivatives, neural progenitors, and cells of the mesoderm and endoderm layers. Building upon these findings, we propose to extend this study to a 60-day time course and rendering PHATE more scal- able to capture differentiation to more mature lineages. Then we propose to integrate scRNA-seq and epigenetic data, by interpolating bulk CHIP-seq measurements on sorted populations to a pseudo single- cell resolution. Finally, in order to understand the gene regulatory logic that guides differentiation along specific lineages, we will train a new neural network architecture known as DyMon (dynamics modeling network), to walk through the data-manifold to learn a predictive computational model of germ layer de- velopment in its hidden layers. Thus we will connect gene regulatory logic rewiring with developmental cellular phenotypes and offer insights into reprogramming during this process.