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

项目总结： 对控制人类早期发育的遗传和表观遗传调控逻辑有深刻的理解 MANS对于揭示发育疾病的机制和设计新的方案是必不可少的 用于再生医学应用。尽管多年来许多对发育具有重要意义的基因， 对于这些基因如何动态地相互作用来创造细胞，还没有一个系统的了解 和生物表型。为此，我们建议将实验和计算相结合。 建立人类胚胎干细胞早期生殖层发育预测模型的方法 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个胚胎干细胞，在27天内以类胚体(EBS)的形式生长，观察分化为 不同的细胞系。我们开发并应用了一种新的降维和可视化方法 将PHATE调用到该系统，并发现PHATE生成了一个全面的和可解释的 差异化的图景。它捕获了早期发育的所有分支，包括ESCs，神经脊细胞 以及它们的衍生物、神经前体细胞以及中胚层和内胚层的细胞。建立在 我们建议将这项研究扩展到60天的时间进程，并使fi更具规模- 能够捕捉到分化到更成熟的谱系。然后，我们建议将scRNA-seq和 表观遗传学数据，通过将排序群体的批量CHIP-SEQ测量内插到伪单序列中。 单元格分辨率。最后，为了理解引导分化的基因调控逻辑 我们将训练一种新的神经网络体系结构，称为fi(动力学建模 网络)，遍历数据流形以学习胚层分解的预测计算模型。 它的隐藏层中的发展。因此，我们将把基因调控逻辑的重新布线与发育 细胞表型，并提供了对这一过程中的重新编程的见解。

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.