Deep learning models to predict primitive streak formation in human development

项目摘要 估计有3%的活产婴儿患有先天性出生缺陷。为了制定有效的治疗策略， 对人类早期发育的深入了解是必要的。我们的实验室最近在体外 人类原肠胚形成的模型，三个胚层（内胚层，外胚层，中胚层） 是在怀孕第三周左右形成的这种所谓的“胃样”模型是通过治疗人类 胚胎干细胞与纯化的分化因子，使他们自我组织成一种模式， 类似于原肠胚。在这个过程中的关键事件之一是原始的形成 条纹-一个专门的间充质干细胞迁移沿着胚胎中线，将形成所有 中胚层组织包括心脏、肺、血管和循环系统的细胞。在同一 此时，胚胎外围的细胞开始形成胚外中胚层，其最终将 变成胎盘组织尽管这些细胞命运变化至关重要，但目前尚不清楚它们是什么。 胚胎干细胞群将分化形成原条或胚外中胚层， 这些细胞命运的决定是如何决定的本研究金计划的研究目的是 了解人类干细胞何时以及如何分化为原始条纹和胚外中胚层 在原肠胚形成期间。我的总体方法是使用延时荧光成像来监测分化 在真实的时间和单细胞解决方案的决策。然后我会使用一种特殊类型的机器学习 被称为深度学习，以准确跟踪单个细胞的运动和信号行为。接下来我会 开发一个计算模型，使用细胞的图像模式来准确预测每个细胞如何“选择” 分化命运之间的差异。本研究的两个具体目标是：1）确定人类的亚群 胚胎干细胞，将致力于原始条纹;和2）以确定细胞内的组合 和控制向胚外中胚层分化的细胞外信号事件。拟议工作 包括新的实验程序（具体来说，目标1中的胃状体形成的实时成像）， 以及独特的神经网络架构，可以准确预测单个干细胞的二进制细胞命运结果， 根据它们的信号历史。这些方法将推广到其他生物系统。的 拟议的培训计划侧重于生成和应用尖端的统计方法， 结合单细胞特征数据，以便进行稳健的、理论上和生物学上合理的预测 人类干细胞命运的决定。更好地了解早期人类发展将为未来提供信息 细胞疗法，以预防和治疗先天性出生缺陷。为了支持我的训练，我收集了一个 强大的导师团队，在干细胞生物学、活细胞成像、机器学习方法学 因果推理。

Project Summary Congenital birth defects affect an estimated 3% of live births. To develop effective treatment strategies, a thorough understanding of early human development is necessary. Our lab recently developed and in vitro model of human gastrulation, the process by which the three germ layers (endoderm, ectoderm, mesoderm) are formed around week three of gestation. This so-called “gastruloid” model is formed by treating human embryonic stem cells with purified differentiation factors that cause them to self-organize into a pattern resembling a gastrulating embryo. One of the key events during this process is formation of the primitive streak—a migration of specialized mesenchymal stem cells along the embryonic midline that will form all mesodermal tissues including the heart, lungs, blood vessels, and cells of the circulatory system. At the same time, cells on the periphery of the embryo begin forming extraembryonic mesoderm, which will ultimately become placental tissue. Despite the critical importance of these cell fate changes, it is currently unclear which population of embryonic stem cells will differentiate to form primitive streak or extraembryonic mesoderm and how these cell fate decisions are determined. The research objective of this fellowship proposal is to understand when and how human stem cells differentiate into primitive streak and extraembryonic mesoderm during gastrulation. My overall approach is to use time-lapse fluorescence imaging to monitor differentiation decisions in real time and at single-cell solution. I will then employ a specialized type of machine learning known as deep learning to accurately track the movement and signaling behavior of individual cells. Next, I will develop a computational model that uses a cell’s image patterns to accurately predict how each cell “chooses” between differentiation fates. The two specific research aims are: 1) to identify the subpopulation of human embryonic stem cells that will commit to primitive streak; and 2) to determine the combination of intracellular and extracellular signaling events that govern differentiation to extraembryonic mesoderm. The proposed work includes novel experimental procedures (specifically, real-time imaging of gastruloids formation in Aim 1) as well as unique neural network architectures that accurately predict binary cell fate outcomes of individual stem cells based on their signaling history. These methods will be generalizable to other biological systems. The proposed training plan focuses on generating and applying cutting-edge statistical methods tasked with full single-cell feature data incorporation in order to make robust, theoretically and biologically sound predictions about human stem cell fate decisions. A better understanding of early human development will inform future cellular therapies to prevent and treat congenital birth defects. To support my training, I have assembled a strong mentorship team with expertise in stem cell biology, live-cell imaging, machine learning methodologies, and causal inference.