Deep learning models to predict primitive streak formation in human development

深度学习模型预测人类发育中的原始条纹形成

• 批准号: 10559639
• 负责人: Tarek Zikry
• 金额: $ 3.92万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10559639
• 关键词: Affect; Antibodies; Architecture; Behavior; Bilateral; Blood Vessels; Brachyury protein; Cardiovascular system; Cell Cycle; Cell Lineage; Cell Therapy; Cells; Characteristics; Computer Models; Congenital Abnormality; Cues; Data; Data Analyses; Data Set; Defect; Dependence; Development; Differential Equation; Ectoderm; Embryo; Embryonic Development; Endoderm; Event; Fellowship; Future; Generations; Germ Layers; Goals; Heart; Human; Human Development; Human body; Image; Individual; Inherited; Live Birth; Lung; Machine Learning; Mentorship; Mesenchymal Stem Cells; Mesoderm; Mesoderm Cell; Methodology; Methods; Microscopy; Modeling; Monitor; Movement; Outcome; Pattern; Placenta; Population; Positioning Attribute; Pregnancy; Prevention; Primitive Streaks; Procedures; Process; Public Health; Recording of previous events; Regenerative Medicine; Reporter; Research; Research Training; Signal Transduction; Specific qualifier value; Spontaneous abortion; Stains; Statistical Methods; Stream; Structure; Surface; System; Time; Tissues; Training; Work; biological systems; blastomere structure; bone morphogenetic protein 4; cell type; cellular imaging; daughter cell; deep learning; deep learning model; differentiation protocol; directed differentiation; effective therapy; embryo cell; embryonic stem cell; extracellular; fluorescence imaging; gastrulation; human embryonic stem cell; human imaging; human model; human stem cells; improved; in vitro Model; live cell imaging; machine learning method; migration; molecular marker; morphogens; movie; neural network; neural network architecture; novel; pluripotency; precursor cell; prevent; real-time images; recurrent neural network; repaired; self organization; self-renewal; skills; sound; statistical learning; stem cell biology; stem cell differentiation; stem cell fate; stem cell therapy; stem cells; treatment strategy; undergraduate student

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.

项目摘要 据估计，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，a 对早期人类发育的透彻理解是必要的。我们的实验室最近开发出了一种体外培养技术 人类原肠形成的模型，即三个胚层(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“原肠”模型是通过治疗人类 含有纯化的分化因子的胚胎干细胞可以使它们自组织成一种模式 原肠胚胎的类似原肠胚胎的在这个过程中的一个关键事件是原语的形成 条纹-特化间充质干细胞沿胚胎中线的迁移，将形成ALL 中胚层组织，包括心脏、肺、血管和循环系统的细胞。同时 随着时间的推移，胚胎外围的细胞开始形成胚外中胚层，最终 变成胎盘组织。尽管这些细胞命运的改变至关重要，但目前还不清楚哪些是 胚胎干细胞群体将分化形成原始条纹或胚外中胚层 这些细胞命运的决定是如何决定的。这项奖学金提案的研究目标是 了解人类干细胞何时以及如何分化为原始条纹和胚胎外中胚层 在原肠形成期间。我的总体方法是使用延时荧光成像来监测分化 实时决策和单电池解决方案。然后，我将采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 开发一种计算模型，使用细胞的图像模式来准确预测每个细胞如何“选择” 在分化的命运之间。两个具体的研究目的是：1)识别人类亚群 胚胎干细胞将致力于原始条纹；2)确定细胞内的结合 以及控制胚胎外中胚层分化的细胞外信号事件。拟议中的工作 包括新的实验程序(具体地说，目标1中的原肠形成的实时成像)为 以及独特的神经网络结构，可以准确预测单个干细胞的二元细胞命运结果 基于其信号历史的细胞。这些方法将被推广到其他生物系统。这个 拟议的培训计划侧重于生成和应用尖端统计方法，任务是全面 结合单细胞特征数据以做出稳健的、理论上和生物学上合理的预测 关于人类干细胞命运的决定。对早期人类发育的更好理解将为未来 预防和治疗先天性出生缺陷的细胞疗法。为了支持我的训练，我组织了一支 强大的导师团队，拥有干细胞生物学、活细胞成像、机器学习方法、 和因果推论。