Single-cell gene expression dynamics during neurogenesis

神经发生过程中的单细胞基因表达动态

• 批准号: 9899738
• 负责人: Daniel Burkhardt
• 金额: $ 3.03万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9899738
• 关键词: 3-Dimensional; Biological; Caenorhabditis elegans; Cardiac; Cell Differentiation process; Cell Lineage; Cells; Collaborations; Computing Methodologies; Congenital Abnormality; Data; Dependence; Development; Dimensions; Doxycycline; Dropout; Drosophila genus; Euclidean Space; Expression Profiling; Foundations; Gene Combinations; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genes; Genetic Counseling; Genetic Transcription; Germ Layers; Goals; Human; In Vitro; Label; Laboratories; Learning; Life; Mathematics; Measures; Methods; Modeling; Mus; Neural Crest; Neural Crest Cell; Neuroectoderm; Neurons; Noise; PAX3 gene; Plant Roots; Population; Process; RNA-Binding Proteins; Regenerative Medicine; Regulator Genes; Reporting; Research; Resolution; Sorting - Cell Movement; Specific qualifier value; Statistical Models; Structure; System; Technology; Time; Tissues; Variant; Work; Zebrafish; bone; cell type; combinatorial; developmental disease; discrete time; genetic testing; human embryoid body; improved; insight; mutant; nerve stem cell; neurodevelopment; neurogenesis; novel; personalized medicine; progenitor; relating to nervous system; single-cell RNA sequencing; stem cells; success; tool; transcription factor; transcriptome; transcriptome sequencing; transcriptomics; two-dimensional

PROJECT SUMMARY/ABSTRACT The proper specification of the neuroectoderm, neural crest, and neural progenitor populations dictates the overall success of neurodevelopment, and disruption of these populations is at the root of many developmental diseases. Recent advances in single cell transcriptomics allow for the study of this process in tens of thousands of cells across the transcriptome. Unlike bulk sequencing, which averages expression across a population, single cell approaches are essential to studying a multifaceted, asynchronous process like neurodevelopment. However, several computational barriers exist to analyzing scRNA-seq data, including dropout, technical noise, and batch effects. Furthermore, studying developing neural tissue provides its own challenges because in many contexts, progenitor cells and mature progeny are present at the same time. Thus, gaining biological insight from these data requires development of novel computational methods. The overall goal of this proposal is to establish computational methods to study single cell gene expression during neurodevelopment, focusing on human embryoid bodies (EBs). In aim 1, I will characterize the branching structure of germ layer and neural specification in a 27-day time course of EB culture. In my preliminary results, I used PHATE, a dimensionality reduction tool I co-developed, to describe a set of smooth transitions from stem cell through the three germ layers to several derivatives including progenitors of the cardiac, bone, and neural lineages. I will validate this structure of differentiation by FACS sorting and bulk RNA-sequencing of predicted neuroectoderm and neural progenitor cells. In aim 2, I will describe the smooth changes of gene expression the drive specification of the neural lineages by inferring the latent developmental time in the EB time course. To assign each cell a unique single developmental time label, I developed a method called MELD (Manifold Enhancement of Latent Dimensions). In my preliminary work, I use MELD to recapitulate known patterns of gene expression during the specification of neural crest cells. In this aim, I will expand my analysis to the neuroectoderm and neural progenitor populations and confirm that MELD predicts intermediate cell states present during time points not sampled in the original time course. The work in this aim will generate a continuous roadmap of gene expression from stem cell through neural progenitor in EBs. In the third aim I will use mutual information to study edge-rewiring of transcription factors and their targets (how the regulatory relationships change during neurogenesis). By modelling the statistical dependency between regulatory factors and their targets as a continuous process, it will be possible to infer the crucial windows during which regulatory factors expression has the strongest influence on downstream targets. To confirm the timing of these windows, I will use doxycycline induction to show induction has a greater effect on downstream transcription within windows than outside them. Together, these insights will produce methods to gain a greater understanding of neurogenesis from single cell RNA-sequencing data.

项目总结/摘要 神经外胚层、神经嵴和神经祖细胞群的适当特化决定了 神经发育的总体成功，以及这些群体的破坏是许多发育障碍的根源。 疾病单细胞转录组学的最新进展允许在数万个细胞中研究这一过程。 在转录组中的分布。与批量测序不同，批量测序对群体中的表达进行平均， 细胞方法对于研究像神经发育这样多方面的异步过程至关重要。 然而，分析scRNA-seq数据存在几个计算障碍，包括丢失，技术噪音， 批次效应。此外，研究发育中的神经组织本身也存在挑战，因为在许多情况下， 在某些情况下，祖细胞和成熟后代同时存在。因此，从生物学角度， 这些数据需要开发新的计算方法。该提案的总体目标是建立 计算方法来研究神经发育过程中的单细胞基因表达，重点是人类 胚状体（EBs）。在目标1中，我将描述胚层的分支结构和神经特化 在EB培养的27天时间过程中。在我的初步结果中，我使用了PHATE，一个降维工具， 共同开发的，描述了一套从干细胞通过三个胚层到几个胚层的平滑过渡。 包括心脏、骨和神经谱系的祖细胞的衍生物。我将验证这个结构， 通过预测的神经外胚层和神经祖细胞的FACS分选和批量RNA测序进行分化 细胞在目标2中，我将描述基因表达的平滑变化，神经元的驱动规范， 通过推断EB时间进程中的潜在发育时间来推断谱系。为每个单元格指定唯一的单个 在开发时间标签的过程中，我开发了一种称为MELD（潜在维度的流形增强）的方法。在 我的初步工作，我用MELD概括已知的基因表达模式，在规范的， 神经嵴细胞在这个目标下，我将把我的分析扩展到神经外胚层和神经祖细胞群体 并确认MELD预测在原始数据中未采样的时间点期间存在的中间单元状态 时间进程这一目标的工作将产生一个连续的基因表达路线图，从干细胞到 EB中的神经祖细胞。在第三个目标中，我将利用互信息来研究转录的边缘重连 因子及其靶点（神经发生过程中调控关系如何变化）。通过建模， 作为一个连续的过程，监管因素及其目标之间的统计依赖关系， 推断调控因子表达对细胞生长影响最大的关键窗口， 下游目标为了确认这些窗口的时间，我将使用强力霉素诱导来显示诱导 在窗口内对下游转录的影响大于窗口外。总之，这些见解将 产生方法，从单细胞RNA测序数据中更好地了解神经发生。