A Single-Cell Transcriptome Atlas for Zebrafish Development

安全、有效的治疗通常针对仅在与疾病相关的疾病中出现的特定疾病相关分子 单元类型。问题：我们知识的空白包括对任何细胞类型的全面定义 脊椎动物物种的发育时间和对每种细胞类型在什么位置表达哪些基因的了解 级别。如果我们知道，目前只知道序列的基因可能会为细胞治疗提供独特的靶点 哪种细胞类型表达它们。一条前进的道路：最近已经有可能识别转录 利用单细胞RNA测序获得具有前所未有的分子精确度的单个单细胞图谱 (scRNA-seq)与强大的高度降维软件相结合，将细胞分组为 生物信息识别的簇包含与基因表达谱密切相关的细胞类型。这个 这个项目的目标是首先，全面鉴定转录上独特的细胞类型 斑马鱼的发育时间，一个主要的医学模型，第二，这些数据作为资源发布 通过斑马鱼信息网以方便搜索的形式提供给研究界 (ZFIN)。方法：目标1确定野生型不同阶段的单细胞转录组表型。 斑马鱼胚胎、幼体和幼鱼，并通过原位杂交定位这些注释的细胞类型 细胞类型特异性标记基因在整体和组织学上的表达实验 横断面。目的2确定野生型斑马鱼所有主要器官的单细胞转录组表型。 成年男性和女性，并通过细胞类型特异性的原位杂交鉴定体内突出的细胞类型 组织切片上的标记基因。目标3是开发一种自动生物信息学管道来识别细胞 通过将基因表达谱与现有资源进行比较，包括ZFIN， 其他模式生物数据库(AGR，基因组资源联盟)和人类基因表达数据。 目标4是在ZFIN中开发一个界面，使研究界能够轻松地查询斑马鱼scRNA- 序列号数据。创新：目前还没有动物物种有组织的全面的细胞类型概要 通过发育过程中全基因组范围内的基因表达模式。意义：此R24应用程序 将开发资源和相关材料，以1)增强、进一步描述和改进关键的 用于研究人类疾病机制的动物模型；2)促进访问从 使用人类疾病的动物模型；以及3)解决许多分类NIH的研究兴趣 专注于各种器官系统和疾病类型的研究所和中心。此资源将标识 以前未知的细胞类型，从而促进了针对潜在疗法的细胞类型的精确定位；将 将以前未知的基因与特定的细胞类型联系起来，从而增加了潜在的分子靶点 药物疗法；并将提出基因表达网络的假说，从而提高我们对 健康中的细胞机制，并加深我们对疾病病因学中基因相互作用网络的理解。

Safe, effective therapies generally target specific disease-related molecules that appear only in disease-related cell types. The problem: Gaps in our knowledge include a comprehensive definition of cell types in any vertebrate species over developmental time and knowledge of which genes each cell type expresses at what levels. Genes currently known only by sequence might provide unique targets for cell therapies if we knew which cell types express them. A way forward: It has recently become possible to identify the transcriptional profile of individual, single cells with unprecedented molecular precision using single-cell RNA sequencing (scRNA-seq) coupled with powerful highly dimensional-reducing software that groups cells into bioinformatically identified clusters containing cell types with closely related gene expression profiles. The goals of this project are first, the comprehensive identification of transcriptionally unique cell types over developmental time in zebrafish, a major medical model, and second, the release of these data as a resource to the research community in a convenient searchable format through the Zebrafish Information Network (ZFIN). Approach: Aim 1 is to define single cell transcriptome phenotypes for various stages of wild-type zebrafish embryos, larvae, and juveniles and to locate these annotated cell types by in situ hybridization experiments displaying the expression of cell type-specific marker genes on whole mounts and histological sections. Aim 2 is to define the single cell transcriptome phenotype for all major organs in wild-type zebrafish adult males and females and to identify prominent cell types in vivo by in situ hybridization for cell type-specific marker genes on histological sections. Aim 3 is to develop an automated bioinformatic pipeline to identify cell types in scRNA-seq clusters by comparing gene expression profiles to existing resources, including ZFIN, other model organism databases (AGR, Alliance of Genome Resources), and human gene expression data. Aim 4 is to develop an interface in ZFIN to enable the research community to easily query zebrafish scRNA- seq data. Innovation: No animal species currently has a comprehensive compendium of cell types organized by gene expression patterns on a genome-wide scale during development. Significance: This R24 application will develop resources and related materials that will 1) enhance, further characterize, and improve a critical animal model for the investigation of human disease mechanisms; 2) facilitate access to data generated from the use of animal models of human disease; and 3) address the research interests of many categorical NIH Institutes and Centers that focus on various organ systems and disease types. This resource will identify previously unknown cell types, thus facilitating the precision targeting of cell types for potential therapies; will associate previously unknown genes with specific cell types, thus increasing potential molecular targets for drug therapies; and will suggest hypotheses for gene expression networks, thus improving our knowledge of cellular mechanisms in health and deepening our understanding of gene interaction webs in disease etiology.