A Morphology and Gene Expression Atlas for Drosophila Embryogenesis

果蝇胚胎发生的形态学和基因表达图谱

• 批准号: 9081613
• 负责人: David W. Knowles
• 金额: $ 62.69万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9081613
• 关键词: Anatomy; Animals; Apoptosis; Atlases; Biological Process; Blastoderm; Cell Count; Cell Lineage; Cell Nucleus; Cell Shape; Cell membrane; Cells; Complex; Computational Technique; DNA; Data; Development; Drosophila genus; Drosophila melanogaster; Electronics; Embryo; Embryonic Development; Event; Funding; Gene Expression; Gene Expression Profile; Gene Targeting; Generic Drugs; Genetic Transcription; Goals; Grant; Health; Histocompatibility Testing; Image; Imagery; Individual; Label; Learning; Left; Life; Light; Link; Location; Maps; Methods; Mitosis; Mitotic; Modeling; Molecular; Morphogenesis; Morphology; Movement; Muscle Fibers; Nuclear; Organ; Pattern; Positioning Attribute; Process; Proteins; Records; Regulatory Pathway; Research Personnel; Resolution; Sampling; Shapes; Staging; Staining method; Stains; Structure; Study models; System; Systems Biology; Techniques; Tissue-Specific Gene Expression; Tissues; Variant; Work; biological research; cell motility; computational atlas; developmental genetics; digital; embryo tissue; gene product; insight; live cell imaging; meetings; multidisciplinary; tool; transcription factor

DESCRIPTION (provided by applicant): Creating digital atlases of animal morphology and gene expression is an emerging multidisciplinary field. The goal is to quantitate complex tissue structures and gene expression patterns as a digital record from which biological processes can be modeled and studied. Mitosis, apoptosis, differentiation and morphogenic movements result in complex embryo morphologies, and are driven by the expression of thousands of gene products that vary in amount from cell to cell throughout the embryo. Capturing such information requires high resolution embryo image data and sophisticated computational techniques to recognize thousands of cells, which vary from each other in size, shape and tissue type. Our goal is to create methods to produce a quantitative, cellular resolution map of gene expression and morphology for all of embryo development for Drosophila melanogaster. We have made significant progress towards this goal. First, for the early stage blastoderm embryo-a relatively simple, single layer structure of 6,000 cells surrounding a yolk-we created an extensive morphology and gene expression atlas that revealed previously unknown processes, such as nuclear movements. Second, funded by the previous round of this grant, we have initiated development an atlas for late stage embryos, which comprise 40,000 cells, ~70 tissue types and all major larval organs. The late embryo is significantly more complex that the blastoderm and presented new challenges: higher resolution images were required to resolve the more densely packed cells, and it proved essential to assign each nucleus/cell to a specific tissue type prior t nuclear/cell identification. This last task was difficult because it is only practical to label a gven sample with a few specific probes physically, yet ~70 different labels are required to identify each tissue separately. To meet this challenge, we developed a computational technique that uses an image of a nuclear DNA stain and the rich morphology of the system to computationally label embryonic tissues at cellular resolution. The computational labeling learns to recognize a set of spatial identifiers that uniquely describe the morphology of a specific tissue in images of nuclear stained embryos annotated to reveal that tissue. The technique is then able to recognize the tissues in other unannotated embryos using only the nuclear image data. Once nuclei have been assigned to a specific tissue, we were then able to identify nuclear volumes using nuclear segmentation methods optimized for each tissue. This approach proved essential as the tissue optimized segmentation methods performed more accurately than a single generic method applied to all tissues equally. The resulting partial atlas allowed us to characterize the variation in cell numbers between embryos and left/right asymmetry in far more detail than previously possible and sets the stage for the development of a complete atlas. We will continue this work by 1. Establishing methods to recognize intricate tissues and cell shapes, 2. Building embryo atlases for three stages of development, and 3. Using live cell approaches to determine cell lineages between atlases of different stages.

描述（由申请人提供）：创建动物形态和基因表达的数字图谱是一个新兴的多学科领域。目标是将复杂的组织结构和基因表达模式定量为数字记录，从中可以对生物过程进行建模和研究。有丝分裂、细胞凋亡、分化和形态发生运动导致复杂的胚胎形态，并由数千种基因产物的表达驱动，这些基因产物在整个胚胎中每个细胞的数量各不相同。捕获此类信息需要高分辨率胚胎图像数据和复杂的计算技术来识别数千个细胞，这些细胞的大小、形状和组织类型各不相同。我们的目标是创建方法来生成果蝇所有胚胎发育的基因表达和形态学的定量细胞分辨率图。我们已经在实现这一目标方面取得了重大进展。首先，对于早期胚盘胚胎（卵黄周围有 6,000 个细胞组成的相对简单的单层结构），我们创建了广泛的形态和基因表达图谱，揭示了以前未知的过程，例如核运动。其次，在上一轮拨款的资助下，我们开始开发晚期胚胎图谱，其中包括 40,000 个细胞、约 70 种组织类型和所有主要幼虫器官。晚期胚胎比胚盘复杂得多，并提出了新的挑战：需要更高分辨率的图像来解析更密集的细胞，并且事实证明，在核/细胞识别之前将每个核/细胞分配给特定的组织类型至关重要。最后一项任务很困难，因为仅用一些特定探针物理标记给定样本才可行，但需要约 70 个不同的标记来分别识别每个组织。为了应对这一挑战，我们开发了一种计算技术，该技术使用核 DNA 染色图像和系统丰富的形态来以细胞分辨率计算标记胚胎组织。计算标记学会识别一组空间标识符，这些标识符唯一地描述了核染色胚胎图像中特定组织的形态，并进行了注释以揭示该组织。然后，该技术能够仅使用核图像数据来识别其他未注释胚胎中的组织。一旦细胞核被分配到特定的组织，我们就能够使用针对每个组织优化的细胞核分割方法来识别细胞核体积。事实证明，这种方法至关重要，因为组织优化分割方法比同等应用于所有组织的单一通用方法执行得更准确。由此产生的部分图谱使我们能够比以前更详细地描述胚胎之间细胞数量的变化和左/右不对称性，并为完整图谱的开发奠定了基础。我们将通过以下方式继续这项工作：1.建立识别复杂组织和细胞形状的方法，2.构建三个发育阶段的胚胎图谱，3.使用活细胞方法确定不同阶段图谱之间的细胞谱系。