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地图集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，可以从中对生物过程进行建模和研究。有丝分裂，细胞凋亡，分化和形态学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因细胞的数量而变化。捕获此类信息需要高分辨率的胚胎图像数据和复杂的计算技术，以识别数千个细胞，它们的大小，形状和组织类型彼此不同。我们的目标是创建为果蝇果蝇的所有胚胎发育的基因表达和形态的定量，细胞分辨率图。我们已经朝着这个目标取得了重大进展。首先，对于早期胚胚胚 - 一个相对简单的，围绕蛋黄的6,000个细胞的单层结构，我们创造了广泛的形态和基因表达地图集，该地图集揭示了以前未知的过程，例如核运动。其次，由该赠款的上一轮资助，我们启动了晚期胚胎的地图集，该胚胎包含40,000个细胞，约70种组织类型和所有主要的幼虫器官。晚期胚胎比胚钉和提出新的挑战要复杂得多：解决较密集的堆积细胞需要更高的分辨率图像，事实证明，将每个核/细胞分配给特定的组织类型t核/细胞鉴定。最后的任务很困难，因为仅在物理上用一些特定探针标记GVEN样品，但需要约70个不同的标签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染色的图像和系统的丰富形态来在细胞分辨率下计算标记胚胎组织。计算标记学会了识别一组空间标识符，这些空间标识符在注释以揭示该组织的核染色胚胎图像中唯一描述了特定组织的形态。然后，该技术仅使用核图像数据才能识别其他未注释的胚胎中的组织。一旦将核分配到特定的组织中，我们就可以使用针对每个组织优化的核分割方法鉴定核体积。这种方法被证明是必不可少的，因为组织优化的分割方法比同样应用于所有组织的单一通用方法更准确地执行。由此产生的部分地图集使我们能够表征胚胎和左/右不对称之间细胞数的变化，远比以前可能更详细，并为开发完整地图集设定了阶段。我们将通过1。建立方法来识别复杂的组织和细胞形状，2。建立三个发育阶段的胚胎地图集，以及3。使用活细胞方法来确定不同阶段的地图酶之间的细胞谱系。