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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

基本信息

批准号：
9306947
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
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-01 至 2020-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9306947
关键词：
Anatomy Animal Model Animals Apoptosis Atlases Biological Process Blastoderm Cell Count Cell Lineage Cell Nucleus Cell Shape Cell membrane Cells Complex Computational Technique Computer Simulation DNA Data Development Drosophila genus Drosophila melanogaster Embryo Embryonic Development Event Funding Gene Expression Gene Expression Profile Gene Targeting Generic Drugs Genetic Transcription Goals Grant Image Individual Label Learning Left 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 Staining method Stains Structure Study models System Systems Biology Techniques Tissue-Specific Gene Expression Tissues Variant Visualization software Work annotation system biological research cell motility computational atlas developmental genetics digital embryo tissue gene product high resolution imaging insight live cell imaging multidisciplinary nuclear imaging portability public health relevance tool transcription factor

项目摘要

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。其目标是将复杂的组织结构和基因表达模式量化为数字记录，从而可以对生物过程进行建模和研究。有丝分裂、凋亡、分化和形态发生运动导致复杂的胚胎形态，并且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的细胞与细胞之间的量不同。捕捉这些信息需要高分辨率的胚胎图像数据和复杂的计算技术来识别成千上万的细胞，这些细胞在大小，形状和组织类型上彼此不同。我们的目标是创造方法来产生一个定量的，细胞分辨率的基因表达和形态学的所有胚胎发育的果蝇。我们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-一个相对简单的，单层结构的6,000个细胞围绕一个蛋黄-我们创建了一个广泛的形态和基因表达图谱，揭示了以前未知的过程，如核运动。其次，在上一轮拨款的资助下，我们已经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。晚期胚胎明显比胚盘更复杂，并提出了新的挑战：需要更高分辨率的图像来分辨更密集的细胞，并且在核/细胞鉴定之前将每个核/细胞分配到特定的组织类型被证明是至关重要的。这最后一项任务是困难的，因为它是唯一实际的标记一个gven样品与一些特定的探针物理，但需要约70种不同的标签，以确定每个组织分别。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，以细胞分辨率计算标记胚胎组织。计算标记学习识别一组空间标识符，这些空间标识符唯一地描述了核染色胚胎图像中特定组织的形态，这些图像被注释以揭示该组织。然后，该技术能够仅使用核图像数据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。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等应用于所有组织的单一通用方法更准确。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。我们将继续这项工作1。建立识别复杂组织和细胞形状的方法，2。建立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。