A Morphology and Gene Expression Atlas for Drosophila Embryogenesis

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。目标是量化复杂的组织结构和基因表达模式，作为生物过程可以建模和研究的数字记录。有丝分裂、凋亡、分化和形态发生运动导致了复杂的胚胎形态，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都不同。获取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数千个细胞，这些细胞在大小、形状和组织类型上各不相同。我们的目标是创建一种方法，以产生一个定量的，细胞分辨率的基因表达和形态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，对于早期胚皮胚胎——一个相对简单的单层结构，由6000个细胞围绕着一个蛋黄——我们创建了一个广泛的形态学和基因表达图谱，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的幼虫器官。晚期胚胎比囊胚复杂得多，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，并且在细胞核/细胞鉴定之前必须将每个细胞核/细胞分配到特定的组织类型。最后一项任务是困难的，因为用几个特定的探针物理标记给定样品是可行的，但是需要大约70个不同的标签来单独识别每个组织。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染色图像和系统的丰富形态来计算标记胚胎组织的细胞分辨率。计算标记学习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，以显示该组织。然后，该技术仅使用核图像数据就可以识别其他未注释胚胎中的组织。一旦细胞核被分配到一个特定的组织，我们就能够使用针对每个组织优化的核分割方法来识别核体积。这种方法被证明是必不可少的，因为组织优化分割方法比适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，并为完整图谱的开发奠定了基础。我们将在1点之前继续这项工作。建立识别复杂组织和细胞形状的方法；3.建立三个发育阶段的胚胎地图集；使用活细胞方法确定不同阶段地图集之间的细胞系。

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.