Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain.
Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain.
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DOI：
10.7554/elife.61408
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发表时间：
2021-02-11
期刊：
影响因子：
7.7
通讯作者：
Sanders SJ
中科院分区：
文献类型：
--
作者：
Young DM;Fazel Darbandi S;Schwartz G;Bonzell Z;Yuruk D;Nojima M;Gole LC;Rubenstein JL;Yu W;Sanders SJ
3D成像数据需要3D参考地图集进行准确的定量解释。从2D衍生的地图集生成3D地图集的现有计算方法导致大量的工件，而手动策展方法是劳动密集型的。我们提出了一种计算方法，用于3D图谱的建设，大大减少伪影识别解剖边界的基础成像数据，并使用这些来指导3D变换。解剖学边界也允许扩展图谱以完成边缘区域。将这些方法应用于艾伦小鼠脑发育图谱（ADMBA）中的八个发育阶段，产生了更全面、更准确的图谱。我们从15个完整的小鼠大脑中生成成像数据，以验证图谱的性能，并观察到定性和定量的改善（图谱和解剖边界之间的对齐增加了37%）。我们提供的管道MagellanMapper软件和八个三维重建ADMBA地图集。这些资源促进了样本之间和整个开发过程中的全器官定量分析。研究界需要精确、可靠的3D器官图谱来确定生物结构和过程的位置。例如，这些图谱对于理解特定基因在哪里被打开或关闭，或者随着时间的推移不同细胞群的空间组织至关重要。几个世纪以来，地图集一直是通过将器官“切片”，然后精确地表示每个2D层来构建的。然而，这种方法是不完美的：每一层可能是准确的，但不可避免的不匹配出现在切片之间时，从3D或从另一个角度来看。显微镜的进步现在允许整个器官以3D成像。将这些图像与地图集进行比较，可以帮助发现指示或潜在疾病的细微差异。然而，这只有在3D地图是准确的并且不具有层之间的不匹配时才是可能的。要创建没有这些伪影的地图集，一种方法是从头开始，手动重新绘制3D地图，这是一种劳动密集型方法，会丢弃大量已建立的地图集。相反，Young等人开始创建一种自动化方法，可以帮助改进现有的“基于图层”的地图集，发布任何人都可以用来改进当前地图的软件。该软件包是通过利用艾伦开发小鼠大脑图谱中的八个图谱创建的，然后使用底层解剖图像来解决层之间的差异或填充任何缺失的区域。该软件被称为MagellanMapper，经过广泛测试，以证明其创建的地图的准确性，包括与15只小鼠大脑的全脑成像数据进行比较。有了这个新软件，研究人员可以提高他们的地图集的准确性，帮助他们在细胞水平上了解器官的结构，并让他们深入了解各种人类疾病。
3D imaging data necessitate 3D reference atlases for accurate quantitative interpretation. Existing computational methods to generate 3D atlases from 2D-derived atlases result in extensive artifacts, while manual curation approaches are labor-intensive. We present a computational approach for 3D atlas construction that substantially reduces artifacts by identifying anatomical boundaries in the underlying imaging data and using these to guide 3D transformation. Anatomical boundaries also allow extension of atlases to complete edge regions. Applying these methods to the eight developmental stages in the Allen Developing Mouse Brain Atlas (ADMBA) led to more comprehensive and accurate atlases. We generated imaging data from 15 whole mouse brains to validate atlas performance and observed qualitative and quantitative improvement (37% greater alignment between atlas and anatomical boundaries). We provide the pipeline as the MagellanMapper software and the eight 3D reconstructed ADMBA atlases. These resources facilitate whole-organ quantitative analysis between samples and across development. The research community needs precise, reliable 3D atlases of organs to pinpoint where biological structures and processes are located. For instance, these maps are essential to understand where specific genes are turned on or off, or the spatial organization of various groups of cells over time. For centuries, atlases have been built by thinly ‘slicing up’ an organ, and then precisely representing each 2D layer. Yet this approach is imperfect: each layer may be accurate on its own, but inevitable mismatches appear between the slices when viewed in 3D or from another angle. Advances in microscopy now allow entire organs to be imaged in 3D. Comparing these images with atlases could help to detect subtle differences that indicate or underlie disease. However, this is only possible if 3D maps are accurate and do not feature mismatches between layers. To create an atlas without such artifacts, one approach consists in starting from scratch and manually redrawing the maps in 3D, a labor-intensive method that discards a large body of well-established atlases. Instead, Young et al. set out to create an automated method which could help to refine existing ‘layer-based’ atlases, releasing software that anyone can use to improve current maps. The package was created by harnessing eight atlases in the Allen Developing Mouse Brain Atlas, and then using the underlying anatomical images to resolve discrepancies between layers or fill out any missing areas. Known as MagellanMapper, the software was extensively tested to demonstrate the accuracy of the maps it creates, including comparison to whole-brain imaging data from 15 mouse brains. Armed with this new software, researchers can improve the accuracy of their atlases, helping them to understand the structure of organs at the level of the cell and giving them insight into a broad range of human disorders.
影响因子：
4.8
作者：
Li B;Christensen GE;Hoffman EA;McLennan G;Reinhardt JM
通讯作者：
Reinhardt JM