A computational framework for ultrastructural mapping of neural circuitry.
A computational framework for ultrastructural mapping of neural circuitry.
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DOI：
10.1371/journal.pbio.1000074
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发表时间：
2009-03
期刊：
影响因子：
9.8
通讯作者：
Marc RE
中科院分区：
文献类型：
--
作者：
Anderson JR;Jones BW;Yang JH;Shaw MV;Watt CB;Koshevoy P;Spaltenstein J;Jurrus E;U V K;Whitaker RT;Mastronarde D;Tasdizen T;Marc RE
后生动物神经系统的电路映射是困难的，因为典型的神经区域（包含所有组件的一个或多个副本的区域）很大，区域边界是不确定的，神经元的多样性很高，潜在的网络拓扑结构如此之多，只有解剖学上的基础事实才能解决它们。特定网络的完整映射需要突触分辨率、规范区域覆盖和鲁棒的神经元分类。虽然透射电子显微镜（TEM）仍然是网络映射的最佳工具，但由于需要精确拼接失真的图像块并配准失真的马赛克，因此构建大型连续切片TEM（ssTEM）图像体积的过程变得困难。此外，大多数分子神经元类标记物与最佳TEM成像不兼容。我们的目标是建立一个完整的框架，超微结构电路映射。该框架将强大的TEM兼容小分子分析与自动图像拼接、自动切片到切片图像配准和千兆字节级图像浏览相结合，以进行体积注释。具体来说，我们展示了如何将分子分析数据集及其所得的分类图嵌入ssTEM数据集，以及如何使用脚本采集工具（SerialEM），镶嵌和注册（IR工具）和大切片查看器（MosaicBuilder，维京海盗）来管理TB级体积。这些方法可以对新数据和旧数据进行大规模的连接分析。在适定任务中（例如，视网膜中的完整网络映射），以前需要数十年组装的TB级图像量现在可以在数月内完成。也许更重要的是，融合的分子分析，图像采集SerialEM，ir-tools卷组装，和数据查看器/注释器也允许ssTEM被用作一个前瞻性的工具，发现在非神经系统和一个实用的筛选方法神经遗传学。最后，这个框架提供了一个机制，在国际用户群的ssTEM成像，体积组装和数据分析的并行化，提高生产力的一大群电子显微镜。建立脊椎动物神经系统的精确神经网络图是神经科学的一个主要挑战。不同的神经元群体共同发挥作用，形成复杂的连接模式，通常跨越大脑组织的大区域，边界不确定。虽然连续切片透射电子显微镜仍然是精细解剖分析的最佳工具，但这项工作的时间和成本令人望而却步。我们已经组装了一个完整的框架，使用传统的透射电子显微镜，大大加快图像分析的超微结构映射。该框架结合了小分子分析来对细胞进行分类、自动图像采集、自动马赛克形成、自动切片到切片图像配准以及用于体积注释的大规模图像浏览。需要数十年或更长时间手动组装的TB级图像卷现在可以在几个月内自动构建。这使得连续切片透射电子显微镜实用的所有复杂的组织系统（神经或非神经）的高分辨率探索，以及遗传模型的超微结构筛选。TB级连续切片透射电子显微镜（ssTEM）数据集的分析框架克服了计算障碍，加速了高分辨率组织分析，为映射复杂的神经回路提供了一种实用的方法，并为神经遗传学提供了有效的筛选工具。
Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components) are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM) remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM) image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM), mosaicking and registration (ir-tools), and large slice viewers (MosaicBuilder, Viking) can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina), terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally, this framework provides a mechanism for parallelization of ssTEM imaging, volume assembly, and data analysis across an international user base, enhancing the productivity of a large cohort of electron microscopists. Building an accurate neural network diagram of the vertebrate nervous system is a major challenge in neuroscience. Diverse groups of neurons that function together form complex patterns of connections often spanning large regions of brain tissue, with uncertain borders. Although serial-section transmission electron microscopy remains the optimal tool for fine anatomical analyses, the time and cost of the undertaking has been prohibitive. We have assembled a complete framework for ultrastructural mapping using conventional transmission electron microscopy that tremendously accelerates image analysis. This framework combines small-molecule profiling to classify cells, automated image acquisition, automated mosaic formation, automated slice-to-slice image registration, and large-scale image browsing for volume annotation. Terabyte-scale image volumes requiring decades or more to assemble manually can now be automatically built in a few months. This makes serial-section transmission electron microscopy practical for high-resolution exploration of all complex tissue systems (neural or nonneural) as well as for ultrastructural screening of genetic models. A framework for analysis of terabyte-scale serial-section transmission electron microscopic (ssTEM) datasets overcomes computational barriers and accelerates high-resolution tissue analysis, providing a practical way of mapping complex neural circuitry and an effective screening tool for neurogenetics.
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影响因子：
16.2
作者：
Deans, MR;Volgyi, B;Paul, DL
通讯作者：
Paul, DL
影响因子：
2.5
作者：
Eglen, SJ;Raven, MA;Reese, BE
通讯作者：
Reese, BE
DOI：
10.1111/j.1365-2818.2005.01466.x
发表时间：
2005-04-01
期刊：
JOURNAL OF MICROSCOPY-OXFORD
影响因子：
--
作者：
Fiala, JC
通讯作者：
Fiala, JC
影响因子：
9.8
作者：
Denk W;Horstmann H
通讯作者：
Horstmann H
影响因子：
64.8
作者：
Calkins, DJ;Sterling, P
通讯作者：
Sterling, P