Modularly built, complete, coordinate- and template-free brain atlases

模块化构建、完整、无坐标和模板的大脑图谱

• 批准号: 10570256
• 负责人: Hang Lu
• 金额: $ 65.97万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-10 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10570256
• 关键词: Address; Adult; Age; Aging; Algorithms; Amaze; Anatomy; Animals; Atlases; Benchmarking; Biological; Brain; Caenorhabditis elegans; Calibration; Cells; Collection; Complex; Data; Data Set; Democracy; Development; Disease; Ensure; Equipment; Functional Imaging; Future; Gene Expression Profiling; Genetic; Goals; Graph; Hand; Hermaphroditism; Human; Image; Individual; Individual Differences; Knowledge; Label; Laboratories; Larva; Letters; Literature; Machine Learning; Manuals; Maps; Methods; Modeling; Molecular; Nature; Nervous System; Neuronal Differentiation; Neurons; Organ; Organism; Outcome; Output; Phenotype; Population; Production; Reporter; Resolution; Sampling; Standardization; Stereotyping; Structure; System; Techniques; Tissues; Transgenic Animals; Transgenic Organisms; Update; Work; computational pipelines; crowdsourcing; design; digital; experimental study; flexibility; graph theory; imaging biomarker; imaging modality; improved; in vivo; in vivo imaging; individual variation; innovation; instrument; interest; large datasets; machine learning algorithm; medical specialties; model organism; neural circuit; neurodevelopment; prevent; sex; tool; young adult

Project Summary Anatomical atlases are spatial reference maps of cells in tissues/organs/brains and provide structure information for a wide range of biological analyses. The anatomical atlas of C. elegans nervous system is the only atlas for the entire nervous system of an animal with a resolution of all neuronal classes. However, built on a limited dataset and manual annotations, the standard atlas is insufficient in capturing biological variabilities, inaccurate and difficult to use for cell identification routinely, and only applicable for wildtype adult. While several heroic efforts of generating and imaging marker strains to build atlases have much improved the atlases, there is still a need for a pipeline to build accurate genetic-background-specific (or experimental-condition-specific) atlases easily and cheaply; further, there is a need to build such atlases that can be used without specialized equipment and with as few genetic perturbations as possible. Recent development of machine learning techniques and molecular transgenic approaches enabling the systematic production of in vivo reporters and imaging methods capable of collecting and processing high-resolution datasets at a large scale. The goal of this application is to address the current bottleneck by establishing a combined experimental and computational pipeline for modularly built, complete, coordinate- and template-free brain atlases for democratized and flexible uses. By imaging in vivo markers in a large number of live animals, the project will generate complete anatomical atlases for the C. elegans nervous system that capture variability in the population, which will greatly enhance the accuracy of identity predictions when used on each animal. The project will generate a collection of transgenic animals expressing partly overlapping in vivo markers that cover all neurons and build a computational pipeline to assemble the atlases. Further, a few widely applicable developmental atlases as a direct output of the project will showcase the pipeline and the approach. Importantly, the atlases do not seek to provide a set of rigid coordinates for each neuron class, but instead, a set of constraints that can be used to provide best estimates of neuron identities for each new sample. This ensures accuracy and applicability of the atlases to specific use case. The building of whole-brain atlases is piece-wise from easily-obtained partial atlases, and can be crowd-sourced if desired. The use will be streamlined with image input and neuron-identity prediction output. The proposed project is innovative, because it will build the first complete anatomical atlases of a nervous system using large datasets collected from in vivo markers of many live animals; it uses relational information uniquely suited to provide more accurate assignments; it will capture variabilities among individual animals. The proposed the work is significant, because it will address the urgent and unmet need for accurate, easy-to-use and easily updatable atlases to curate the brain; further, it will develop and establish conceptual framework and techniques for similar efforts in more complex anatomical systems.

项目摘要 解剖图谱是组织/器官/大脑中细胞的空间参考图，并提供结构 各种生物学分析的信息。秀丽隐杆线虫神经系统的解剖图谱是 仅适用于具有所有神经元类别的动物的整个神经系统。但是，建立在 标准地图集是有限的数据集和手动注释，不足以捕获生物学变异性， 通常不准确且难以用于细胞识别，仅适用于野生型成人。而几个 产生和成像标记菌株以建造地图集的英勇努力已经大大改善了地图集 仍然需要管道来构建准确的遗传背景特异性（或实验条件特异性） 轻松，便宜地地图集；此外，有必要构建可以在没有专业化的情况下使用的地图 设备和尽可能少的遗传扰动。机器学习的最新发展 技术和分子转基因方法实现了体内记者的系统生产和 能够大规模收集和处理高分辨率数据集的成像方法。目标的目标 应用是通过建立合并的实验和计算来解决当前的瓶颈 用于模块化，完整，坐标和无模板的大脑图书馆的管道，以民主化和 灵活用途。通过在许多活动物中成像体内标记，该项目将产生完整 C.秀丽隐杆线虫神经系统的解剖图谱，捕获人群的变异性，这将大大 在每只动物上使用时，可以提高身份预测的准确性。该项目将产生集合 转基因动物在体内标记中部分重叠，覆盖所有神经元并建立一个 计算管道组装地图集。此外，一些广泛适用的发展图集 该项目的直接输出将展示管道和方法。重要的是，这些地图集并不试图 为每个神经元类提供一组刚性坐标 为每个新样本提供最佳的神经元身份估计。这确保了准确性和适用性 特定用例的地图集。从易于体现的部分中，整个脑图的建造都是零件 地图集，如果需要，可以众包。使用图像输入和神经元身份将简化使用 预测输出。拟议的项目具有创新性，因为它将建立第一个完整的解剖图谱 使用从许多活动物的体内标记中收集的大型数据集的神经系统；它使用关系 非常适合提供更准确作业的信息；它将捕获个人之间的变异性 动物。提议的工作很重要，因为它将满足准确的紧急和未满足的需求 易于使用且易于更新的地图集，以策划大脑；此外，它将发展并建立概念 在更复杂的解剖系统中采取类似努力的框架和技术。