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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要解剖图谱是组织/器官/大脑中细胞的空间参考图，并提供结构用于广泛的生物分析的信息。线虫神经系统的解剖图谱是唯一一个动物整个神经系统的图谱，具有所有神经元类别的分辨率。然而，建立在由于数据集和手动注释有限，标准图谱不足以捕捉生物变异性，不准确且难以用于常规细胞鉴定，并且仅适用于野生型成体。虽然有几个生成和成像标记菌株以构建图谱的英勇努力极大地改进了图谱，仍然需要建立准确的遗传背景特定（或实验条件特定）的管道轻松且便宜地制作地图集；此外，需要建立这样的地图集，无需专门的人员即可使用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能够系统地生产体内报告基因和能够大规模收集和处理高分辨率数据集的成像方法。此举的目标应用程序是通过建立一个组合的实验和计算来解决当前的瓶颈用于模块化构建、完整、无坐标和模板的大脑图谱的管道，用于民主化和灵活运用。通过对大量活体动物体内标记物进行成像，该项目将生成完整的线虫神经系统的解剖图谱，捕捉种群的变异性，这将极大地当用于每只动物时提高身份预测的准确性。该项目将生成一个集合转基因动物表达部分重叠的体内标记，覆盖所有神经元并构建组装地图集的计算管道。此外，一些广泛适用的发育图谱作为该项目的直接输出将展示管道和方法。重要的是，这些地图集并不旨在为每个神经元类提供一组刚性坐标，而是一组可用于为每个新样本提供神经元身份的最佳估计。这确保了准确性和适用性将地图集映射到特定用例。全脑图谱的构建是从容易获得的部分图谱中分段构建的地图集，如果需要的话可以众包。通过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，因为它将构建第一个完整的解剖图谱使用从许多活体动物的体内标记收集的大型数据集来研究神经系统；它使用关系特别适合提供更准确作业的信息；它将捕获个体之间的差异动物。拟议的工作意义重大，因为它将解决对准确的、未满足的迫切需求。易于使用且易于更新的地图集来管理大脑；此外，它将发展和建立概念在更复杂的解剖系统中进行类似工作的框架和技术。