BRAIN CONNECTS: PatchLink, scalable tools for integrating connectomes, projectomes, and transcriptomes

大脑连接：PatchLink，用于集成连接组、投影组和转录组的可扩展工具

• 批准号: 10665493
• 负责人: TIM M JARSKY
• 金额: $ 174.99万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10665493
• 关键词: Acceleration; Address; Adopted; Anatomy; Area; Automation; Axon; Basal Ganglia; Binding; Brain; Cells; Collaborations; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Dendrites; Detection; Development; Electron Microscopy; Electrophysiology (science); Gene Expression Profile; Generations; Genetic; Genomics; Human; Image; Imaging technology; Individual; Infrastructure; Interneurons; Link; Location; Machine Learning; Measures; Methods; Modality; Modeling; Modernization; Molecular; Morphology; Mus; Neurons; Positioning Attribute; Presynaptic Terminals; Property; Sampling; Science; Synapses; Techniques; Technology; Testing; Tissue imaging; Training; Vesicle; Work; brain circuitry; brain morphology; cell type; computer framework; computerized data processing; connectome; data standards; experience; experimental study; flexibility; improved; machine learning method; machine vision; multimodal data; multimodality; multiple datasets; novel strategies; open source; parallelization; patch clamp; patch sequencing; prevent; reconstruction; scale up; synaptic function; tissue processing; tool; transcriptome; transcriptomics

Project Summary / Abstract Upcoming brain-wide descriptions of synaptic connectivity are poised to transform our understanding of brain circuitry in the same way single-cell genomics has revolutionized our understanding of cell type diversity. The challenge of relating whole-brain wiring diagrams to cell-type genetic properties must be overcome in order to fully realize the potential of these datasets. Very few techniques generate multi-modality, "Rosetta stone" datasets needed to link cell types to connectivity, and none presently have the throughput to do so across an entire mammalian brain. In this proposal, we address key limitations that currently prevent such techniques from scaling to meet the throughput of whole-mouse-brain connectivity initiatives, and develop the computational frameworks needed to bind cell types to wiring diagrams. The Patch-seq method links the full gene expression profile of single neurons with their fundamental properties, including local morphology and electrophysiology1,2. In Aim 1, we will automate the Patch-seq technique to allow parallelization and scaling sufficient for whole mouse brain coverage. This will be achieved by integrating and optimizing recently developed methods for patch clamp automation, including pipette cleaning, cell detection, and machine learning approaches to cell identification and tracking. Developments will be fully documented and packaged for dissemination to lower barriers to access and further improve throughput via collaborative data generation. Similarly, methods for reconstructing the brain-wide full morphology of single neurons provides simultaneous access to their local morphology and long-range projection targets. In Aim 2, we will improve and extend the quality, efficiency, and capability of our automatic morphological reconstruction pipeline by adopting new approaches to reconstruction (e.g., a hierarchy of deep learners), and testing advanced methods for tissue processing and imaging across our Patch-seq and Full Morphology data generation pipelines. Automated reconstruction methods will be trained and tested on gold standard data. Tools and data will be collaboratively generated and publicly shared. In Aim 3, we will develop new computational frameworks to link whole-brain connectivity datasets to multi- modality cell type datasets. Powered by the throughput achieved in Aims 1 and 2, we will develop, apply, and share machine learning-based data analysis methods to synthesize the observations collected from individual platforms to achieve an integrated and predictive understanding of neuronal identity. This approach, which facilitates cell type assignment, cross-modality integration and inference, and characterization of the discreteness and continuity of fundamental cellular properties within and across types, will be scaled to achieve whole mouse brain coverage.

项目概要/摘要 即将到来的全脑突触连接描述将改变我们对大脑的理解 就像单细胞基因组学彻底改变了我们对细胞类型多样性的理解一样。这 必须克服将全脑接线图与细胞类型遗传特性联系起来的挑战，以便 充分发挥这些数据集的潜力。很少有技术能够产生多模态，“罗塞塔石碑” 将细胞类型与连接联系起来所需的数据集，但目前没有一个数据集具有跨网络这样做的吞吐量 整个哺乳动物的大脑。在本提案中，我们解决了目前阻止此类技术的关键限制 扩展以满足全鼠大脑连接计划的吞吐量，并开发计算 将单元类型绑定到接线图所需的框架。 Patch-seq 方法将单个神经元的完整基因表达谱与其基本特性联系起来， 包括局部形态学和电生理学1,2。在目标 1 中，我们将自动化 Patch-seq 技术，以允许 并行化和缩放足以覆盖整个小鼠大脑。这将通过整合和实现 优化最近开发的膜片钳自动化方法，包括移液器清洁、细胞检测、 以及用于细胞识别和跟踪的机器学习方法。进展情况将被完整记录并 打包传播以降低访问障碍并通过协作数据进一步提高吞吐量 一代。 类似地，重建单个神经元全脑完整形态的方法提供了同步 访问其局部形态和远程投影目标。在目标 2 中，我们将改进和扩展 通过采用新的技术，我们的自动形态重建管道的质量、效率和能力 重建方法（例如深度学习者的层次结构）以及测试组织的先进方法 跨我们的 Patch-seq 和完整形态学数据生成管道进行处理和成像。自动化 重建方法将在黄金标准数据上进行训练和测试。工具和数据将协同工作 生成并公开共享。 在目标 3 中，我们将开发新的计算框架，将全脑连接数据集与多脑连接数据集联系起来。 模态细胞类型数据集。在目标 1 和 2 中实现的吞吐量的支持下，我们将开发、应用和 分享基于机器学习的数据分析方法来综合从个人收集的观察结果 平台来实现对神经元身份的综合和预测性理解。这种做法， 促进细胞类型分配、跨模态集成和推理以及细胞的表征 类型内部和类型之间基本细胞特性的离散性和连续性将被缩放以实现 整个小鼠大脑的覆盖。