Simultaneous high-throughput functional, transcriptomic and connectivity profiling using FUNseq

使用 FUNseq 同时进行高通量功能、转录组和连接分析

• 批准号: 10413650
• 负责人: Andreas Tolias
• 金额: $ 381.62万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10413650
• 关键词: Algorithms; Anatomy; Animals; Area; Axon; BRAIN initiative; Bar Codes; Behavior; Big Data; Brain; Cells; Cognition; Complex; Data; Development; Dimensions; Disease; Distant; Electron Microscopy; Fingerprint; Functional Imaging; Genetic; Goals; In Situ; In Vitro; Individual; Link; Machine Learning; Maps; Measurement; Measures; Mental disorders; Methodology; Methods; Multiplexed Analysis of Projections by Sequencing; Mus; Neocortex; Neurons; Neurosciences; Pathologic; Pattern; Physiological; Population; Property; RNA; Resolution; Role; Silicon; Silicon Dioxide; Source; Structure; Techniques; Technology; Time; Visual Cortex; Work; analysis pipeline; area striata; awake; cell type; cost; cost effective; deep learning; deep learning model; density; design; experimental analysis; experimental study; in vivo; machine learning method; multimodality; multiphoton imaging; neural circuit; novel; patch sequencing; predictive modeling; reconstruction; relating to nervous system; scaffold; single cell sequencing; single-cell RNA sequencing; tool; transcriptome; transcriptome sequencing; transcriptomics

Project Summary Recent advances in technology driven by the BRAIN Initiative have yielded new methods for characterizing the ac- tivity, transcriptome, and microscale anatomy of neurons throughout the brain, and have increased the throughput of these techniques by orders of magnitude. It is currently possible to record the simultaneous activity of popu- lations of neurons on the order of tens of thousands of neurons in awake behaving animals in vivo using large field of view multiphoton imaging or high density silicon probes, and new machine learning methods are enabling more comprehensive functional characterization than ever before. Transcriptomic profiling can be accomplished at scales of tens or hundreds of thousands of neurons in vitro. Finally the microscale anatomy of axonal pro- jections and connections across the brain can also be assessed in tens of thousands of neurons in the same animal using dense electron microscopy reconstruction for local circuits, or (as we propose here) RNA barcoding methods for local and long-range axonal projections. Each of these techniques on their own can provide im- portant clues about the diversity of neurons and their organization into canonical circuits with specific functional, transcriptomic, or axonal projection profiles. While these techniques are all being pushed forward independently, they remain effectively siloed from each other, precluding multi-modal characterization of the same neurons in the same animal. Developing a comprehensive pipeline to characterize transcriptomic, axonal projections and in vivo functional fingerprints as we propose to do here would enable synergistic analyses of cell-type composition across these multiple dimensions. Finally, because of the low cost and high throughput of this approach, experi- ments using this novel pipeline could be repeated many times in different animals to answer pressing questions about how the relationship between the function, structure, and transcriptome of neurons changes across devel- opmental or disease states. In this proposal, we will leverage our team's combined expertise in in vivo functional imaging and Machine Learning to characterize the complex functional properties of neurons in primary visual cortex of the mouse, and novel sequencing techniques developed by PI Zador to combine transcriptomic profiling with RNA barcoding to measure single-neuron projection patterns throughout the brain at axonal resolution.

项目摘要 由BRAIN计划驱动的技术的最新进展产生了表征交流的新方法， 整个大脑的神经元的活性，转录组和微观解剖，并增加了吞吐量 这些技术的数量级。目前，可以记录popu的同时活动， 在清醒行为的动物体内使用大的神经元， 视野多光子成像或高密度硅探针，以及新的机器学习方法， 比以往任何时候都更全面的功能表征。转录组学分析可以通过 在体外的数万或数十万个神经元的规模。最后，轴突前体的显微解剖， 大脑中的喷射和连接也可以在同一个神经元中的数万个神经元中进行评估。 动物使用密集电子显微镜重建局部电路，或（如我们在这里提出的）RNA条形码 局部和远程轴突投射的方法。这些技术中的每一种都可以提供IM- 关于神经元多样性及其组织成具有特定功能的规范电路的重要线索， 转录组或轴突投射轮廓。虽然这些技术都是独立推进的， 它们仍然有效地彼此孤立，排除了相同神经元的多模态表征， 同样的动物。开发一个全面的管道，以表征转录组，轴突投射和 我们在这里提出的体内功能指纹将能够协同分析细胞类型组成， 在这些多个维度上。最后，由于这种方法的低成本和高吞吐量， 使用这种新颖的管道可以在不同的动物身上重复多次，以回答紧迫的问题。 关于神经元的功能、结构和转录组之间的关系如何在发育过程中发生变化， 精神或疾病状态。在本提案中，我们将利用我们团队在体内功能性 成像和机器学习来表征初级视觉中神经元的复杂功能特性 小鼠皮质，以及PI Zador开发的联合收割机结合转录组学分析的新测序技术 用RNA条形码以轴突分辨率测量整个大脑的单神经元投射模式。