A Connectomic Analysis of a Developing Brain Undergoing Neurogenesis

正在经历神经发生的发育中大脑的连接组学分析

• 批准号: 10719296
• 负责人: Paul S Katz
• 金额: $ 67.36万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10719296
• 关键词: 3-Dimensional; Address; Adolescent; Adult; Animal Model; Animals; Atlases; Axon; Brain; Classification; Computers; Data Set; Dendrites; Development; Distant; Efferent Neurons; Electron Microscope; Electron Microscopy; Electrons; Enzymes; Eye; Ganglia; Gene Expression; Gene Expression Profile; Generations; Growth; Human; Image; In Situ Hybridization; Individual; Label; Learning; Location; Machine Learning; Maps; Messenger RNA; Motor; Nerve; Neurologic; Neurons; Neurophysiology - biologic function; Neurosciences; Neurotransmitters; Organism; Phenotype; Process; Reaction; Rest; Scanning; Sensory; Series; Standard Model; Structure; Synapses; System; Testing; Thick; Time; Tracer; Vertebrates; Visual; automated segmentation; cell type; connectome; course development; insight; machine learning algorithm; machine learning classification; mature animal; microscopic imaging; model organism; motor control; nervous system disorder; network architecture; neural; neural circuit; neurodevelopment; neurogenesis; neuron development; neuronal cell body; novel; postsynaptic; presynaptic; reconstruction; sample fixation; synaptogenesis; transcription factor

PROJECT SUMMARY / ABSTRACT Over the course of the development and into adulthood, the human brain builds neural circuits composed of thousands of types of neurons. As new neurons are born, they are incorporated into developing and existing circuits making connections to neurons that are nearby as well as neurons that are in distant parts of the brain. Many neurological conditions are related to the improper growth of networks in the brain. Yet, we lack a basic understanding of how neural circuits change as new neurons join. To address this question, this proposal uses a novel animal model, the mollusc, Berghia stephanieae, in which it is possible to construct a cellular- and synaptic-level wiring diagram of the entire brain at several juvenile stages as well as the adult. Using these whole brain connectomes, the project will track the changes in specific neurons, in neural circuits, and in whole brain networks as the number of neurons in the brain increases by over 40-fold. Neurons will be identified by intersectional labeling of gene expression using sets of up to five in situ hybridization chain reaction probes that label different mRNA sequences. Overlapping sets of probes will used so that individually identifiable neurons and neuron types can be distinguished based on their patterns of gene expression combined with their soma location and size. Additionally, in adult animals, neurons will be labeled using fluorescent tracers applied to nerves emanating from the brain. Machine learning (ML) will be employed to classify neuronal types based on all of these features. ML classifications of neurons across developmental stages will be corrected by humans to enhance the predictive power of the ML. A series of connectomes of the brains of an adult and juveniles from four stages will be constructed. The brain will be serially sectioned. Each of the 30 nm thick sections will be imaged using a 61 beam scanning electron microscope. The sections will be aligned and all neurons will be automatically reconstructed in 3D. The reconstructions will include all axons, dendrites, and synapses. Again, humans will proofread the results to correct the ML algorithm. The result will be five complete brain connectomes spanning from the early juvenile with 500 neurons to the mature adult with over 23,000 neurons. The developmental series will be analyzed to test hypotheses about the organization and development of neurons, neural circuits, and entire brain networks. Changes in neural structure of identified neurons will be tracked over development. Comparisons will be made between neural types as new neurons are added. Complete neural circuitry for visual, olfactory, and motor systems will be determined. Finally, the project will determine whether hubs develop around the oldest neurons or whether the network scales without concentrating connectivity at particular hubs. The results will provide an unprecedented look at how the synaptic networks of neurons across an entire brain change as new neurons are added.

项目概要/摘要 在发育和成年过程中，人脑构建了由以下部分组成的神经回路： 数千种类型的神经元。随着新神经元的诞生，它们被纳入正在发育和现有的神经元中。 与大脑附近的神经元以及远处的神经元建立连接的电路。 许多神经系统疾病与大脑网络的不当生长有关。然而，我们缺乏一个基本的 了解当新神经元加入时神经回路如何变化。为了解决这个问题，本提案使用 一种新的动物模型，软体动物，Berghia stephanieae，在其中可以构建细胞和 整个大脑在几个青少年阶段以及成人阶段的突触级接线图。使用这些整体 大脑连接组，该项目将跟踪特定神经元、神经回路和整个大脑的变化 随着大脑神经元数量增加 40 倍以上。 将使用最多五个原位组通过基因表达的交叉标记来识别神经元 杂交链式反应探针标记不同的 mRNA 序列。将使用重叠的探针组 以便可以根据基因模式区分可单独识别的神经元和神经元类型 表达与其体细胞位置和大小相结合。此外，在成年动物中，神经元将被标记 使用应用于大脑发出的神经的荧光示踪剂。机器学习（ML）将被用于 根据所有这些特征对神经元类型进行分类。跨发育神经元的 ML 分类 阶段将由人类纠正，以增强机器学习的预测能力。 将构建四个阶段的成人和青少年大脑的一系列连接体。这 大脑将被连续切片。每个 30 nm 厚的切片将使用 61 光束扫描进行成像 电子显微镜。切片将对齐，所有神经元将自动以 3D 形式重建。这 重建将包括所有轴突、树突和突触。同样，人类将校对结果 修正机器学习算法。结果将是五个完整的大脑连接体，跨越早期青少年 从拥有 500 个神经元到拥有超过 23,000 个神经元的成熟成人。 将分析发展系列以检验有关组织和发展的假设 神经元、神经回路和整个大脑网络。已识别神经元的神经结构的变化将是 跟踪开发。随着新神经元的添加，将在神经类型之间进行比较。 将确定视觉、嗅觉和运动系统的完整神经回路。最后，该项目将 确定中枢是否围绕最古老的神经元发展，或者网络是否在不集中的情况下扩展 特定枢纽的连接。研究结果将为我们提供前所未有的视角，让我们了解突触网络如何 随着新神经元的添加，整个大脑的神经元会发生变化。