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.

项目摘要 /摘要 在整个发展和成年期间，人脑建立了由神经回路组成的 数千种类型的神经元。随着新神经元的诞生，它们被纳入发展和现有 与附近的神经元以及大脑遥远部分的神经元建立连接的电路。 许多神经系统条件与大脑网络的增长不当有关。但是，我们缺乏基本 了解随着新神经元的加入，了解神经回路的变化。为了解决这个问题，该建议使用 一种新型的动物模型，软体动物，卑鄙 整个大脑的突触级接线图在几个少年阶段和成人阶段。使用这些 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 网络作为大脑中神经元的数量增加了40倍以上。 神经元将通过使用多达五个原位的基因表达的相交标记来鉴定基因表达 杂交链反应探针标记了不同的mRNA序列。重叠的探针集将使用 因此，可以根据其基因模式来区分单独识别的神经元和神经元类型 表达结合其SOMA位置和大小。此外，在成年动物中，神经元将被标记 使用用于从大脑发出的神经的荧光示踪剂。机器学习（ML）将用于 根据所有这些功能对神经元类型进行分类。跨发育性神经元的ML分类 人类将纠正阶段，以增强ML的预测能力。 将构建成人和少年四个阶段的少年的一系列连接组。这 大脑将被连续切开。 30 nm厚的部分中的每一个将使用61束扫描来成像 电子显微镜。这些部分将被对齐，所有神经元将自动重建3D。这 重建将包括所有轴突，树突和突触。同样，人类会校对结果 更正ML算法。结果将是五个完整的脑连接组，这些脑连接来自早期的少年 成熟成人有500个神经元，有23,000多个神经元。 将分析发展系列的系列，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。确定神经元的神经结构的变化将是 追踪开发。随着新神经元的添加，将进行比较。 将确定用于视觉，嗅觉和运动系统的完整神经电路。最后，该项目将 确定轮毂是否围绕最古老的神经元发展，还是网络尺度不集中的尺度 特定集线器的连接性。结果将提供前所未有的观察 随着新神经元的添加，整个大脑变化的神经元。