A Connectomic Analysis of a Developing Brain Undergoing Neurogenesis

项目摘要/摘要 在发育和成年的过程中，人脑建立了由以下组成的神经回路 数以千计的神经元。随着新神经元的诞生，它们被合并到正在发育和存在的 连接到附近神经元和大脑远处神经元的电路。 许多神经疾病与大脑中网络的不正常生长有关。然而，我们缺乏一个基本的 了解神经回路如何随着新神经元的加入而变化。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，在其中可以构建一种细胞和 整个大脑在几个幼年阶段和成年阶段的突触水平的接线图。使用这些完整的 大脑连接，该项目将跟踪特定神经元、神经回路和整个大脑的变化 当大脑中的神经元数量增加40倍以上时，网络。 神经元将通过使用最多五组原位基因表达的交叉标记来识别 标记不同信使核糖核酸序列的杂交链式反应探针。将使用重叠的探针组 从而可以根据它们的基因模式来区分单独可识别的神经元和神经元类型 结合它们胞体的位置和大小来表达。此外，在成年动物中，神经元将被标记 使用荧光示踪剂对大脑发出的神经进行追踪。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。不同发育阶段神经元的ML分类 人类将对阶段进行修正，以增强ML的预测能力。 将从四个阶段构建一系列成人和青少年的大脑连接。这个 大脑将被连续切片。每个30 nm厚的切片都将使用61光束扫描进行成像 电子显微镜。切片将被对齐，所有神经元将在3D中自动重建。这个 重建将包括所有轴突、树突和突触。再一次，人类将校对结果以 修正最大似然算法。结果将得到五个完整的大脑连接，从早期的青少年 成熟的成年人有500个神经元，超过23,000个神经元。 将对发展系列进行分析，以检验关于组织和发展的假设 神经元、神经回路和整个大脑网络。已识别的神经元的神经结构的变化将是 对发展进行了跟踪。随着新神经元的加入，将对神经类型进行比较。 将确定视觉、嗅觉和运动系统的完整神经电路。最后，该项目将 确定中枢是否围绕最老的神经元发展，或者网络是否在不集中的情况下进行扩展 特定枢纽的连接性。这一结果将提供一个前所未有的视角，了解大脑中的突触网络如何 随着新神经元的加入，整个大脑中的神经元都会发生变化。

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.