Koonce Noelle L, Emerson Sarah E, Bhaskar Dhananjay, Kuchroo Manik, Moyle Mark W, Arroyo-Morales Pura, Martínez Nabor Vázquez, Krishnaswamy Smita, Mohler William, Colón-Ramos Daniel
Department of Neuroscience and Department of Cell Biology, Wu Tsai Institute, Yale University, New Haven, CT, USA.
Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
bioRxiv. 2024 Oct 24:2024.08.27.609993. doi: 10.1101/2024.08.27.609993.
Volume electron microscopy (vEM) datasets such as those generated for connectome studies allow nanoscale quantifications and comparisons of the cell biological features underpinning circuit architectures. Quantifications of cell biological relationships in the connectome result in rich multidimensional datasets that benefit from data science approaches, including dimensionality reduction and integrated graphical representations of neuronal relationships. We developed NeuroSCAN, an online open-source platform that bridges sophisticated graph analytics from data science approaches with the underlying cell biological features in the connectome. We analyze a series of published brain neuropils and demonstrate how these integrated representations of neuronal relationships facilitate comparisons across connectomes, catalyzing new insights on the structure-function relationships of the circuits and their changes during development. NeuroSCAN is designed for intuitive examination and comparisons across connectomes, enabling synthesis of knowledge from high-level abstractions of neuronal relationships derived from data science techniques to the detailed identification of the cell biological features underpinning these abstractions.
诸如为连接组研究生成的那些体积电子显微镜（vEM）数据集，能够对支撑电路架构的细胞生物学特征进行纳米级定量和比较。连接组中细胞生物学关系的定量产生了丰富的多维数据集，这些数据集受益于数据科学方法，包括降维和神经元关系的综合图形表示。我们开发了NeuroSCAN，这是一个在线开源平台，它将数据科学方法中的复杂图分析与连接组中的潜在细胞生物学特征联系起来。我们分析了一系列已发表的脑髓质，并展示了这些神经元关系的综合表示如何促进跨连接组的比较，催生关于电路结构 - 功能关系及其在发育过程中变化的新见解。NeuroSCAN旨在用于跨连接组的直观检查和比较，能够将从数据科学技术得出的神经元关系的高级抽象知识与支撑这些抽象的细胞生物学特征的详细识别进行综合。
