School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China.
SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
Bioinformatics. 2022 Oct 31;38(21):4987-4989. doi: 10.1093/bioinformatics/btac594.
Analysis of cell types is recognized as a major task in current single-cell genotyping and phenotyping. In neuroscience, 3-D neuron morphologies are often reconstructed from multi-dimensional microscopic images. Recent studies indicate that neurons could form very complicated distributions in the feature space, and thus they can be explored using manifold analysis. We have developed manifold classification toolkit software to replace the conventional clustering analysis to discover cell subtypes from three state-of-the-art collections of single neurons' 3-D morphologies that reconstructed from images. We have gathered 9208 3-D spatially registered whole mouse brain neurons from three datasets with the highest quality to date generated by the single neuron morphology community. To explore manifold distribution, our method uses minimum spanning tree-based principal skeletons to approximate locally linear embeddings, to explore the morphological feature spaces, which correspond to dendritic arbors, axonal arbors or both categories of arborization patterns of neurons. We show manifold classification is a suitable approach for a majority of often referred cell types, each of which was also discovered to contain multiple subtypes. Our results show an initial effort to employ manifold classification but not traditional clustering analysis as an alternative framework for analyzing 3-D neuron morphologies reconstructed from 3-D microscopic images.
Freely available at https://github.com/Mr-strlen/Cell_Pattern_Analysis_Tool.'
分析细胞类型被认为是当前单细胞基因分型和表型分析的主要任务。在神经科学中，三维神经元形态通常是从多维显微镜图像重建的。最近的研究表明，神经元在特征空间中可以形成非常复杂的分布，因此可以使用流形分析进行探索。我们开发了流形分类工具包软件，以取代传统的聚类分析，从三个最先进的单神经元三维形态重建图像集合中发现细胞亚型。我们收集了来自三个数据集的 9208 个三维空间注册的全鼠脑神经元，这些数据集是由单神经元形态学社区迄今为止生成的具有最高质量的数据集。为了探索流形分布，我们的方法使用基于最小生成树的主骨架来近似局部线性嵌入，以探索形态特征空间，这些特征空间对应于树突树、轴突树或这两类神经元分支模式。我们表明，流形分类是一种适合大多数常见细胞类型的方法，其中每种细胞类型都被发现包含多个亚型。我们的结果表明，我们首次尝试使用流形分类而不是传统的聚类分析作为从三维显微镜图像重建的三维神经元形态进行分析的替代框架。
可在 https://github.com/Mr-strlen/Cell_Pattern_Analysis_Tool 上免费获得。
