Feng Zhao, Li Xiangning, Luo Yue, Liu Xin, Long Ben, Jiang Tao, Jia Xueyan, Chen Xiaowei, Luo Jie, Chai Xiaokang, Wang Zhen, Ren Miao, Lu Xin, Yao Gang, Zhao Mengting, Li Yuxin, Liu Zhixiang, Ni Hong, Dou Chuhao, Bao Shengda, Yang Shicheng, Zhang Zoutao, Zhou Jiandong, Cai Lingyi, Zhang Qi, Tudi Ayizuohere, Tan Chaozhen, Xu Zhengchao, Chen Siqi, Ding Wenxiang, Shi Wenjuan, Li Anan, Dong Hong-Wei, Gong Hui, Luo Qingming
State Key Laboratory of Digital Medical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China.
HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China.
Nature. 2025 Jul 2. doi: 10.1038/s41586-025-09211-8.
Multi-omics studies, represented by connectomes and spatial transcriptomes, have entered the era of single-cell resolution, necessitating a reference brain atlas with spatial localization capability at the single-cell level. However, such atlases are unavailable. Here we present a whole mouse brain dataset of Nissl-based cytoarchitecture with isotropic 1-μm resolution, achieved through continuous micro-optical sectioning tomography. By integrating multi-modal images, we constructed a three-dimensional reference atlas of the mouse brain, providing the three-dimensional topographies of 916 structures and enabling arbitrary-angle slice image generation at 1-μm resolution. We developed an informatics-based platform for visualizing and sharing of the atlas images, offering services such as brain slice registration, neuronal circuit mapping and intelligent stereotaxic surgery planning. This atlas is interoperable with widely used stereotaxic atlases, supporting cross-atlas navigation of corresponding coronal planes in two dimensions and spatial mapping across atlas spaces in three dimensions. By facilitating the data analysis and visualization for large brain mapping projects, our atlas promises to be a versatile brainsmatics tool for studying the whole brain at single-cell level.
以连接组学和空间转录组学为代表的多组学研究已进入单细胞分辨率时代,这就需要一个具有单细胞水平空间定位能力的参考脑图谱。然而,目前尚无此类图谱。在此,我们展示了一个基于尼氏染色细胞构筑的全小鼠脑数据集,其通过连续微光学切片断层扫描实现了各向同性1微米分辨率。通过整合多模态图像,我们构建了小鼠脑的三维参考图谱,提供了916个结构的三维地形图,并能够以1微米分辨率生成任意角度的切片图像。我们开发了一个基于信息学的平台来可视化和共享图谱图像,提供诸如脑切片配准、神经回路映射和智能立体定向手术规划等服务。该图谱可与广泛使用的立体定向图谱互操作,支持二维中相应冠状平面的跨图谱导航以及三维中跨图谱空间的空间映射。通过促进大型脑图谱项目的数据分析和可视化,我们的图谱有望成为一个用于在单细胞水平研究全脑的多功能脑图谱工具。