Full-Spectrum Neuronal Diversity and Stereotypy through Whole Brain Morphometry

Yufeng Liu, Shengdian Jiang, Yingxin Li, Sujun Zhao, Zhixi Yun,...Lijuan Liu, & Hanchuan Peng*
New Cornerstone Science Laboratory
SEU-ALLEN Joint Center
Institute for Brain and Intelligence
Southeast University, Nanjing, Jiangsu, China

*Correspondence: h@braintell.org

About the Project

Home page for the full-spectrum neuronal morphometry analysis project.

This project is led by Prof. Hanchuan Peng at Southeast University, and it is a collaborative work across several groups worldwide, with major contributions from Yufeng Liu, Shengdian Jiang, Yingxin Li, Sujun Zhao, and source code refactoring conducted by Yufeng Liu and Qiaobo Gong.

The project offers a comprehensive framework for extracting and analyzing neuronal morphometry across various scales, ranging from centimeters to micrometers. It also provides tools for cross-scale comparative and integrative analytical paradigm. The primary modules within the full spectrum sources repository encompass neuron population, microenviron, full morphology, axonal varicosity, axonal motif and axonal arbor. Each module includes detailed documentation with step-by-step guidance. These resources aim to support researchers in their studies of neuronal morphometry.


Highlights

  • One of the largest multi-morphometry databases of mammalian brains to date.
  • 204 mouse brain, 6 BICCN data sources, 3 major imaging modalities, 5 collaborative projects.
  • 182,497 soma locations (SEU-S182K); 15,441 local morphologies (SEU-D15K); 1,876 full morphologies (SEU-A1876); 2.63 million axonal varicosities.
  • 6 levels of analysis: neuronal populations, dendritic microenvironments, single-cell full morphology, sub-neuronal dendritic and axonal arborization, axonal varicosities, and sub-neuronal structural motifs.

Data Resources


Soma Locations (SEU-S182K)

Local Morphologies (SEU-D15K)

Full Morphologies (SEU-A1876)

Axonal Arbors

Axonal Motifs




Axonal Varicosities (MST, YouTube)

Abstract

We conducted a large-scale study of whole-brain morphometry, analyzing 3.7 peta-voxels of mouse brain images at the single-cell resolution, producing one of the largest multi-morphometry databases of mammalian brains to date. We spatially registered 204 mouse brains and associated data from six Brain Initiative Cell Census Network (BICCN) data sources covering three major imaging modalities from five collaborative projects to the Allen Common Coordinate Framework (CCF) atlas, annotated 3D locations of cell bodies of 182,497 neurons, modeled 15,441 dendritic microenvironments, characterized the full morphology of 1,876 neurons along with their axonal motifs, and detected 2.63 million axonal varicosities that indicate potential synaptic sites. Our analysis covers six levels of information related to neuronal populations, dendritic microenvironments, single-cell full morphology, sub-neuronal dendritic and axonal arborization, axonal varicosities , and sub-neuronal structural motifs, along with a quantification of the diversity and stereotypy of patterns at each level. We identified 15 modules consisting of intercorrelated brain regions selected from 314 anatomical areas in the CCF. Our analysis revealed the dendritic microenvironment as a powerful method for delineating region-specific cell types and potential subtypes. We also found that full neuronal morphologies can be categorized into four distinct classes based on spatially tuned morphological features, with substantial cross-areal diversity in dendritic arbors and axonal arbors, along with quantified stereotypy within cortical, thalamic, and striatal regions. We found the lamination of somas is more effective than the brain region of somas in differentiating neuron arbors within the cortex. Further analysis of diverging and converging projections of individual neurons in 25 brain regions throughout the brain reveals branching preferences in the brain-wide and local distributions of axonal varicosities . Overall, our study provides a comprehensive description of key anatomical structures of neurons and their types, covering a wide range of scales and features, and contributes an extensive resource to understanding neuronal diversity in the mammalian brain.

Main Results


Play with the dataset

Supplementary Figs


Acknowledgement

We thank Yong Yao and NIH for coordination of the BICCN effort in data collection and synergized analysis, Brain Image Library (BIL) for public sharing of the BICCN database, Josh Huang for sharing raw image data, Yuanyuan Song and LuLu Yin for assistance of the single-neuron annotation, Jiangshan Liang, Luchen Deng, Shize Chen, Fei Xing, Yihang Zhu, Lei Huang and Kaixiang Li for help in tool development, Xuan Zhao, Ye Zhong, Jingzhou Yuan, and another 7 anonymous users for help in soma-annotation, and Yiwei Li for assistance of optimizing soma locations. This work was mainly supported by a Southeast University (SEU) initiative of neuroscience awarded to Hanchuan Peng.

BibTeX

@article{liu2023full,
        title={Full-Spectrum Neuronal Diversity and Stereotypy through Whole Brain Morphometry},
        author={Liu, Yufeng and Jiang, Shengdian and Li, Yingxin and Zhao, Sujun and Yun, Zhixi and Liu, Lijuan and Peng, Hanchuan and others},
        journal={Research Square},
        year={2023}
      }