Science AdvancesNeurosciencePeer-reviewed journal article
Whole Human-Brain Mapping of Single Cortical Neurons for Profiling Morphological Diversity and Stereotypy
Xiaofeng HaniD, Shuxia GuoiD, Nan JiiD, Tian LiiD, Jian LiuiD, Xiangqiao YeiD, Yi WangiD, Zhixi YuniD, Feng XiongiD, Jing Rong, Di LiuiD, Hui MaiD, Yujin WangiD, Yue Ming HuangiD, Peng ZhangiD, Wenhao WuiD, Liya DingiD, Michael HawrylycziD, Ed S. LeiniD, Giorgio A. AscoliiD, Wei XieiD, Lijuan LiuiD, Liwei ZhangiD, Hanchuan PengiD
Abstract
Quantification of individual cells morphology and their distribution at the whole brain scale is essential to understand the structure and diversity of cell types. Despite recent technological advances, especially single cell labeling and whole brain imaging, for many prevailing animal models, it is exceedingly challenging to reuse similar technologies to study human brains. Here we propose Adaptive Cell Tomography (ACTomography), a low-cost, high-throughput, high-efficacy tomography approach, based on adaptive targeting of individual cells suitable for human-brain scale modeling of single neurons to characterize their 3-D structures, statistical distributions, and extensible for other cellular features. Specifically, we established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and 1 donated fresh postmortem brain. We collected 3-D images of 1746 cortical neurons, of which 852 neurons were subsequentially reconstructed to quantify their local dendritic morphology, and mapped to standard atlases both computationally and semantically. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor-field of neuron morphology to characterize 3-D anatomical modularity of a human brain.
- organism: human
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: dimensionality reduction
- research focus: clustering
- research focus: spatial domain detection
- approach: experimental assay
- Citations
- 34
- FWCI
- 4.11
- Field percentile
- top 4%
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