Morphological diversity of single neurons in molecularly defined cell types
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Abstract
Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types 1,2 , yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.
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In Gnb4-Cre mice, no labelled cells were found in MOp; all labelled cells were far outside MOp and close to the claustrum.
“We were unable to find any labelled cells in MOp in the Gnb4-Cre mice: all labelled cells were far outside of MOp and close to the claustrum46.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [46]
Within each cortical excitatory subclass, individual neurons show extensive heterogeneity along regional, topographic, and individual (potentially stochastic) axes.
“Within each subclass, the study also finds extensive heterogeneity among individual neurons; this heterogeneity reflects three axes of variations: regional specificity, topographic specificity and individual (potentially stochastic) variation,”
From What is a cell type and how to define it? (2022) · cites this paper as Peng et al., 2021
A study using complete reconstruction in cortical excitatory neuron subclass-specific Cre driver lines found distinct projection patterns between subclasses, including between IT and ET and between L2/3 IT and L5 IT neurons, confirming subclass level projection specificity.
“A study using this approach in cortical excitatory neuron subclass-specific Cre driver lines (Peng et al., 2021) reveals distinct projection patterns between subclasses, e.g., not only between IT and ET but also between L2/3 IT and L5 IT neurons, confirming subclass level projection specificity (Fig. 3C).”
From What is a cell type and how to define it? (2022) · cites this paper as Peng et al., 2021
Brain-wide complete reconstruction of single neuron morphology is currently the only method to capture the full extent of a neuron's axon projection pattern and define projection neuron types.
“Brain-wide complete reconstruction of single neuron morphology is currently the only approach to capture the full extent of a neuron’s axon projection pattern and define projection neuron types (Gao et al., 2022; Peng et al., 2021; Winnubst et al., 2019).”
From What is a cell type and how to define it? (2022) · cites this paper as Peng et al., 2021
These three axes of variation do not readily correlate with transcriptomic types within the subclass.
“which do not readily correlate with transcriptomic types within the subclass.”
From What is a cell type and how to define it? (2022) · cites this paper as Peng et al., 2021
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Jorstad, N. L., Song, J. H. T., Exposito-Alonso, D., Suresh, H., Castro-Pacheco, N., Krienen, F. M., Yanny, A. M., Close, J., Gelfand, E., Long, B., et al. (2023) · Science · doi:10.1126/science.ade9516 · Cited by 208 · see citing works
“High-CUX2 neuronsareselectivelyenriched in language-related regions in the human temporal and parietal cortex (MTG, A1, and ANG) ( 21 ), and these neurons may have distinct connectivity and contribute to the functional specializations of these regions.”
BackgroundGenetic dissection of the glutamatergic neuron system in cerebral cortex
Matho, K. S., Huilgol, D., Galbavy, W., He, M., Kim, G., An, X., Lu, J., Wu, P., Di Bella, D. J., Shetty, A. S., et al. (2021) · Nature · doi:10.1038/s41586-021-03955-9 · Cited by 155 · see citing works
Self-citationAxonal energy metabolism, and the effects in aging and neurodegenerative diseases
Yang, S., Park, J. H., & Lu, H.-C. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00634-3 · Cited by 94 · see citing works
High-speed mapping of whole-mouse peripheral nerves at subcellular resolution
Shi, M.-Y., Yao, Y., Wang, M., Yang, Q., Ding, L., Li, R., Li, Y., Huang, H., Yang, C.-Y., Zhou, Z., et al. (2025) · Cell · doi:10.1016/j.cell.2025.06.011 · Cited by 18 · see citing works
Self-citationLayer-specific input to medial prefrontal cortex is linked to stress susceptibility
Solakoğlu, S. T., Erdener, Ş. E., Gliko, O., Can, A., Sümbül, U., & Eren-Koçak, E. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03258-x · Cited by 5 · see citing works
- Authors
- Peng, H., Xie, P., Liu, L., Kuang, X., Wang, Y., Qu, L., Gong, H., Jiang, S., Li, A., Ruan, Z., Ding, L., Yao, Z., Chen, C., Chen, M., Daigle, T. L., Dalley, R., Ding, Z., Duan, Y., Feiner, A., He, P., Hill, C., Hirokawa, K. E., Hong, G., Huang, L., Kebede, S., Kuo, H.-C., Larsen, R., Lesnar, P., Li, L., Li, Q., Li, X., Li, Y., Li, Y., Liu, A., Lu, D., Mok, S., Ng, L., Nguyen, T. N., Ouyang, Q., Pan, J., Shen, E., Song, Y., Sunkin, S. M., Tasic, B., Veldman, M. B., Wakeman, W., Wan, W., Wang, P., Wang, Q., Wang, T., Wang, Y., Xiong, F., Xiong, W., Xu, W., Ye, M., Yin, L., Yu, Y., Yuan, J., Yuan, J., Yun, Z., Zeng, S., Zhang, S., Zhao, S., Zhao, Z., Zhou, Z., Huang, Z. J., Esposito, L., Hawrylycz, M. J., Sorensen, S. A., Yang, X. W., Zheng, Y., Gu, Z., Xie, W., Koch, C., Luo, Q., Harris, J. A., Wang, Y., & Zeng, H.
- Affiliations
- Allen Institute for Brain Science (US)
- Southeast University (CN)
- Wenzhou Medical University (CN)
- Shanghai University (CN)
- Anhui University (CN)
- Wuhan National Laboratory for Optoelectronics (CN)
- Tencent (China) (CN)
- University of California, Los Angeles (US)
- Duke University (US)
- Hainan University (CN)
- Corresponding author
- Peng, Hanchuan
- Zeng, Hongkui
- ORCID
- Peng, Hanchuan
- Xie, Peng
- Liu, Lijuan
- Kuang, Xiuli
- Wang, Yimin
- Qu, Lei
- Gong, Hui
- Jiang, Shengdian
- Li, Anan
- Ruan, Zongcai
- Ding, Liya
- Yao, Zizhen
- Chen, Chao
- Daigle, Tanya L.
- Dalley, Rachel
- Duan, Yanjun
- Hill, Chris
- Hirokawa, Karla E.
- Huang, Lei
- Kebede, Sara
- Larsen, Rachael
- Lesnar, Phil
- Li, Longfei
- Li, Qi
- Li, Xiangning
- Li, Yaoyao
- Li, Yuanyuan
- Liu, An
- Lu, Donghuan
- Mok, Stephanie
- Ng, Lydia
- Nguyen, Thuc Nghi
- Shen, Elise
- Song, Yuanyuan
- Sunkin, Susan M.
- Tasic, Bosiljka
- Veldman, Matthew B.
- Wakeman, Wayne
- Wang, Peng
- Wang, Quanxin
- Wang, Tao
- Wang, Yaping
- Xiong, Feng
- Xu, Wenjie
- Ye, Min
- Yin, Lulu
- Yu, Yang
- Yuan, Jia
- Yuan, Jing
- Yun, Zhixi
- Zeng, Shaoqun
- Zhang, Shichen
- Zhao, Sujun
- Zhao, Zijun
- Zhou, Zhi
- Huang, Z. Josh
- Esposito, Luke
- Hawrylycz, Michael J.
- Sorensen, Staci A.
- Yang, X. William
- Zheng, Yefeng
- Gu, Zhongze
- Xie, Wei
- Koch, Christof
- Luo, Qingming
- Harris, Julie A.
- Wang, Yun
- Zeng, Hongkui
- Venue
- Nature
- Published
- 2021-10-06
- Volume
- 598
- Issue
- 7879
- Pages
- 174-181
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8494643
- OpenAlex
- W3202358235
- Cited by
- 417
- References
- 74
- Citations by year
- 2026: 70 · 2025: 93 · 2024: 95 · 2023: 89 · 2022: 45 · 2021: 20 · 2020: 4
- FWCI
- 19.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Wenzhou Medical University
- Tiny Blue Dot Foundation
- École Polytechnique Fédérale de Lausanne
- National Natural Science Foundation of China (GXXT-2019-008, 61721092, 91632201, 61890953, 61871411)
- Southeast University
- National Institutes of Health
- National Institute of Mental Health (U01MH117079, U01MH106008, U01MH105982, U19MH114830)
- National Eye Institute (R01EY023173)
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- axonal projections, neuronal cell types, mouse brain, cortical neurons, thalamus, striatum, claustrum, cell type classification
- MeSH terms
- RNA-Seq, Atlases as Topic, Brain, Humans, Neuroglia, Neurons, Reproducibility of Results, Biomarkers, Gene Expression Regulation, Developmental, Neocortex, Cell Shape, Neurogenesis, Single-Cell Analysis
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