The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.
Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex
Open accessPublished version of a preprint
Abstract
Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566
Self-citationVisualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834
Self-citationHe, K., Gkioxari, G., Dollar, P., & Girshick, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2844175 · Cited by 3,457
On the Dependency of Cellular Protein Levels on mRNA Abundance
Liu, Y., Beyer, A., & Aebersold, R. (2016) · Cell · doi:10.1016/j.cell.2016.03.014 · Cited by 3,221
Spatially resolved, highly multiplexed RNA profiling in single cells
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969
Self-citationMolecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,552
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424
CORTICAL PLASTICITY: From Synapses to Maps
Buonomano, D. V., & Merzenich, M. M. (1998) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.21.1.149 · Cited by 2,083
Research works printed but not linked: 1
- Yuste, 2020, A community-based transcriptomics classification and nomenclature of neocortical cell types, Nat. Neurosci., 23, 1456, 10.1038/s41593-020-0685-8 · DOI · Paper page
Cited by
Sorted by citations
Using clusterProfiler to characterize multiomics data
Xu, S., Hu, E., Cai, Y., Xie, Z., Luo, X. M., Zhan, L., Tang, W., Wang, Q., Liu, B., Wang, R., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01020-z · Cited by 1,528
BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis
Singhal, V., Chou, N., Lee, J., Yue, Y., Liu, J., Chock, W. K., Lin, L., Chang, Y.-C., Teo, E. M. L., Aow, J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01664-3 · Cited by 282
Self-citationSpatiotemporal omics for biology and medicine
Liu, L., Chen, A., Li, Y., Mulder, J., Heyn, H., & Xu, X. (2024) · Cell · doi:10.1016/j.cell.2024.07.040 · Cited by 252
Self-citationBenchmarking spatial clustering methods with spatially resolved transcriptomics data
Yuan, Z., Zhao, F., Lin, S., Zhao, Y. S., Yao, J., Cui, Y., Zhang, X.-Y., & Zhao, Y. C. (2024) · Nature Methods · doi:10.1038/s41592-024-02215-8 · Cited by 219
Self-citationCheng, M., Jiang, Y., Xu, J., Mentis, A. A., Wang, S., Zheng, H., Sahu, S. K., Liu, L., & Xu, X. (2023) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2023.03.011 · Cited by 195
Self-citation
Metadata
- Authors
- Chen, A., Sun, Y., Lei, Y., Li, C., Liao, S., Meng, J., Bai, Y., Liu, Z., Liang, Z., Zhu, Z., Yuan, N., Yang, H., Wu, Z., Lin, F., Wang, K., Li, M., Zhang, S., Yang, M., Fei, T., Zhuang, Z., Huang, Y., Zhang, Y., Xu, Y., Cui, L., Zhang, R., Han, L., Sun, X., Chen, B., Li, W., Huangfu, B., Ma, K., Ma, J., Li, Z., Lin, Y., Wang, H., Zhong, Y., Zhang, H., Yu, Q., Wang, Y., Liu, X., Peng, J., Liu, C., Chen, W., Pan, W., An, Y., Xia, S., Lu, Y., Wang, M., Song, X., Liu, S., Wang, Z., Gong, C., Huang, X., Yuan, Y., Zhao, Y., Chai, Q., Tan, X., Liu, J., Zheng, M., Li, S., Huang, Y., Hong, Y., Huang, Z., Li, M., Jin, M., Li, Y., Zhang, H., Sun, S., Gao, L., Bai, Y., Cheng, M., Hu, G., Liu, S., Wang, B., Xiang, B., Li, S., Li, H., Chen, M., Wang, S., Li, M., Liu, W., Liu, X., Zhao, Q., Lisby, M., Wang, J., Fang, J., Lin, Y., Xie, Q., Liu, Z., He, J., Xu, H., Huang, W., Mulder, J., Yang, H., Sun, Y., Uhlen, M., Poo, M., Wang, J., Yao, J., Wei, W., Li, Y., Shen, Z., Liu, L., Liu, Z., Xu, X., & Li, C.
- Affiliations
- BGI Group (China) (CN)
- Center for Excellence in Brain Science and Intelligence Technology (CN)
- Tencent (China) (CN)
- Shanghai Institute of Nutrition and Health (CN)
- China National GeneBank (CN)
- University of Copenhagen (DK)
- Science for Life Laboratory (SE)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Sun, Yidi
- Yao, Jianhua
- Li, Yuxiang
- Shen, Zhiming
- Liu, Longqi
- Liu, Zhiyong
- Xu, Xun
- Li, Chengyu
- ORCID
- Chen, Ao
- Sun, Yidi
- Lei, Ying
- Li, Chao
- Liao, Sha
- Meng, Juan
- Bai, Yiqin
- Yang, Hao
- Wu, Zihan
- Lin, Feng
- Wang, Kexin
- Li, Mei
- Fei, Tianyi
- Zhuang, Zhenkun
- Huang, Yiming
- Zhang, Yong
- Zhang, Ruiyi
- Han, Lei
- Sun, Xing
- Chen, Bichao
- Li, Wenjiao
- Ma, Kailong
- Wang, He
- Zhang, Huifang
- Wang, Yaqian
- Liu, Xing
- Peng, Jian
- Liu, Chuanyu
- Chen, Wei
- Pan, Wentao
- Xia, Shihui
- Wang, Mingli
- Song, Xinxiang
- Wang, Zhifeng
- Huang, Xin
- Yuan, Yue
- Zhao, Yun
- Chai, Qinwen
- Tan, Xing
- Li, Shengkang
- Huang, Yaling
- Li, Min
- Jin, Mengmeng
- Li, Yan
- Zhang, Hui
- Sun, Suhong
- Gao, Li
- Bai, Yinqi
- Hu, Guohai
- Liu, Shiping
- Wang, Bo
- Li, Huanhuan
- Chen, Mengni
- Wang, Shiwen
- Li, Minglong
- Liu, Weibin
- Liu, Xin
- Zhao, Qian
- Lisby, Michael
- Wang, Jing
- Xie, Qing
- He, Jie
- Xu, Huatai
- Huang, Wei
- Mulder, Jan
- Yang, Huanming
- Wang, Jian
- Yao, Jianhua
- Wei, Wu
- Li, Yuxiang
- Shen, Zhiming
- Liu, Longqi
- Liu, Zhiyong
- Xu, Xun
- Li, Chengyu
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2023-08
- Volume
- 186
- Issue
- 17
- Pages
- 3726-3743.e24
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W4384071384
- Cited by
- 232
- References
- 122
- Citations by year
- 2026: 57 · 2025: 81 · 2024: 65 · 2023: 26
- FWCI
- 20.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (2018SHZDZX05, 31827803, 31900466)
- Chinese Academy of Sciences (153D31KYSB20170059, 2018SHZDZX05, XDB32010100)
- Shenzhen Fundamental Research Program
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- macaque cortex, cortical cell types, single-nucleus RNA sequencing, spatial transcriptomics, cell type atlas, cortical layers, cortical regions, glutamatergic neurons, GABAergic neurons, non-neuronal cells, layer 4, cross-species comparison, primate brain evolution
- MeSH terms
- Animals, Cerebral Cortex, Humans, Macaca, Mice, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
