Treffer: Perisomatic ultrastructure efficiently classifies cells in mouse cortex.
Title:
Perisomatic ultrastructure efficiently classifies cells in mouse cortex.
Authors:
Elabbady L; Allen Institute for Brain Science, Seattle, WA, USA.; University of Washington, Seattle, WA, USA., Seshamani S; Allen Institute for Brain Science, Seattle, WA, USA., Mu S; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Mahalingam G; Allen Institute for Brain Science, Seattle, WA, USA., Schneider-Mizell CM; Allen Institute for Brain Science, Seattle, WA, USA., Bodor AL; Allen Institute for Brain Science, Seattle, WA, USA., Bae JA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Brittain D; Allen Institute for Brain Science, Seattle, WA, USA., Buchanan J; Allen Institute for Brain Science, Seattle, WA, USA., Bumbarger DJ; Allen Institute for Brain Science, Seattle, WA, USA., Castro MA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Dorkenwald S; Allen Institute for Brain Science, Seattle, WA, USA.; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Halageri A; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Jia Z; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Jordan C; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Kapner D; Allen Institute for Brain Science, Seattle, WA, USA., Kemnitz N; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Kinn S; Allen Institute for Brain Science, Seattle, WA, USA., Lee K; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Li K; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Lu R; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Macrina T; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Mitchell E; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Mondal SS; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Nehoran B; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Popovych S; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Silversmith W; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Takeno M; Allen Institute for Brain Science, Seattle, WA, USA., Torres R; Allen Institute for Brain Science, Seattle, WA, USA., Turner NL; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Wong W; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Wu J; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Yin W; Allen Institute for Brain Science, Seattle, WA, USA., Yu SC; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Seung HS; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Reid RC; Allen Institute for Brain Science, Seattle, WA, USA., da Costa NM; Allen Institute for Brain Science, Seattle, WA, USA., Collman F; Allen Institute for Brain Science, Seattle, WA, USA. forrestc@alleninstitute.org.
Source:
Nature [Nature] 2025 Apr; Vol. 640 (8058), pp. 478-486. Date of Electronic Publication: 2025 Apr 09.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 0410462 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-4687 (Electronic) Linking ISSN: 00280836 NLM ISO Abbreviation: Nature Subsets: MEDLINE
Imprint Name(s):
Publication: Basingstoke : Nature Publishing Group
Original Publication: London, Macmillan Journals ltd.
Original Publication: London, Macmillan Journals ltd.
MeSH Terms:
Neocortex*/cytology , Neocortex*/ultrastructure , Neurons*/ultrastructure , Neurons*/classification , Classification Algorithms*, Synapses/ultrastructure ; Cell Nucleus/ultrastructure ; DNA Primase/genetics ; Animals ; Male ; Mice ; Microscopy, Electron ; Datasets as Topic ; Mice, Knockout ; Autoencoder
References:
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Shapson-Coe, A. et al. A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution. Science 384, eadk4858 (2024).
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Schneider-Mizell, C. M. et al. Inhibitory specificity from a connectomic census of mouse visual cortex. Nature https://doi.org/10.1038/s41586-024-07780-8 (2025).
Gamlin, C. R. et al. Connectomics of predicted Sst transcriptomic types in mouse visual cortex. Nature https://doi.org/10.1038/s41586-025-08805-6 (2025).
Bodor, A. L. et al. The synaptic architecture of layer 5 thick tufted excitatory neurons in the visual cortex of mice. Preprint at bioRxiv https://doi.org/10.1101/2023.10.18.562531 (2023).
Turner, N. L. et al. Reconstruction of neocortex: organelles, compartments, cells, circuits, and activity. Cell 185, 1082–1100 (2022). (PMID: 35216674933790910.1016/j.cell.2022.01.023)
Kim, E. J., Juavinett, A. L., Kyubwa, E. M., Jacobs, M. W. & Callaway, E. M. Three types of cortical layer 5 neurons that differ in brain-wide connectivity and function. Neuron 88, 1253–1267 (2015). (PMID: 26671462468812610.1016/j.neuron.2015.11.002)
Schneider-Mizell, C. M. et al. Structure and function of axo-axonic inhibition. eLife 10, e73783 (2021). (PMID: 34851292875814310.7554/eLife.73783)
Kubota, Y., Karube, F., Nomura, M. & Kawaguchi, Y. The diversity of cortical inhibitory synapses. Front. Neural Circuits 10, 27 (2016). (PMID: 27199670484277110.3389/fncir.2016.00027)
Callaway, E. M. in Micro-, Meso- and Macro-Connectomics of the Brain (eds Kennedy, H. et al.) 11–19 (Springer, 2016).
Sultan, K. T. & Shi, S.-H. Generation of diverse cortical inhibitor interneurons. Wiley Interdiscip. Rev. Dev. Biol. https://doi.org/10.1002/wdev.306 (2018).
Seshamani, S. et al. Automated neuron shape analysis from electron microscopy. Preprint at https://arxiv.org/abs/2006.00100 (2020).
Sholl, D. A. Dendritic organization in the neurons of the visual and motor cortices of the cat. J. Anat. 87, 387–406-1 (1953). (PMID: 131177571244622)
Keller, D., Erö, C. & Markram, H. Cell densities in the mouse brain: a systematic review. Front. Neuroanat. 12, 83 (2018). (PMID: 30405363620598410.3389/fnana.2018.00083)
Rudy, B., Fishell, G., Lee, S. & Hjerling-Leffler, J. Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons. Dev. Neurobiol. 71, 45–61 (2011). (PMID: 21154909355690510.1002/dneu.20853)
Kim, Y. et al. Brain-wide maps reveal stereotyped cell-type-based cortical architecture and subcortical sexual dimorphism. Cell 171, 456–469 (2017). (PMID: 28985566587082710.1016/j.cell.2017.09.020)
Cragg, B. G. The density of synapses and neurones in the motor and visual areas of the cerebral cortex. J. Anat. 101, 639–654 (1967). (PMID: 49646961270900)
Kapfer, C., Glickfeld, L. L., Atallah, B. V. & Scanziani, M. Supralinear increase of recurrent inhibition during sparse activity in the somatosensory cortex. Nat. Neurosci. 10, 743–753 (2007). (PMID: 17515899351886610.1038/nn1909)
Pfeffer, C. K., Xue, M., He, M., Huang, Z. J. & Scanziani, M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16, 1068–1076 (2013). (PMID: 23817549372958610.1038/nn.3446)
Lee, S., Kruglikov, I., Huang, Z. J., Fishell, G. & Rudy, B. A disinhibitory circuit mediates motor integration in the somatosensory cortex. Nat. Neurosci. 16, 1662–1670 (2013). (PMID: 24097044410007610.1038/nn.3544)
Pi, H.-J. et al. Cortical interneurons that specialize in disinhibitory control. Nature 503, 521–524 (2013). (PMID: 24097352401762810.1038/nature12676)
Defelipe, J., Hendry, S. H. C., Jones, E. G. & Schmechel, D. Variability in the terminations of GABAergic chandelier cell axons on initial segments of pyramidal cell axons in the monkey sensory-motor cortex. J. Comp. Neurol. 231, 364–384 (1985). (PMID: 298190710.1002/cne.902310307)
Fairén, A. & Valverde, F. A specialized type of neuron in the visual cortex of cat: a Golgi and electron microscope study of chandelier cells. J. Comp. Neurol. 194, 761–779 (1980). (PMID: 720464210.1002/cne.901940405)
Somogyi, P., Freund, T. F. & Cowey, A. The axo-axonic interneuron in the cerebral cortex of the rat, cat and monkey. Neuroscience 7, 2577–2607 (1982). (PMID: 715534310.1016/0306-4522(82)90086-0)
Jones, E. G. Varieties and distribution of non-pyramidal cells in the somatic sensory cortex of the squirrel monkey. J. Comp. Neurol. 160, 205–267 (1975). (PMID: 80351810.1002/cne.901600204)
Peters, A., Proskauer, C. C. & Ribak, C. E. Chandelier cells in rat visual cortex. J. Comp. Neurol. 206, 397–416 (1982). (PMID: 709663410.1002/cne.902060408)
Sorensen, S. A. et al. Correlated gene expression and target specificity demonstrate excitatory projection neuron diversity. Cereb. Cortex 25, 433–449 (2015). (PMID: 2401467010.1093/cercor/bht243)
Celii, B. et al. NEURD offers automated proofreading and feature extraction for connectomics. Nature https://doi.org/10.1038/s41586-025-08660-5 (2025).
Weis, M. A. et al. Large-scale unsupervised discovery of excitatory morphological cell types in mouse visual cortex. Preprint at bioRxiv https://doi.org/10.1101/2022.12.22.521541 (2023).
Buchanan, J. et al. Oligodendrocyte precursor cells ingest axons in the mouse neocortex. Proc. Natl Acad. Sci. USA 119, e2202580119 (2022). (PMID: 36417438988988610.1073/pnas.2202580119)
Gouwens, N. W. et al. Integrated morphoelectric and transcriptomic classification of cortical GABAergic cells. Cell 183, 935–953 (2020). (PMID: 33186530778106510.1016/j.cell.2020.09.057)
Attili, S. M., Silva, M. F. M., Nguyen, T.-V. & Ascoli, G. A. Cell numbers, distribution, shape, and regional variation throughout the murine hippocampal formation from the adult brain Allen Reference Atlas. Brain Struct. Funct. 224, 2883–2897 (2019). (PMID: 31444616677871910.1007/s00429-019-01940-7)
Luengo-Sanchez, S. et al. A univocal definition of the neuronal soma morphology using Gaussian mixture models. Front. Neuroanat. 9, 137 (2015). (PMID: 26578898463028910.3389/fnana.2015.00137)
Fariñas, I. & DeFelipe, J. Patterns of synaptic input on corticocortical and corticothalamic cells in the cat visual cortex. I. The cell body. J. Comp. Neurol. 304, 53–69 (1991). (PMID: 201641210.1002/cne.903040105)
Gilman, J. P., Medalla, M. & Luebke, J. I. Area-specific features of pyramidal neurons—a comparative study in mouse and rhesus monkey. Cereb. Cortex 27, 2078–2094 (2017). (PMID: 26965903)
Peters, A. & Kara, D. A. The neuronal composition of area 17 of rat visual cortex. I. The pyramidal cells. J. Comp. Neurol. 234, 218–241 (1985). (PMID: 398898310.1002/cne.902340208)
Georgiou, C. et al. A subpopulation of cortical VIP-expressing interneurons with highly dynamic spines. Commun. Biol. 5, 352 (2022). (PMID: 35418660900803010.1038/s42003-022-03278-z)
Wittmann, M. et al. Synaptic activity induces dramatic changes in the geometry of the cell nucleus: interplay between nuclear structure, histone H3 phosphorylation, and nuclear calcium signaling. J. Neurosci. 29, 14687–14700 (2009). (PMID: 19940164666601710.1523/JNEUROSCI.1160-09.2009)
Dorkenwald, S. et al. Binary and analog variation of synapses between cortical pyramidal neurons. eLife 11, e76120 (2022). (PMID: 36382887970480410.7554/eLife.76120)
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Grant Information:
RF1 MH123400 United States MH NIMH NIH HHS; RF1 MH125932 United States MH NIMH NIH HHS; RF1 MH117808 United States MH NIMH NIH HHS; U24 NS126935 United States NS NINDS NIH HHS; RF1 MH129268 United States MH NIMH NIH HHS
Substance Nomenclature:
EC 2.7.7.- (DNA Primase)
Entry Date(s):
Date Created: 20250409 Date Completed: 20250409 Latest Revision: 20250414
Update Code:
20260130
PubMed Central ID:
PMC11981918
DOI:
10.1038/s41586-024-07765-7
PMID:
40205216
Database:
MEDLINE
Weitere Informationen
Mammalian neocortex contains a highly diverse set of cell types. These cell types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches<sup>1-4</sup>. Each modality offers new perspectives on the variation of biological processes underlying cell-type specialization. Cellular-scale electron microscopy provides dense ultrastructural examination and an unbiased perspective on the subcellular organization of brain cells, including their synaptic connectivity and nanometre-scale morphology. In data that contain tens of thousands of neurons, most of which have incomplete reconstructions, identifying cell types becomes a clear challenge for analysis<sup>5</sup>. Here, to address this challenge, we present a systematic survey of the somatic region of all cells in a cubic millimetre of cortex using quantitative features obtained from electron microscopy. This analysis demonstrates that the perisomatic region is sufficient to identify cell types, including types defined primarily on the basis of their connectivity patterns. We then describe how this classification facilitates cell-type-specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.
(© 2025. The Author(s).)
Competing interests: T.M., K. Lee, S.P., N.K. and H.S.S. declare financial interests in Zetta AI. The remaining authors declare no competing interests.