Perisomatic ultrastructure efficiently classifies cells in mouse cortex

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.

Authors

Institutions

Publication Details

Journal
Nature
Published
2025-04-09
DOI
https://doi.org/10.1038/s41586-024-07765-7
Citations
15
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
18.60

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Perisomatic ultrastructure efficiently classifies cells in mouse cortex

Ágnes L. Bodor, R. Clay Reid, Sergiy Popovych, JoAnn Buchanan et al.
15 citations
Nature
Neural dynamics and brain function
18.60
article

Perisomatic ultrastructure efficiently classifies cells in mouse cortex

Ágnes L. Bodor, R. Clay Reid, Sergiy Popovych, JoAnn Buchanan, Szi-chieh Yu, Nuno Maçarico da Costa, Thomas Macrina, William S. Wong, Russel Torres, Sven Dorkenwald, Shanka Subhra Mondal, Manuel Castro, Leila Elabbady, Nicholas L. Turner, H. Sebastian Seung, William Silversmith, Gayathri Mahalingam, Nico Kemnitz, Jingpeng Wu, Sam Kinn, Marc Takeno, Daniel J. Bumbarger, Shang Mu, Akhilesh Halageri, Casey M Schneider-Mizell, Eric Mitchell, Dan Kapner, Sharmishtaa Seshamani, Forrest Collman, Barak Nehoran, Derrick Brittain, Kai Li, J. Alexander Bae, Kisuk Lee, Zhen Jia, Ran Lu, Wenjing Yin, Chris Jordan
article en
15 citations

Abstract

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.

NatureVol. 640(8058)
Allen Institute for Brain Science (US), Princeton University (US), University of Washington (US), Allen Institute (US)
National Science Foundation, Office of the Director of National Intelligence, National Institutes of Health, Intelligence Advanced Research Projects Activity, Interior Business Center
Openalex Percentile: Top 1%
Neural dynamics and brain function
18.60
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.