Connectomics of predicted Sst transcriptomic types in mouse visual cortex

Clare R. Gamlin, Casey M. Schneider-Mizell, Matthew Mallory, Leila Elabbady, Nathan Gouwens, Grace Williams, Alice Mukora, Rachel Dalley, Agnes L. Bodor, Derrick Brittain, JoAnn Buchanan, Daniel J. Bumbarger, Emily Joyce, Daniel Kapner, Sam Kinn, Gayathri Mahalingam, Sharmishtaa Seshamani, Marc Takeno, Russel Torres, Wenjing Yin, Philip R. Nicovich, J. Alexander Bae, Manuel A. Castro, Sven Dorkenwald, Akhilesh Halageri, Zhen Jia, Chris Jordan, Nico Kemnitz, Kisuk Lee, Kai Li, Ran Lu, Thomas Macrina, Eric Mitchell, Shanka Subhra Mondal, Shang Mu, Barak Nehoran, Sergiy Popovych, William Silversmith, Nicholas L. Turner, William Wong, Jingpeng Wu, Szi-chieh Yu, Jim Berg, Tim Jarsky, Brian Lee, H. Sebastian Seung, Hongkui Zeng, R. Clay Reid, Forrest Collman (), Nuno Maçarico Costa () and Staci A. Sorensen ()
Additional contact information
Clare R. Gamlin: Allen Institute for Brain Science
Casey M. Schneider-Mizell: Allen Institute for Brain Science
Matthew Mallory: Allen Institute for Brain Science
Leila Elabbady: Allen Institute for Brain Science
Nathan Gouwens: Allen Institute for Brain Science
Grace Williams: Allen Institute for Brain Science
Alice Mukora: Allen Institute for Brain Science
Rachel Dalley: Allen Institute for Brain Science
Agnes L. Bodor: Allen Institute for Brain Science
Derrick Brittain: Allen Institute for Brain Science
JoAnn Buchanan: Allen Institute for Brain Science
Daniel J. Bumbarger: Allen Institute for Brain Science
Emily Joyce: Allen Institute for Brain Science
Daniel Kapner: Allen Institute for Brain Science
Sam Kinn: Allen Institute for Brain Science
Gayathri Mahalingam: Allen Institute for Brain Science
Sharmishtaa Seshamani: Allen Institute for Brain Science
Marc Takeno: Allen Institute for Brain Science
Russel Torres: Allen Institute for Brain Science
Wenjing Yin: Allen Institute for Brain Science
Philip R. Nicovich: Allen Institute for Brain Science
J. Alexander Bae: Princeton University
Manuel A. Castro: Princeton University
Sven Dorkenwald: Princeton University
Akhilesh Halageri: Princeton University
Zhen Jia: Princeton University
Chris Jordan: Princeton University
Nico Kemnitz: Princeton University
Kisuk Lee: Princeton University
Kai Li: Princeton University
Ran Lu: Princeton University
Thomas Macrina: Princeton University
Eric Mitchell: Princeton University
Shanka Subhra Mondal: Princeton University
Shang Mu: Princeton University
Barak Nehoran: Princeton University
Sergiy Popovych: Princeton University
William Silversmith: Princeton University
Nicholas L. Turner: Princeton University
William Wong: Princeton University
Jingpeng Wu: Princeton University
Szi-chieh Yu: Princeton University
Jim Berg: Allen Institute for Brain Science
Tim Jarsky: Allen Institute for Brain Science
Brian Lee: Allen Institute for Brain Science
H. Sebastian Seung: Princeton University
Hongkui Zeng: Allen Institute for Brain Science
R. Clay Reid: Allen Institute for Brain Science
Forrest Collman: Allen Institute for Brain Science
Nuno Maçarico Costa: Allen Institute for Brain Science
Staci A. Sorensen: Allen Institute for Brain Science

Nature, 2025, vol. 640, issue 8058, 497-505

Abstract: Abstract Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between them1. Neural cell types have previously been defined by morphology2,3, electrophysiology4, transcriptomic expression5,6, connectivity7–9 or a combination of such modalities10–12. The Patch-seq technique enables the characterization of morphology, electrophysiology and transcriptomic properties from individual cells13–15. These properties were integrated to define 28 inhibitory, morpho-electric-transcriptomic (MET) cell types in mouse visual cortex16, which do not include synaptic connectivity. Conversely, large-scale electron microscopy (EM) enables morphological reconstruction and a near-complete description of a neuron’s local synaptic connectivity, but does not include transcriptomic or electrophysiological information. Here, we leveraged morphological information from Patch-seq to predict the transcriptomically defined cell subclass and/or MET-type of inhibitory neurons within a large-scale EM dataset. We further analysed Martinotti cells—a somatostatin (Sst)-positive17 morphological cell type18,19—which were classified successfully into Sst MET-types with distinct axon myelination and synaptic output connectivity patterns. We demonstrate that morphological features can be used to link cell types across experimental modalities, enabling further comparison of connectivity to gene expression and electrophysiology. We observe unique connectivity rules for predicted Sst cell types.

Date: 2025
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DOI: 10.1038/s41586-025-08805-6

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