Cell
Volume 162, Issue 3, 30 July 2015, Pages 648-661
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Saturated Reconstruction of a Volume of Neocortex

https://doi.org/10.1016/j.cell.2015.06.054Get rights and content
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Highlights

  • Tape-based pipeline for electron microscopic reconstruction of brain tissue

  • Annotated database of 1,700 synapses from a saturated reconstruction of cortex

  • Excitatory axon proximity to dendritic spines not sufficient to predict synapses

Summary

We describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many sub-cellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database. We explore these data to study physical properties of brain tissue. For example, by tracing the trajectories of all excitatory axons and noting their juxtapositions, both synaptic and non-synaptic, with every dendritic spine we refute the idea that physical proximity is sufficient to predict synaptic connectivity (the so-called Peters’ rule). This online minable database provides general access to the intrinsic complexity of the neocortex and enables further data-driven inquiries.

Cited by (0)

8

Present address: Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA

9

Present address: Janelia Farm Research Campus, Ashburn, VA 20147, USA

10

Present address: Department of Computer Science, Stanford University, Stanford, CA 94305, USA

11

Present address: Department of Neuroscience, Columbia University, New York, NY 10032, USA

12

Present address: Princeton Neuroscience Institute and Department of Computer Science, Princeton University, Princeton, NJ 08544, USA

13

Present address: Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723, USA

14

Present address: Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218-2682, USA