Format

Send to

Choose Destination
Cell. 2015 Jul 30;162(3):648-61. doi: 10.1016/j.cell.2015.06.054.

Saturated Reconstruction of a Volume of Neocortex.

Author information

1
Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA. Electronic address: bobby.kasthuri@gmail.com.
2
Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
3
Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
4
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
5
Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA; School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
6
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
7
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218-2128, USA.
8
Department of Statistical Science and Neurobiology, Duke University, Durham, NC 27708, USA.
9
Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
10
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218-2682, USA.
11
Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA. Electronic address: jeff@mcb.harvard.edu.

Abstract

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.

PMID:
26232230
DOI:
10.1016/j.cell.2015.06.054
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center