SynEM, automated synapse detection for connectomics

Abstract
Data availability
Article and author information
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Abstract

Nerve tissue contains a high density of chemical synapses, about 1 per µm³ in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.

Data availability

The following data sets were generated

1. Boergens
2. K
3. Berning
4. M
5. Staffler
6. B.
7. Gour
8. A.
9. Helmstaedter
10. M.
(2017) SBEM data from mouse S1 cortex for SynEM development and validation
Publicly available at Max Planck Compute Center.

https://synem.rzg.mpg.de/webdav/

The following previously published data sets were used

1. Kasthuri
2. N.
3. Hayworth
4. K. J.
5. Berger
6. D. R.
7. Schalek
8. R. L.
9. Conchello
10. J. A.
11. Knowles-Barley
12. S.
13. . . . Lichtman
14. J. W.
(2015) Data from: Saturated Reconstruction of a Volume of Neocortex.
doi:10.1016/j.cell.2015.06.054.

http://openconnecto.me/Kasthurietal2014

Article and author information

Author details

Benedikt Staffler

Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany

Competing interests
The authors declare that no competing interests exist.
Manuel Berning

Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-3679-8363
Kevin M Boergens

Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany

Competing interests
The authors declare that no competing interests exist.
Anjali Gour

Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany

Competing interests
The authors declare that no competing interests exist.
Patrick van der Smagt

Data Lab, Volkswagen Group, Munich, Germany

Competing interests
The authors declare that no competing interests exist.
Moritz Helmstaedter

Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany

For correspondence
mh@brain.mpg.de

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-7973-0767

Funding

Max-Planck Society (Open access funding)

Benedikt Staffler
Manuel Berning
Kevin M Boergens
Anjali Gour
Moritz Helmstaedter

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All animal experiments were performed in accordance with the guidelines for the Use of Laboratory Animals of the Max Planck Society and approved by the local authorities Regierungspräsidium Oberbayern, AZ 55.2-1-54-2532.3-103-12.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.