Reconstruction of genetically identified neurons imaged by serial-section electron microscopy
- Harvard University, United States
- University of Notre Dame, United States
- California Institute of Technology, United States
- Harvard, United States
Abstract
Resolving patterns of synaptic connectivity in neural circuits currently requires serial section electron microscopy. However, complete circuit reconstruction is prohibitively slow and may not be necessary for many purposes such as comparing neuronal structure and connectivity among multiple animals. Here, we present an alternative strategy, targeted reconstruction of specific neuronal types. We used viral vectors to deliver peroxidase derivatives, which catalyze production of an electron-dense tracer, to genetically identified neurons, and developed a protocol that enhances the electron-density of the labeled cells and while retaining quality of the ultrastructure. The high contrast of the marked neurons enabled two innovations that dramatically speed data acquisition: targeted high-resolution reimaging of regions selected from rapidly-acquired lower resolution reconstruction, and an unsupervised segmentation algorithm. This pipeline reduces imaging and reconstruction times by at least two orders of magnitude, facilitating directed inquiry of circuit motifs.
Data availability
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Data from: Reconstruction of genetically identified neurons imaged by serial-section electron microscopy.Available at Dryad Digital Repository under a CC0 Public Domain Dedication.
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Ethics
Animal experimentation: Animals were used in accordance with NIH guidelines and protocols approved by Institutional Animal Use and Care Committee at Harvard University (Protocol 233 #92_19).
Copyright
© 2016, Joesch et al.
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.
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Reconstruction of genetically identified neurons imaged by serial-section electron microscopy
eLife 5:e15015.
https://doi.org/10.7554/eLife.15015
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