Neuronal circuit reconstruction using serial block-face scanning electron microscopy

Abstract

Event Abstract Back to Event Neuronal circuit reconstruction using serial block-face scanning electron microscopy A fundamental limitation to understanding the function of neuronal circuits is the lack of complete wiring (connection) diagrams. In many nervous systems, local circuits are comprised of neurons extending several hundreds of microns. The tissue volumes that are required to reconstruct such circuits are on the order of 10^8 um3 representing several terabytes of pixel information at the pixel size (20-30nm) needed to trace all neuronal processes. It is therefore necessary to automate both the acquisition and analysis of the data. Data acquisition of 3D data with the required resolution is possible in a fully automated way by using serial block-face scanning electron microscopy (SBFSEM). Images are acquired from the face of a tissue block with slices as thin as 25 nm removed by ultra-thin sectioning with an oscillating diamond knife. This technique replaces the difficult and labor-intensive process of manually cutting, mounting, and imaging sections in a transmission electron microscope. We acquire data with nearly isotropic resolution at lateral resolutions of ~20 nm and 25 nm section thickness. We envision the ability to automatically collect data from volumes large enough to encompass complete circuits at a resolution capable of capturing all the fine, convoluted neuronal processes. The segmentation of neurons from images is critically dependent on a high signal contrast between structures of interest. We have developed a staining strategy that labels cell surfaces with an electron dense product. This method leaves the cytosol and organelles largely unstained and thus results in a high contrast between intracellular space and cell surfaces, allowing the profiles of neuronal processes to be easily identified. The manual tracing of each and every neuron through a large volume is prohibitively slow, hence the tracing of neurons must be automated. We first manually segment a representative sub-volume into intracellular and cell surface regions. This segmented sub-volume becomes a training dataset for machine learning algorithms. Local cubes of data (5-7 voxels^3) are used to generalize voxel connectivity from the training to test data sets. A crucial step, however, is the validation of the obtained segmentations so as to obtain an estimate of the reconstruction reliability. We have developed tools that allow browsing through large 3D data sets and the efficient creation of 3-dimensional skeletons by human tracers. The comparison between skeletons and automated segmentations allows the estimation of break and merger probabilities, which are needed for the optimization of the classification and segmentation algorithms. Also, this data provides a measure for the reliability of the reconstructed circuitry. Finally, we are attempting to correlate functional recordings with circuit structure. Proof of principle experiments have shown that bulk loading of calcium indicators in the mammalian retina allow functional signals to be imaged without significantly damaging tissue ultrastructure. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Neuronal circuit reconstruction using serial block-face scanning electron microscopy. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.103 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Feb 2009; Published Online: 02 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.