Dataset Information

Nanoscale 3D EM reconstructions reveal intrinsic mechanisms of structural diversity of chemical synapses.

ABSTRACT: Chemical synapses of shared cellular origins have remarkably heterogeneous structures, but how this diversity is generated is unclear. Here, we use three-dimensional (3D) electron microscopy and artificial intelligence algorithms for image processing to reconstruct functional excitatory microcircuits in the mouse hippocampus and microcircuits in which neurotransmitter signaling is permanently suppressed with genetic tools throughout the lifespan. These nanoscale analyses reveal that experience is dispensable for morphogenesis of synapses with different geometric shapes and contents of membrane organelles and that arrangement of morphologically distinct connections in local networks is stochastic. Moreover, loss of activity increases the variability in sizes of opposed pre- and postsynaptic structures without disrupting their alignments, suggesting that inherently variable weights of naive connections become progressively matched with repetitive use. These results demonstrate that mechanisms for the structural diversity of neuronal synapses are intrinsic and provide insights into how circuits essential for memory storage assemble and integrate information.

SUBMITTER: Zhu Y

PROVIDER: S-EPMC8354523 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Nanoscale 3D EM reconstructions reveal intrinsic mechanisms of structural diversity of chemical synapses.

Zhu Yongchuan Y Uytiepo Marco M Bushong Eric E Haberl Matthias M Beutter Elizabeth E Scheiwe Frederieke F Zhang Weiheng W Chang Lyanne L Luu Danielle D Chui Brandon B Ellisman Mark M Maximov Anton A

Cell reports 20210401 1

Chemical synapses of shared cellular origins have remarkably heterogeneous structures, but how this diversity is generated is unclear. Here, we use three-dimensional (3D) electron microscopy and artificial intelligence algorithms for image processing to reconstruct functional excitatory microcircuits in the mouse hippocampus and microcircuits in which neurotransmitter signaling is permanently suppressed with genetic tools throughout the lifespan. These nanoscale analyses reveal that experience i ...[more]

PMID: 33826888

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Project description:BackgroundAs an extension of electron tomography (ET), serial section electron tomography (serial section ET) aims to align the tomographic images of multiple thick tissue sections together, to break through the volume limitation of the single section and preserve the sub-nanoscale voxel size. It could be applied to reconstruct the intact synapse, which expands about one micrometer and contains nanoscale vesicles. However, there are several drawbacks of the existing serial section ET methods. First, locating and imaging regions of interest (ROIs) in serial sections during the shooting process is time-consuming. Second, the alignment of ET volumes is difficult due to the missing information caused by section cutting and imaging. Here we report a workflow to simplify the acquisition of ROIs in serial sections, automatically align the volume of serial section ET, and semi-automatically reconstruct the target synaptic structure.ResultsWe propose an intelligent workflow to reconstruct the intact synapse with sub-nanometer voxel size. Our workflow includes rapid localization of ROIs in serial sections, automatic alignment, restoration, assembly of serial ET volumes, and semi-automatic target structure segmentation. For the localization and acquisition of ROIs in serial sections, we use affine transformations to calculate their approximate position based on their relative location in orderly placed sections. For the alignment of consecutive ET volumes with significantly distinct appearances, we use multi-scale image feature matching and the elastic with belief propagation (BP-Elastic) algorithm to align them from coarse to fine. For the restoration of the missing information in ET, we first estimate the number of lost images based on the pixel changes of adjacent volumes after alignment. Then, we present a missing information generation network that is appropriate for small-sample of ET volume using pre-training interpolation network and distillation learning. And we use it to generate the missing information to achieve the whole volume reconstruction. For the reconstruction of synaptic ultrastructures, we use a 3D neural network to obtain them quickly. In summary, our workflow can quickly locate and acquire ROIs in serial sections, automatically align, restore, assemble serial sections, and obtain the complete segmentation result of the target structure with minimal manual manipulation. Multiple intact synapses in wild-type rat were reconstructed at a voxel size of 0.664 nm/voxel to demonstrate the effectiveness of our workflow.ConclusionsOur workflow contributes to obtaining intact synaptic structures at the sub-nanometer scale through serial section ET, which contains rapid ROI locating, automatic alignment, volume reconstruction, and semi-automatic synapse reconstruction. We have open-sourced the relevant code in our workflow, so it is easy to apply it to other labs and obtain complete 3D ultrastructures which size is similar to intact synapses with sub-nanometer voxel size.

Dataset Information

Nanoscale 3D EM reconstructions reveal intrinsic mechanisms of structural diversity of chemical synapses.

Publications

Nanoscale 3D EM reconstructions reveal intrinsic mechanisms of structural diversity of chemical synapses.

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