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Abstract As the serial section community transitions to volume electron microscopy, tools are needed to balance rapid segmentation efforts with documenting the fine detail of structures that support cell function. New annotation applications should be accessible to users and meet the needs of the neuroscience and connectomics communities while also being useful across other disciplines. Issues not currently addressed by a single, modern annotation application include: 1) built-in curation systems with utilities for expert intervention to provide quality assurance, 2) integrated alignment features that allow for image registration on-the-fly as image flaws are discovered during annotation, 3) simplicity for non-specialists within and beyond the neuroscience community, 5) a system to store experimental meta-data with annotation data in a way that researchers remain masked regarding condition to avoid potential biases, 6) local management of large datasets, 7) fully open-source codebase allowing development of new tools, and more. Here, we present PyReconstruct, a modern successor to the Reconstruct annotation tool. PyReconstruct operates in a field-agnostic manner, runs on all major operating systems, breaks through legacy RAM limitations, features an intuitive and collaborative curation system, and employs a flexible and dynamic approach to image registration. It can be used to analyze, display, and publish experimental or connectomics data. PyReconstruct is suited for generating ground truth to implement in automated segmentation, outcomes of which can be returned to PyReconstruct for proofreading and quality control. Significance statementIn neuroscience, the emerging field of connectomics has produced annotation tools for reconstruction that prioritize circuit connectivity across microns to centimeters and farther. Determining the strength of synapses forming the connections is crucial to understand function and requires quantification of their nanoscale dimensions and subcellular composition. PyReconstruct, successor to the early annotation tool Reconstruct, meets these requirements for synapses and other structures well beyond neuroscience. PyReconstruct lifts many restrictions of legacy Reconstruct and offers a user-friendly interface, integrated curation, dynamic alignment, nanoscale quantification, 3D visualization, and more. Extensive compatibility with third-party software provides access to the expanding tools from the connectomics and imaging communities. 

Abstract Synapse clustering facilitates circuit integration, learning, and memory. Long-term potentiation (LTP) of mature neurons produces synapse enlargement balanced by fewer spines, raising the question of how clusters form despite this homeostatic regulation of total synaptic weight. Three-dimensional reconstruction from serial section electron microscopy (3DEM) revealed the shapes and distributions of smooth endoplasmic reticulum (SER) and polyribosomes, subcellular resources important for synapse enlargement and spine outgrowth. Compared to control stimulation, synapses were enlarged two hours after LTP on resource-rich spines containing polyribosomes (4% larger than control) or SER (15% larger). SER in spines shifted from a single tubule to complex spine apparatus after LTP. Negligible synapse enlargement (0.6%) occurred on resource-poor spines lacking SER and polyribosomes. Dendrites were divided into discrete synaptic clusters surrounded by asynaptic segments. Spine density was lowest in clusters having only resource-poor spines, especially following LTP. In contrast, resource-rich spines preserved neighboring resource-poor spines and formed larger clusters with elevated total synaptic weight following LTP. These clusters also had more shaft SER branches, which could sequester cargo locally to support synapse growth and spinogenesis. Thus, resources appear to be redistributed to synaptic clusters with LTP-related synapse enlargement while homeostatic regulation suppressed spine outgrowth in resource-poor synaptic clusters.