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Method Article
A flexible methodological pipeline to identify, visualize, and quantify thin subcellular neuronal processes within focused ion beam scanning electron microscopy image volumes using user-friendly open-source software packages.
Recent advances in scanning electron microscope technologies now permit the rapid three-dimensional (3D) analysis of ultrathin subcellular processes. Here, a methodological pipeline is presented to identify, visualize, and analyze thin neuronal processes, such as those that project into the presynaptic boutons of other neurons (termed 'spinules'). Using freely available software packages, this protocol demonstrates how to use a decision tree to identify common neuronal subcellular structures using morphological criteria within focused ion beam scanning electron microscopy (FIB-SEM) image volumes, with particular attention on identifying a diversity of spinules projecting into presynaptic boutons. In particular, this protocol describes how to trace spinules within neuronal synapses to produce 3D reconstructions of these thin subcellular projections, their parent neurites, and postsynaptic partners. Additionally, the protocol includes a list of freely available open-source software programs for analyzing FIB-SEM data and offers tips (e.g., smoothing, lighting) toward improving 3D reconstructions for visualization and publication. This adaptable protocol offers an entry point into the rapid nanoscale analysis of subcellular structures within FIB-SEM image volumes.
Investigations into the structure-function relationships of nanometer-thin subcellular components often benefit from 3D visualization and analysis1. However, serial section transmission electron microscopy studies have been temporally and spatially constrained by the necessity to use a diamond knife to cut and align hundreds to thousands of ≥40 nm serial ultrathin sections. These constraints have limited the ability to sample and effectively analyze thin (<40 nm in diameter) subcellular structures, and the necessity to become proficient at ultrathin serial sectioning has hampered the application of 3D structural analyses2,3. However, recent advancements in focused ion beam scanning electron microscopy (FIB-SEM) have revolutionized the speed and resolution of obtainable image volumes and now permit the quantitative analysis of thin subcellular structures such as smooth endoplasmic reticula4,5, neuronal synapses3,6, and synaptic vesicles7,8 at scale. In addition, wider use of FIB-SEM image volumes has accelerated the development of freely accessible FIB-SEM image volume repositories9 and 3D analysis software (e.g., Espina10, IMOD11, Neuromorph12, Reconstruct13, TrakEM14) that expand the reach of this technology and now enable investigations into the structure and function of fine subcellular structures.
One such nanoscale subcellular feature is the neuronal synaptic 'spinule.' Spinules are thin (~0.06-0.15 µm wide, 0.1-1 µm long), finger-like projections that emanate from one neuron and become encapsulated by the neurite (e.g., presynaptic bouton) of another neuron15,16. Spinules embedded within neuronal processes have been reported in the electron microscopy literature for almost 60 years17, and spinule-like protrusions are a conserved18,19,20 and ubiquitous21,22 feature of excitatory synapses. Nevertheless, despite the pervasiveness of synaptic spinules, their function(s) remain obscure, and there is a dearth of necessary data to explain their abundance and structural conservation. This lack of experimental characterization of spinules has mostly been due to the difficulty in quantitatively analyzing spinule prevalence and sizes. Their small dimensions are most suited to analyses using a previously unattainable z (depth) resolution (i.e., ≤15 nm).
Here, a FIB-SEM analysis pipeline is presented for identifying, visualizing, and analyzing thin (with cross sections ≤40 nm wide) subcellular structures that can serve as an entry point for FIB-SEM newcomers and experts alike. This protocol serves as a primer for identifying neuronal subcellular structures within a 3D FIB-SEM image volume, emphasizing how to use specific criteria to recognize and classify subtypes of spinules and synapses. Additionally, the protocol demonstrates how to import image volumes into a free 3D analysis software platform (Reconstruct), use this software to trace spinules within excitatory neuronal synapses, and produce 3D reconstructions of these subcellular projections, their parent neurites, and encapsulating presynaptic boutons. Lastly, the protocol shows how to use free, open-source 3D rending software (Blender) to smooth the 'skin' on 3D reconstructions for visualization and potential publication, detailing the advantages and potential pitfalls of this technique.
1. Image volume data and subcellular object size: considerations and registration
2. Neurite and synaptic spinule identification
3. Determine the area of interest and transfer image volume to 3D analysis software
4. 3D reconstructions and analysis of thin subcellular structures in Reconstruct
NOTE: It is highly advantageous to use a mouse equipped with a wheel while using Reconstruct. In addition, if most or all traces will be performed manually, using a stylus to draw outlines on a computer with a touchscreen can dramatically increase trace efficiency.
5. Importing, recoloring, smoothing, and adding transparency to 3D reconstructions in Blender
NOTE: For detailed assistance in using Blender, please consult the Blender manual and/or the myriad "how to" videos on using each Blender function (simply do a web search for Blender AND Desired Function). What follows is a short primer on how to recolor, smooth, and add transparency to 3D reconstructions.
Quantifying the percentage of synaptic spinules within the excitatory presynaptic bouton population in ferret primary visual cortex
Although spinule-like protrusions from neurites into excitatory presynaptic boutons have been observed for decades19,26, their potential importance for synaptic function has remained obscure. These experiments were designed to determine the proportion of excitatory presynaptic boutons containing spinules throug...
This FIB-SEM image volume analysis pipeline can produce reliable 3D reconstructions and quantitative measurements of thin subcellular structures. While current semi-automated techniques using deep neural network and segmentation algorithms can increase the speed and efficiency in reconstructing cellular structures possessing relatively high membrane contrast within large image volumes33, many subcellular structures (e.g., spinules, smooth endoplasmic reticula, endosomes) will remain difficult to r...
The authors have no conflicts of interest to disclose.
This work was supported by the University of Washington Bridge Fund and the University of Washington Tacoma Pilot RRF Fund. Many thanks to Dr. Claudia Lopez and Dr. Jessica Riesterer from the MMC at Oregon Health & Sciences University for FIB-SEM technical support, Dr. Graham Knott for the use of the CA1 FIB-SEM image volume, and the UW Tacoma students in the Neuronal Reconstructions (TBIOMD 495) course for their patience and excellence in working with this protocol.
Name | Company | Catalog Number | Comments |
Fiji (ImageJ) | https://imagej.net/software/fiji/downloads | ||
Reconstruct | https://synapseweb.clm.utexas.edu/software-0 | ||
Blender | Blender Foundation | https:/www.blender.org |
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