FIJI Macro 3D ART VeSElecT: 3D Automated Reconstruction Tool for Vesicle Structures of Electron Tomograms

Abstract

Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial.

An ALS-associated mutation in human FUS reduces neurotransmission fromC. elegansmotor neurons to muscles

“…Given the presence of large endosomes in FUS501 terminals, we used automated reconstruction and classification tools (Kaltdorf et al, 2017, 2018) to reconstruct the vesicle pools from tomograms of cholinergic synapses. In total, we automatically reconstructed and classified 1,030 vesicles for FUS501, 164 vesicles for FUSwt, and 2,053 vesicles for N2 at the cholinergic NMJs.…”

“…Median (closed circles) and mean (open circles) are given on each plot. (A, B) Vesicle reconstruction and classification via the 3D ART VeSElecT and automated classification Fiji macros (Kaltdorf et al, 2017, 2018). (A) Linear distances of the center points of all vesicles to the center of the active zone (AZ) as given by the classification macro.…”

Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning

“… 1) preparation: StackSegmented (registered vesicles) and StackDuplicateScale (scaled tomogram) from 3D ART VeSElecT [ 22 ] are used as input; the user is requested to mark the active zone via point-selection (semi-automated). 2) readout of features: gray value (gv), radius (r), vesicles coordinates and gray value standard deviation (GVSD) in the middle slice of vesicles are determined; the distance to active zone (distAZ) is calculated.…”

“…DCVs are characterized by their eponymous dark, electron dense core that typically contains neuropeptides, small protein molecules with about 3–100 amino acids [ 1 ] or other protein cargos such as active zone proteins [ 3 ]. DCVs have a bigger average size of about 60–80 nm in comparison to CCVs, which have a much smaller diameter [ 19 ], that can vary in different organisms or developmental states [ 22 , 23 ]. Whereas CCVs cluster around the active zone, DCVs tend to locate further away from the release site, typically at the rim of the SV pool as described for C .…”

“…To circumvent these shortcomings we previously developed 3D ART VeSElecT , a workflow for automated reconstruction of vesicles [ 22 ]: This ImageJ/Fiji tool allows for automated reconstruction of synaptic vesicle pools in electron tomograms, but is not able to differentiate between different types of vesicles. To solve this issue, we here present a python-based machine learning approach that trains vesicle classifiers, and a straightforward ImageJ/Fiji tool that applies a trained classifier to previously detected vesicles.…”