DOI: 10.5061/dryad.5hqbzkh8b
title: Ground truth data used to train the synapse classifier used in Lillvis et al., 2022 for ExLLSM circuit reconstruction

description: Brain function is mediated by the physiological coordination of a vast,
 intricately connected network of molecular and cellular components. The
 physiological properties of network components can be quantified with high
 throughput; the ability to assess many animals per study has been key to
 relating physiological properties to behavior. Conversely, detailed
 anatomical properties (e.g., the synaptic connectivity of
 molecularly-defined cell types across an entire circuit) are presently
 quantifiable only with low throughput; thus we know very little about how
 network structure, and structural variation, influences behavior. For
 neuroanatomical reconstruction there is a methodological gulf between
 electron-microscopic (EM) methods, which yield dense connectomes (but at
 great expense and low throughput) and light-microscopic methods, which
 provide molecular and cell-type specificity with high throughput (but
 without synaptic resolution). We developed a high-throughput analysis
 pipeline and imaging protocol using tissue expansion and light sheet
 microscopy (ExLLSM) to rapidly reconstruct selected circuits across many
 animals with single-synapse resolution and molecular contrast. Using
 Drosophila to validate this approach, we demonstrate that it yields
 synaptic counts similar to those obtained by EM, enables synaptic
 connectivity to be compared across sex and experience, and can be used to
 correlate structural connectivity, functional connectivity, and behavior.
 This approach fills a critical methodological gap in studying variability
 in the structure and function of neural circuits across individuals within
 and between species. Here, we share the data used to train the synapse
 classifier that was utilized in the analysis pipeline. All additional
 software, code, and usage examples to train and run the classifier can be
 found at Github:
 https://github.com/JaneliaSciComp/exllsm-circuit-reconstruction
description: <strong>Automatic synapse
 classification</strong> Presynaptic sites can be identified
 as clusters of BRP proteins (Ehmann et al., 2017). Using 8X ExLLSM and
 labeling BRP with the nc82 antibody (Wagh et al., 2006) or the STaR-BRP
 reporter (Chen et al., 2014b), discrete clusters of fluorescent antibodies
 were present that, as expected (Schneider-Mizell et al., 2016), varied
 significantly in shape and  size across the
 <em>Drosophila</em> brain (Figure 1H-K). We tested using
 ilastik (Sommer et al., 2011), a 3D VGG shaped neural network (Simonyan
 and Zisserman, 2014), and 3D U-Net shaped neural network (Çiçek et al.,
 2016) to segment these heterogeneous structures from non-specific antibody
 labels and background signals. On our data, we found that the neural
 networks performed better than ilastik and similarly to each other, and
 that the U-Net was faster than the VGG. Therefore, we elected to train a
 U-Net convolutional neural network to automatically classify presynaptic
 sites.   To generate ground truth data for
 training the U-Net, we made 100x100x100 and 500x500x500 pixel crops of BRP
 staining (as labeled using the nc82 antibody) using the Fiji N5 Viewer. We
 considered clusters of three or more BRP labels in close proximity that
 fell along a common plane to be presynaptic sites. We semi-automatically
 segmented these presynaptic sites from non-specific antibody labels and
 background signals using VVD Viewer. This semi-automatic segmentation was
 accomplished similarly to semi-automatic neuron segmentation: the VVD
 Viewer Component Analyzer tool was used to extract signal from background
 followed by manual inspection of each potential presynaptic site. In
 total, we segmented over 10,000 presynaptic sites in image crops from 25
 different brains. Crops were made from the optic lobe, mushroom body,
 lateral horn, central complex, antennal lobe, and protocerebrum.
 We
 used these raw image data crops and manually segmented presynaptic sites
 to train the U-Net for 3000 epochs until the loss, accuracy, and error
 rates plateaued. The entire synapse classification and assignment pipeline
 includes a post-U-Net processing workflow. This post-U-Net workflow
 includes a watershed segmentation step to segment individual synaptic
 sites and a size filter to remove connected components below a given size
 threshold. For presynaptic sites labeled by nc82 or STaR-BRP, objects
 smaller than 400 pixels were removed. We
 evaluated the results of this synapse detection pipeline (including
 post-U-Net watershed segmentation and 400 pixel size thresholding) by
 running it on data crops of BRP labeled by nc82 from the optic lobe,
 protocerebrum, and lateral horn of three brain samples that were not
 included in the training. We compared these results to the manually
 segmented ground truth data (2300 presynaptic sites) of these image
 volumes. The final synapse detection pipeline had an average precision of
 94% and recall of 88% (Fig. 1L-Q). Here, we
 include the ground truth data used to train the model. On Github, we
 include the trained model used for classifying synaptic sites, code and
 instructions to train the classifier, and code and instructions to
 calculate performance of the classifier (https://github.com/JaneliaSciComp/exllsm-synapse-detector). These components can be run locally or on a compute cluster, and can be run independently or as part of several common use workflows described below (https://github.com/JaneliaSciComp/exllsm-circuit-reconstruction).
description: Any software that can open .nrrd files (e.g. Fiji: https://imagej.net/software/fiji/, VVDViewer: https://github.com/JaneliaSciComp/VVDViewer) can be used to open the data files.