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The recent development of cellular imaging techniques and the application of genetically encoded sensors of neuronal activity led to significant methodological progress in neurobiological studies. These methods often result in complex and large data sets consisting of image stacks or sets of multichannel fluorescent images. The detection of synapses, visualized by fluorescence labeling, is one major challenge in the analysis of these datasets, due to variations in synapse shape, size, and fluorescence intensity across the images. For their detection, most labs use manual or semi-manual techniques that are time-consuming and error-prone. We developed SynEdgeWs, a MATLAB-based segmentation algorithm that combines the application of an edge filter, morphological operators, and marker-controlled watershed segmentation. SynEdgeWs does not need training data and works with low user intervention. It was superior to methods based on cutoff thresholds and local maximum guided approaches in a realistic set of data. We implemented SynEdgeWs in two automatized routines that allow accurate, direct, and unbiased identification of fluorescently labeled synaptic puncta and their consecutive analysis. SynEval routine enables the analysis of three-channel images, and ImgSegRout routine processes image stacks. We tested the feasibility of ImgSegRout on a realistic live-cell imaging data set from experiments designed to monitor neurotransmitter release using synaptic phluorins. Finally, we applied SynEval to compare synaptic vesicle recycling evoked by electrical field stimulation and chemical depolarization in dissociated cortical cultures. Our data indicate that while the proportion of active synapses does not differ between stimulation modes, significantly more vesicles are mobilized upon chemical depolarization.

Scientists have created the most detailed map of the fruit fly brain to date, identifying over 25,000 neurons and 20 million synapses.

Abstract Background The locations and shapes of synapses are important in reconstructing connectomes and analyzing synaptic plasticity. However, current synapse detection and segmentation methods are still not adequate for accurately acquiring the synaptic connectivity, and they cannot effectively alleviate the burden of synapse validation. Results We propose a fully automated method that relies on deep learning to realize the 3D reconstruction of synapses in electron microscopy (EM) images. The proposed method consists of three main parts: (1) training and employing the faster region convolutional neural networks (R-CNN) algorithm to detect synapses, (2) using the z-continuity of synapses to reduce false positives, and (3) combining the Dijkstra algorithm with the GrabCut algorithm to obtain the segmentation of synaptic clefts. Experimental results were validated by manual tracking, and the effectiveness of our proposed method was demonstrated. The experimental results in anisotropic and isotropic EM volumes demonstrate the effectiveness of our algorithm, and the average precision of our detection (92.8% in anisotropy, 93.5% in isotropy) and segmentation (88.6% in anisotropy, 93.0% in isotropy) suggests that our method achieves state-of-the-art results. Conclusions Our fully automated approach contributes to the development of neuroscience, providing neurologists with a rapid approach for obtaining rich synaptic statistics.

Application-specific validation of antibodies is a critical prerequisite for their successful use. Here we introduce an automated framework for characterization and screening of antibodies against synaptic molecules for high-resolution immunofluorescence array tomography (AT). The proposed Synaptic Antibody Characterization Tool (SACT) is designed to provide an automatic, robust, flexible, and efficient tool for antibody characterization at scale. SACT automatically detects puncta of immunofluorescence labeling from candidate antibodies and determines whether a punctum belongs to a synapse. The molecular composition and size of the target synapses expected to contain the antigen is determined by the user, based on biological knowledge. Operationally, the presence of a synapse is defined by the colocalization or adjacency of the candidate antibody punctum to one or more reference antibody puncta. The outputs of SACT are automatically computed measurements such as target synapse density and target specificity ratio that reflect the sensitivity and specificity of immunolabeling with a given candidate antibody. These measurements provide an objective way to characterize and compare the performance of different antibodies against the same target, and can be used to objectively select the antibodies best suited for AT and potentially for other immunolabeling applications.