Probabilistic fluorescence-based synapse detection

Developing a Toolbox of Antibodies Validated for Array Tomography-Based Imaging of Brain Synapses

Antibody (Ab)-based imaging techniques rely on reagents whose performance may be application specific. Because commercial antibodies are validated for only a few purposes, users interested in other applications may have to perform extensive in-house antibody testing. Here, we present a novel application-specific proxy screening step to efficiently identify candidate antibodies for array tomography (AT), a serial section volume microscopy technique for high-dimensional quantitative analysis of the cellular proteome. To identify antibodies suitable for AT-based analysis of synapses in mammalian brain, we introduce a heterologous cell-based assay that simulates characteristic features of AT, such as chemical fixation and resin embedding that are likely to influence antibody binding. The assay was included into an initial screening strategy to generate monoclonal antibodies that can be used for AT. This approach simplifies the screening of candidate antibodies and has high predictive value for identifying antibodies suitable for AT analyses. In addition, we have created a comprehensive database of AT-validated antibodies with a neuroscience focus and show that these antibodies have a high likelihood of success for postembedding applications in general, including immunogold electron microscopy. The generation of a large and growing toolbox of AT-compatible antibodies will further enhance the value of this imaging technique.

Developing a Toolbox of Antibodies Validated for Array Tomography-Based Imaging of Brain Synapses

Antibody-based imaging techniques rely on reagents whose performance may be application-specific. Because commercial antibodies are validated for only a few purposes, users interested in other applications may have to perform extensive in-house antibody testing. Here we present a novel application-specific proxy screening step to efficiently identify candidate antibodies for array tomography (AT), a serial section volume microscopy technique for high-dimensional quantitative analysis of the cellular proteome. To identify antibodies suitable for AT-based analysis of synapses in mammalian brain, we introduce a heterologous cell-based assay that simulates characteristic features of AT, such as chemical fixation and resin embedding that are likely to influence antibody binding. The assay was included into an initial screening strategy to generate monoclonal antibodies that can be used for AT. This approach simplifies the screening of candidate antibodies and has high predictive value for identifying antibodies suitable for AT analyses. In addition, we have created a comprehensive database of AT-validated antibodies with a neuroscience focus and show that these antibodies have a high likelihood of success for postembedding applications in general, including immunogold electron microscopy. The generation of a large and growing toolbox of AT-compatible antibodies will further enhance the value of this imaging technique.

SynQuant: an automatic tool to quantify synapses from microscopy images

MOTIVATION

Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.

RESULTS

We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.

AVAILABILITY AND IMPLEMENTATION

Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

GABA bouton subpopulations in the human dentate gyrus are differentially altered in mesial temporal lobe epilepsy

Medically intractable temporal lobe epilepsy is a devastating disease, for which surgical removal of the seizure onset zone is the only known cure. Multiple studies have found evidence of abnormal dentate gyrus network circuitry in human mesial temporal lobe epilepsy (MTLE). Principal neurons within the dentate gyrus gate entorhinal input into the hippocampus, providing a critical step in information processing. Crucial to that role are GABA-expressing neurons, particularly parvalbumin (PV)-expressing basket cells (PVBCs) and chandelier cells (PVChCs), which provide strong, temporally coordinated inhibitory signals. Alterations in PVBC and PVChC boutons have been described in epilepsy, but the value of these studies has been limited due to methodological hurdles associated with studying human tissue. We developed a multilabel immunofluorescence confocal microscopy and a custom segmentation algorithm to quantitatively assess PVBC and PVChC bouton densities and to infer relative synaptic protein content in the human dentate gyrus. Using en bloc specimens from MTLE subjects with and without hippocampal sclerosis, paired with nonepileptic controls, we demonstrate the utility of this approach for detecting cell-type specific synaptic alterations. Specifically, we found increased density of PVBC boutons, while PVChC boutons decreased significantly in the dentate granule cell layer of subjects with hippocampal sclerosis compared with matched controls. In contrast, bouton densities for either PV-positive cell type did not differ between epileptic subjects without sclerosis and matched controls. These results may explain conflicting findings from previous studies that have reported both preserved and decreased PV bouton densities and establish a new standard for quantitative assessment of interneuron boutons in epilepsy.NEW & NOTEWORTHY A state-of-the-art, multilabel immunofluorescence confocal microscopy and custom segmentation algorithm technique, developed previously for studying synapses in the human prefrontal cortex, was modified to study the hippocampal dentate gyrus in specimens surgically removed from patients with temporal lobe epilepsy. The authors discovered that chandelier and basket cell boutons in the human dentate gyrus are differentially altered in mesial temporal lobe epilepsy.

Multifaceted Changes in Synaptic Composition and Astrocytic Involvement in a Mouse Model of Fragile X Syndrome

Fragile X Syndrome (FXS), a common inheritable form of intellectual disability, is known to alter neocortical circuits. However, its impact on the diverse synapse types comprising these circuits, or on the involvement of astrocytes, is not well known. We used immunofluorescent array tomography to quantify different synaptic populations and their association with astrocytes in layers 1 through 4 of the adult somatosensory cortex of a FXS mouse model, the FMR1 knockout mouse. The collected multi-channel data contained approximately 1.6 million synapses which were analyzed using a probabilistic synapse detector. Our study reveals complex, synapse-type and layer specific changes in the neocortical circuitry of FMR1 knockout mice. We report an increase of small glutamatergic VGluT1 synapses in layer 4 accompanied by a decrease in large VGluT1 synapses in layers 1 and 4. VGluT2 synapses show a rather consistent decrease in density in layers 1 and 2/3. In all layers, we observe the loss of large inhibitory synapses. Lastly, astrocytic association of excitatory synapses decreases. The ability to dissect the circuit deficits by synapse type and astrocytic involvement will be crucial for understanding how these changes affect circuit function, and ultimately defining targets for therapeutic intervention.

DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images

Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imaging facilitates a bottom-up approach to synapse classification and phenotypic description. Objective automation of efficient and reliable synapse detection within these datasets is essential for the high-throughput investigation of synaptic features. Convolutional neural networks can solve this generalized problem of synapse detection, however, these architectures require large numbers of training examples to optimize their thousands of parameters. We propose DoGNet, a neural network architecture that closes the gap between classical computer vision blob detectors, such as Difference of Gaussians (DoG) filters, and modern convolutional networks. DoGNet is optimized to analyze highly multiplexed microscopy data. Its small number of training parameters allows DoGNet to be trained with few examples, which facilitates its application to new datasets without overfitting. We evaluate the method on multiplexed fluorescence imaging data from both primary mouse neuronal cultures and mouse cortex tissue slices. We show that DoGNet outperforms convolutional networks with a low-to-moderate number of training examples, and DoGNet is efficiently transferred between datasets collected from separate research groups. DoGNet synapse localizations can then be used to guide the segmentation of individual synaptic protein locations and spatial extents, revealing their spatial organization and relative abundances within individual synapses. The source code is publicly available: https://github.com/kulikovv/dognet.

Development of a Novel FIJI-Based Method to Investigate Neuronal Circuitry in Neonatal Mice

The emergence of systems neuroscience tools requires parallel generation of objective analytical workflows for experimental neuropathology. We developed an objective analytical workflow that we used to determine how specific autonomic neural lineages change during postnatal development. While a wealth of knowledge exists regarding postnatal alterations in respiratory neural function, how these neural circuits change and develop in the weeks following birth remains less clear. In this study, we developed our workflow by combining genetic mouse modeling and quantitative immunofluorescent confocal microscopy and used this to examine the postnatal development of neural circuits derived from the transcription factors NKX2.2 and OLIG3 into three medullary respiratory nuclei. Our automated FIJI-based image analysis workflow rapidly and objectively quantified synaptic puncta in user-defined anatomic regions. Using our objective workflow, we found that the density and estimated total number of Nkx2.2-derived afferents into the pre-Bötzinger Complex significantly decreased with postnatal age during the first three weeks of postnatal life. These data indicate that Nkx2.2-derived structures differentially influence pre-Bötzinger Complex respiratory oscillations at different stages of postnatal development.

Q&A: Array tomography

Array tomography encompasses light and electron microscopy modalities that offer unparalleled opportunities to explore three-dimensional cellular architectures in extremely fine structural and molecular detail. Fluorescence array tomography achieves much higher resolution and molecular multiplexing than most other fluorescence microscopy methods, while electron array tomography can capture three-dimensional ultrastructure much more easily and rapidly than traditional serial-section electron microscopy methods. A correlative fluorescence/electron microscopy mode of array tomography furthermore offers a unique capacity to merge the molecular discrimination strengths of multichannel fluorescence microscopy with the ultrastructural imaging strengths of electron microscopy. This essay samples the first decade of array tomography, highlighting applications in neuroscience.

A Computational Synaptic Antibody Characterization Tool for Array Tomography

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.