Neuron tracing in perspective

Automated Analysis of Neuronal Morphology in 2D Fluorescence Micrographs through an Unsupervised Semantic Segmentation of Neurons

Brain function emerges from a highly complex network of specialized cells that are interlinked by billions of synapses. The synaptic connectivity between neurons is established between the elongated processes of their axons and dendrites or, together, neurites. To establish these connections, cellular neurites have to grow in highly specialized, cell-type dependent patterns covering extensive distances and connecting with thousands of other neurons. The outgrowth and branching of neurites are tightly controlled during development and are a commonly used functional readout of imaging in the neurosciences. Manual analysis of neuronal morphology from microscopy images, however, is very time intensive and prone to bias. Most automated analyses of neurons rely on reconstruction of the neuron as a whole without a semantic analysis of each neurite. A fully-automated classification of all neurites still remains unavailable in open-source software. Here we present a standalone, GUI-based software for batch-quantification of neuronal morphology in two-dimensional fluorescence micrographs of cultured neurons with minimal requirements for user interaction. Single neurons are first reconstructed into binarized images using a Hessian-based segmentation algorithm to detect thin neurite structures combined with intensity- and shape-based reconstruction of the cell body. Neurites are then classified into axon, dendrites and their branches of increasing order using a geodesic distance transform of the cell skeleton. The software was benchmarked against a published dataset and reproduced the phenotype observed after manual annotation. Our tool promises accelerated and improved morphometric studies of neuronal morphology by allowing for consistent and automated analysis of large datasets.

From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

Sex-specific impacts of prenatal bisphenol A exposure on genes associated with cortical development, social behaviors, and autism in the offspring's prefrontal cortex

BACKGROUND

Recent studies have shown that prenatal BPA exposure altered the transcriptome profiles of autism-related genes in the offspring's hippocampus, disrupting hippocampal neuritogenesis and causing male-specific deficits in learning. However, the sex differences in the effects of prenatal BPA exposure on the developing prefrontal cortex, which is another brain region highly implicated in autism spectrum disorder (ASD), have not been investigated.

METHODS

We obtained transcriptome data from RNA sequencing analysis of the prefrontal cortex of male and female rat pups prenatally exposed to BPA or control and reanalyzed. BPA-responsive genes associated with cortical development and social behaviors were selected for confirmation by qRT-PCR analysis. Neuritogenesis of primary cells from the prefrontal cortex of pups prenatally exposed to BPA or control was examined. The social behaviors of the pups were assessed using the two-trial and three-chamber tests. The male-specific impact of the downregulation of a selected BPA-responsive gene (i.e., Sema5a) on cortical development in vivo was interrogated using siRNA-mediated knockdown by an in utero electroporation technique.

RESULTS

Genes disrupted by prenatal BPA exposure were associated with ASD and showed sex-specific dysregulation. Sema5a and Slc9a9, which were involved in neuritogenesis and social behaviors, were downregulated only in males, while Anxa2 and Junb, which were also linked to neuritogenesis and social behaviors, were suppressed only in females. Neuritogenesis was increased in males and showed a strong inverse correlation with Sema5a and Slc9a9 expression levels, whereas, in the females, neuritogenesis was decreased and correlated with Anxa2 and Junb levels. The siRNA-mediated knockdown of Sema5a in males also impaired cortical development in utero. Consistent with Anxa2 and Junb downregulations, deficits in social novelty were observed only in female offspring but not in males.

CONCLUSION

This is the first study to show that prenatal BPA exposure dysregulated the expression of ASD-related genes and functions, including cortical neuritogenesis and development and social behaviors, in a sex-dependent manner. Our findings suggest that, besides the hippocampus, BPA could also exert its adverse effects through sex-specific molecular mechanisms in the offspring's prefrontal cortex, which in turn would lead to sex differences in ASD-related neuropathology and clinical manifestations, which deserves further investigation.

Morphology and Fractal-Based Classifications of Neurons and Microglia in Two and Three Dimensions

Microglia and neurons live physically intertwined, intimately related structurally and functionally in a dynamic relationship in which microglia change continuously over a much shorter timescale than do neurons. Although microglia may unwind and depart from the neurons they attend under certain circumstances, in general, together both contribute to the fractal topology of the brain that defines its computational capabilities. Both neuronal and microglial morphologies are well-described using fractal analysis complementary to more traditional measures. For neurons, the fractal dimension has proved valuable for classifying dendritic branching and other neuronal features relevant to pathology and development. For microglia, fractal geometry has substantially contributed to classifying functional categories, where, in general, the more pathological the biological status, the lower the fractal dimension for individual cells, with some exceptions, including hyper-ramification. This chapter provides a review of the intimate relationships between neurons and microglia, by introducing 2D and 3D fractal analysis methodology and its applications in neuron-microglia function in health and disease.

ImageJ in Computational Fractal-Based Neuroscience: Pattern Extraction and Translational Research

To explore questions asked in neuroscience, neuroscientists rely heavily on the tools available. One such toolset is ImageJ, open-source, free, biological digital image analysis software. Open-source software has matured alongside of fractal analysis in neuroscience, and today ImageJ is not a niche but a foundation relied on by a substantial number of neuroscientists for work in diverse fields including fractal analysis. This is largely owing to two features of open-source software leveraged in ImageJ and vital to vigorous neuroscience: customizability and collaboration. With those notions in mind, this chapter's aim is threefold: (1) it introduces ImageJ, (2) it outlines ways this software tool has influenced fractal analysis in neuroscience and shaped the questions researchers devote time to, and (3) it reviews a few examples of ways investigators have developed and used ImageJ for pattern extraction in fractal analysis. Throughout this chapter, the focus is on fostering a collaborative and creative mindset for translating knowledge of the fractal geometry of the brain into clinical reality.

Effects of Tropomodulin 2 on Dendritic Spine Reorganization and Dynamics

Dendritic spines are actin-rich protrusions that receive a signal from the axon at the synapse. Remodeling of cytoskeletal actin is tightly connected to dendritic spine morphology-mediated synaptic plasticity of the neuron. Remodeling of cytoskeletal actin is required for the formation, development, maturation, and reorganization of dendritic spines. Actin filaments are highly dynamic structures with slow-growing/pointed and fast-growing/barbed ends. Very few studies have been conducted on the role of pointed-end binding proteins in the regulation of dendritic spine morphology. In this study, we evaluated the role played by tropomodulin 2 (Tmod2)-a brain-specific isoform, on the dendritic spine re-organization. Tmod2 regulates actin nucleation and polymerization by binding to the pointed end via actin and tropomyosin (Tpm) binding sites. We studied the effects of Tmod2 overexpression in primary hippocampal neurons on spine morphology using confocal microscopy and image analysis. Tmod2 overexpression decreased the spine number and increased spine length. Destroying Tpm-binding ability increased the number of shaft synapses and thin spine motility. Eliminating the actin-binding abilities of Tmod2 increased the number of mushroom spines. Tpm-mediated pointed-end binding decreased F-actin depolymerization, which may positively affect spine stabilization; the nucleation ability of Tmod2 appeared to increase shaft synapses.

Ogt Deficiency Induces Abnormal Cerebellar Function and Behavioral Deficits of Adult Mice through Modulating RhoA/ROCK Signaling

Previous studies have shown the essential roles of O-GlcNAc transferase (Ogt) and O-GlcNAcylation in neuronal development, function and neurologic diseases. However, the function of Ogt and O-GlcNAcylation in the adult cerebellum has not been well elucidated. Here, we have found that cerebellum has the highest level of O-GlcNAcylation relative to cortex and hippocampus of adult male mice. Specific deletion of Ogt in granule neuron precursors (GNPs) induces abnormal morphology and decreased size of the cerebellum in adult male Ogt deficient [conditional knock-out (cKO)] mice. Adult male cKO mice show the reduced density and aberrant distribution of cerebellar granule cells (CGCs), the disrupted arrangement of Bergman glia (BG) and Purkinje cells. In addition, adult male cKO mice exhibit aberrant synaptic connection, impaired motor coordination, and learning and memory abilities. Mechanistically, we have identified G-protein subunit α12 (Gα12) is modified by Ogt-mediated O-GlcNAcylation. O-GlcNAcylation of Gα12 facilitates its binding to Rho guanine nucleotide exchange factor 12 (Arhgef12) and consequently activates RhoA/ROCK signaling. RhoA/ROCK pathway activator LPA can rescue the developmental deficits of Ogt deficient CGCs. Therefore, our study has revealed the critical function and related mechanisms of Ogt and O-GlcNAcylation in the cerebellum of adult male mice.SIGNIFICANCE STATEMENT Cerebellar function are regulated by diverse mechanisms. To unveil novel mechanisms is critical for understanding the cerebellar function and the clinical therapy of cerebellum-related diseases. In the present study, we have shown that O-GlcNAc transferase gene (Ogt) deletion induces abnormal cerebellar morphology, synaptic connection, and behavioral deficits of adult male mice. Mechanistically, Ogt catalyzes O-GlcNAcylation of Gα12, which promotes the binding to Arhgef12, and regulates RhoA/ROCK signaling pathway. Our study has uncovered the important roles of Ogt and O-GlcNAcylation in regulating cerebellar function and cerebellum-related behavior. Our results suggest that Ogt and O-GlcNAcylation could be potential targets for some cerebellum-related diseases.

SNAP: a structure-based neuron morphology reconstruction automatic pruning pipeline

Background

Neuron morphology analysis is an essential component of neuron cell-type definition. Morphology reconstruction represents a bottleneck in high-throughput morphology analysis workflow, and erroneous extra reconstruction owing to noise and entanglements in dense neuron regions restricts the usability of automated reconstruction results. We propose SNAP, a structure-based neuron morphology reconstruction pruning pipeline, to improve the usability of results by reducing erroneous extra reconstruction and splitting entangled neurons.

Methods

For the four different types of erroneous extra segments in reconstruction (caused by noise in the background, entanglement with dendrites of close-by neurons, entanglement with axons of other neurons, and entanglement within the same neuron), SNAP incorporates specific statistical structure information into rules for erroneous extra segment detection and achieves pruning and multiple dendrite splitting.

Results

Experimental results show that this pipeline accomplishes pruning with satisfactory precision and recall. It also demonstrates good multiple neuron-splitting performance. As an effective tool for post-processing reconstruction, SNAP can facilitate neuron morphology analysis.

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

3D Soma Detection in Large-Scale Whole Brain Images via a Two-Stage Neural Network

3D soma detection in whole brain images is a critical step for neuron reconstruction. However, existing soma detection methods are not suitable for whole mouse brain images with large amounts of data and complex structure. In this paper, we propose a two-stage deep neural network to achieve fast and accurate soma detection in large-scale and high-resolution whole mouse brain images (more than 1TB). For the first stage, a lightweight Multi-level Cross Classification Network (MCC-Net) is proposed to filter out images without somas and generate coarse candidate images by combining the advantages of the multi convolution layer's feature extraction ability. It can speed up the detection of somas and reduce the computational complexity. For the second stage, to further obtain the accurate locations of somas in the whole mouse brain images, the Scale Fusion Segmentation Network (SFS-Net) is developed to segment soma regions from candidate images. Specifically, the SFS-Net captures multi-scale context information and establishes a complementary relationship between encoder and decoder by combining the encoder-decoder structure and a 3D Scale-Aware Pyramid Fusion (SAPF) module for better segmentation performance. The experimental results on three whole mouse brain images verify that the proposed method can achieve excellent performance and provide the reconstruction of neurons with beneficial information. Additionally, we have established a public dataset named WBMSD, including 798 high-resolution and representative images ( 256 ×256 ×256 voxels) from three whole mouse brain images, dedicated to the research of soma detection, which will be released along with this paper.

NRRS: a re-tracing strategy to refine neuron reconstruction

It is crucial to develop accurate and reliable algorithms for fine reconstruction of neural morphology from whole-brain image datasets. Even though the involvement of human experts in the reconstruction process can help to ensure the quality and accuracy of the reconstructions, automated refinement algorithms are necessary to handle substantial deviations problems of reconstructed branches and bifurcation points from the large-scale and high-dimensional nature of the image data. Our proposed Neuron Reconstruction Refinement Strategy (NRRS) is a novel approach to address the problem of deviation errors in neuron morphology reconstruction. Our method partitions the reconstruction into fixed-size segments and resolves the deviation problems by re-tracing in two steps. We also validate the performance of our method using a synthetic dataset. Our results show that NRRS outperforms existing solutions and can handle most deviation errors. We apply our method to SEU-ALLEN/BICCN dataset containing 1741 complete neuron reconstructions and achieve remarkable improvements in the accuracy of the neuron skeleton representation, the task of radius estimation and axonal bouton detection. Our findings demonstrate the critical role of NRRS in refining neuron morphology reconstruction.

Availability and implementation

The proposed refinement method is implemented as a Vaa3D plugin and the source code are available under the repository of vaa3d_tools/hackathon/Levy/refinement. The original fMOST images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org). The synthetic dataset is hosted on GitHub (https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/Levy/refinement).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Neuron tracing from light microscopy images: automation, deep learning and bench testing

MOTIVATION

Large-scale neuronal morphologies are essential to neuronal typing, connectivity characterization and brain modeling. It is widely accepted that automation is critical to the production of neuronal morphology. Despite previous survey papers about neuron tracing from light microscopy data in the last decade, thanks to the rapid development of the field, there is a need to update recent progress in a review focusing on new methods and remarkable applications.

RESULTS

This review outlines neuron tracing in various scenarios with the goal to help the community understand and navigate tools and resources. We describe the status, examples and accessibility of automatic neuron tracing. We survey recent advances of the increasingly popular deep-learning enhanced methods. We highlight the semi-automatic methods for single neuron tracing of mammalian whole brains as well as the resulting datasets, each containing thousands of full neuron morphologies. Finally, we exemplify the commonly used datasets and metrics for neuron tracing bench testing.

Using Simulated Training Data of Voxel-Level Generative Models to Improve 3D Neuron Reconstruction

Reconstructing neuron morphologies from fluorescence microscope images plays a critical role in neuroscience studies. It relies on image segmentation to produce initial masks either for further processing or final results to represent neuronal morphologies. This has been a challenging step due to the variation and complexity of noisy intensity patterns in neuron images acquired from microscopes. Whereas progresses in deep learning have brought the goal of accurate segmentation much closer to reality, creating training data for producing powerful neural networks is often laborious. To overcome the difficulty of obtaining a vast number of annotated data, we propose a novel strategy of using two-stage generative models to simulate training data with voxel-level labels. Trained upon unlabeled data by optimizing a novel objective function of preserving predefined labels, the models are able to synthesize realistic 3D images with underlying voxel labels. We showed that these synthetic images could train segmentation networks to obtain even better performance than manually labeled data. To demonstrate an immediate impact of our work, we further showed that segmentation results produced by networks trained upon synthetic data could be used to improve existing neuron reconstruction methods.

Practical guide for preparation, computational reconstruction and analysis of 3D human neuronal networks in control and ischaemic conditions

To obtain commensurate numerical data of neuronal network morphology in vitro, network analysis needs to follow consistent guidelines. Important factors in successful analysis are sample uniformity, suitability of the analysis method for extracting relevant data and the use of established metrics. However, for the analysis of 3D neuronal cultures, there is little coherence in the analysis methods and metrics used in different studies. Here, we present a framework for the analysis of neuronal networks in 3D. First, we selected a hydrogel that supported the growth of human pluripotent stem cell-derived cortical neurons. Second, we tested and compared two software programs for tracing multi-neuron images in three dimensions and optimized a workflow for neuronal analysis using software that was considered highly suitable for this purpose. Third, as a proof of concept, we exposed 3D neuronal networks to oxygen-glucose deprivation- and ionomycin-induced damage and showed morphological differences between the damaged networks and control samples utilizing the proposed analysis workflow. With the optimized workflow, we present a protocol for preparing, challenging, imaging and analysing 3D human neuronal cultures.

Summary: An optimized protocol is presented that allows morphological, quantifiable differences between the damaged and control human neuronal networks to be detected in three-dimensional cultures.

Super-resolution Segmentation Network for Reconstruction of Packed Neurites

Neuron reconstruction can provide the quantitative data required for measuring the neuronal morphology and is crucial in brain research. However, the difficulty in reconstructing dense neurites, wherein massive labor is required for accurate reconstruction in most cases, has not been well resolved. In this work, we provide a new pathway for solving this challenge by proposing the super-resolution segmentation network (SRSNet), which builds the mapping of the neurites in the original neuronal images and their segmentation in a higher-resolution (HR) space. During the segmentation process, the distances between the boundaries of the packed neurites are enlarged, and only the central parts of the neurites are segmented. Owing to this strategy, the super-resolution segmented images are produced for subsequent reconstruction. We carried out experiments on neuronal images with a voxel size of 0.2 μm × 0.2 μm × 1 μm produced by fMOST. SRSNet achieves an average F1 score of 0.88 for automatic packed neurites reconstruction, which takes both the precision and recall values into account, while the average F1 scores of other state-of-the-art automatic tracing methods are less than 0.70.

Tau target identification reveals NSF-dependent effects on AMPA receptor trafficking and memory formation

Microtubule-associated protein tau is a central factor in Alzheimer's disease and other tauopathies. However, the physiological functions of tau are unclear. Here, we used proximity-labelling proteomics to chart tau interactomes in primary neurons and mouse brains in vivo. Tau interactors map onto pathways of cytoskeletal, synaptic vesicle and postsynaptic receptor regulation and show significant enrichment for Parkinson's, Alzheimer's and prion disease. We find that tau interacts with and dose-dependently reduces the activity of N-ethylmaleimide sensitive fusion protein (NSF), a vesicular ATPase essential for AMPA-type glutamate receptor (AMPAR) trafficking. Tau-deficient (tau-/- ) neurons showed mislocalised expression of NSF and enhanced synaptic AMPAR surface levels, reversible through the expression of human tau or inhibition of NSF. Consequently, enhanced AMPAR-mediated associative and object recognition memory in tau-/- mice is suppressed by both hippocampal tau and infusion with an NSF-inhibiting peptide. Pathologic mutant tau from mouse models or Alzheimer's disease significantly enhances NSF inhibition. Our results map neuronal tau interactomes and delineate a functional link of tau with NSF in plasticity-associated AMPAR-trafficking and memory.

Geometrical modelling of neuronal clustering and development

The dynamic geometry of neuronal development is an essential concept in theoretical neuroscience. We aimed to design a mathematical model which outlines stepwise in an innovative form and designed to model neuronal development geometrically and modelling spatially the neuronal-electrical field interaction. We demonstrated flexibility in forming the cell and its nucleus to show neuronal growth from inside to outside that uses a fractal cylinder to generate neurons (pyramidal/sphere) in form of mathematically called ‘surface of revolution’. Furthermore, we verified the effect of the adjacent neurons on a free branch from one-side, by modelling a ‘normal vector surface’ that represented a group of neurons. Our model also indicated how the geometrical shapes and clustering of the neurons can be transformed mathematically in the form of vector field that is equivalent to the neuronal electromagnetic activity/electric flux. We further simulated neuronal-electrical field interaction that was implemented spatially using Van der Pol oscillator and taking Laplacian vector field as it reflects biophysical mechanism of neuronal activity and geometrical change. In brief, our study would be considered a proper platform and inspiring modelling for next more complicated geometrical and electrical constructions.

Zebrafish Larvae Rapidly Recover from Locomotor Effects and Neuromuscular Alterations Induced by Cholinergic Insecticides

Owing to the importance of acetylcholine as a neurotransmitter, many insecticides target the cholinergic system. Across phyla, cholinergic signaling is essential for many neuro-developmental processes including axonal pathfinding and synaptogenesis. Consequently, early-life exposure to such insecticides can disturb these processes, resulting in an impaired nervous system. One test frequently used to assess developmental neurotoxicity is the zebrafish light-dark transition test, which measures larval locomotion as a response to light changes. However, it is only poorly understood which structural alterations cause insecticide-induced locomotion defects and how persistent these alterations are. Therefore, this study aimed to link locomotion defects with effects on neuromuscular structures, including motorneurons, synapses, and muscles, and to investigate the longevity of the effects. The cholinergic insecticides diazinon and dimethoate (organophosphates), methomyl and pirimicarb (carbamates), and imidacloprid and thiacloprid (neonicotinoids) were used to induce hypoactivity. Our analyses revealed that some insecticides did not alter any of the structures assessed, while others affected axon branching (methomyl, imidacloprid) or muscle integrity (methomyl, thiacloprid). The majority of effects, even structural, were reversible within 24 to 72 h. Overall, we find that both neurodevelopmental and non-neurodevelopmental effects of different longevity can account for the reduced locomotion. These findings provide unprecedented insights into the underpinnings of insecticide-induced hypoactivity.

Deep-Learning-Based Automated Neuron Reconstruction From 3D Microscopy Images Using Synthetic Training Images

Digital reconstruction of neuronal structures from 3D microscopy images is critical for the quantitative investigation of brain circuits and functions. It is a challenging task that would greatly benefit from automatic neuron reconstruction methods. In this paper, we propose a novel method called SPE-DNR that combines spherical-patches extraction (SPE) and deep-learning for neuron reconstruction (DNR). Based on 2D Convolutional Neural Networks (CNNs) and the intensity distribution features extracted by SPE, it determines the tracing directions and classifies voxels into foreground or background. This way, starting from a set of seed points, it automatically traces the neurite centerlines and determines when to stop tracing. To avoid errors caused by imperfect manual reconstructions, we develop an image synthesizing scheme to generate synthetic training images with exact reconstructions. This scheme simulates 3D microscopy imaging conditions as well as structural defects, such as gaps and abrupt radii changes, to improve the visual realism of the synthetic images. To demonstrate the applicability and generalizability of SPE-DNR, we test it on 67 real 3D neuron microscopy images from three datasets. The experimental results show that the proposed SPE-DNR method is robust and competitive compared with other state-of-the-art neuron reconstruction methods.

A 3D Tubular Flux Model for Centerline Extraction in Neuron Volumetric Images

Digital morphology reconstruction from neuron volumetric images is essential for computational neuroscience. The centerline of the axonal and dendritic tree provides an effective shape representation and serves as a basis for further neuron reconstruction. However, it is still a challenge to directly extract the accurate centerline from the complex neuron structure with poor image quality. In this paper, we propose a neuron centerline extraction method based on a 3D tubular flux model via a two-stage CNN framework. In the first stage, a 3D CNN is used to learn the latent neuron structure features, namely flux features, from neuron images. In the second stage, a light-weight U-Net takes the learned flux features as input to extract the centerline with a spatial weighted average strategy to constrain the multi-voxel width response. Specifically, the labels of flux features in the first stage are generated by the 3D tubular model which calculates the geometric representations of the flux between each voxel in the tubular region and the nearest point on the centerline ground truth. Compared with self-learned features by networks, flux features, as a kind of prior knowledge, explicitly take advantage of the contextual distance and direction distribution information around the centerline, which is beneficial for the precise centerline extraction. Experiments on two challenging datasets demonstrate that the proposed method outperforms other state-of-the-art methods by 18% and 35.1% in F1-measurement and average distance scores at the most, and the extracted centerline is helpful to improve the neuron reconstruction performance.

Early Life Social Stress Causes Sex- and Region-Dependent Dopaminergic Changes that Are Prevented by Minocycline

Early life stress (ELS) is known to modify trajectories of brain dopaminergic development, but the mechanisms underlying have not been determined. ELS perturbs immune system and microglia reactivity, and inflammation and microglia influence dopaminergic transmission and development. Whether microglia mediate the effects of ELS on dopamine (DA) system development is still unknown. We explored the effects of repeated early social stress on development of the dopaminergic system in male and female mice through histological, electrophysiological, and transcriptomic analyses. Furthermore, we tested whether these effects could be mediated by ELS-induced altered microglia/immune activity through a pharmacological approach. We found that social stress in early life altered DA neurons morphology, reduced dopamine transporter (DAT) and tyrosine hydroxylase expression, and lowered DAT-mediated currents in the ventral tegmental area but not substantia nigra of male mice only. Notably, stress-induced DA alterations were prevented by minocycline, an inhibitor of microglia activation. Transcriptome analysis in the developing male ventral tegmental area revealed that ELS caused downregulation of dopaminergic transmission and alteration in hormonal and peptide signaling pathways. Results from this study offer new insight into the mechanisms of stress response and altered brain dopaminergic maturation after ELS, providing evidence of neuroimmune interaction, sex differences, and regional specificity.

Structure-Guided Segmentation for 3D Neuron Reconstruction

Digital reconstruction of neuronal morphologies in 3D microscopy images is critical in the field of neuroscience. However, most existing automatic tracing algorithms cannot obtain accurate neuron reconstruction when processing 3D neuron images contaminated by strong background noises or containing weak filament signals. In this paper, we present a 3D neuron segmentation network named Structure-Guided Segmentation Network (SGSNet) to enhance weak neuronal structures and remove background noises. The network contains a shared encoding path but utilizes two decoding paths called Main Segmentation Branch (MSB) and Structure-Detection Branch (SDB), respectively. MSB is trained on binary labels to acquire the 3D neuron image segmentation maps. However, the segmentation results in challenging datasets often contain structural errors, such as discontinued segments of the weak-signal neuronal structures and missing filaments due to low signal-to-noise ratio (SNR). Therefore, SDB is presented to detect the neuronal structures by regressing neuron distance transform maps. Furthermore, a Structure Attention Module (SAM) is designed to integrate the multi-scale feature maps of the two decoding paths, and provide contextual guidance of structural features from SDB to MSB to improve the final segmentation performance. In the experiments, we evaluate our model in two challenging 3D neuron image datasets, the BigNeuron dataset and the Extended Whole Mouse Brain Sub-image (EWMBS) dataset. When using different tracing methods on the segmented images produced by our method rather than other state-of-the-art segmentation methods, the distance scores gain 42.48% and 35.83% improvement in the BigNeuron dataset and 37.75% and 23.13% in the EWMBS dataset.

A report on digitised neuronal tracing method to study neurons in their entirety

Conventional camera lucida (CL) aided neuronal tracing technique for studying neural plasticity is a demanding procedure. Stereo Investigator-Neurolucida enabled neuronal tracing system is not accessible to all researchers. This necessitates alternate simple and less challenging digitised neuronal tracing methods. This report describes a novel digitised neuronal tracing method using widefield microscopy, and its effectiveness is compared with the traditional camera lucida aided neuronal tracing method. Golgi-Cox stained hippocampal cornu ammonis area-3 (CA3) pyramidal neuron photomicrographs were serially captured at a depth of every 2µm in the z-axis by a wide field microscope from the point of appearance to the disappearance. These images were stacked along the axis perpendicular to the image plane to reconstruct the neuron in its entirety, digitally traced and dendritic quantification was performed using open source software. The same neurons were manually traced using camera lucida, and Sholl analysis was done manually to quantify the dendritic arborisation pattern. The dendritic quantification data were not significantly different in both methods. Hence, the technology-enabled, less demanding, and equally accurate neuronal tracing can be adopted instead of manual tracing and analysis of neurons.

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A simple digitised neuronal tracing method is described.

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It is fast, rigorous, and comparable to traditional tracing techniques.

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Helps the researcher to repeatedly probe data to reduce errors.

DICOMization of Proprietary Files Obtained from Confocal, Whole-Slide, and FIB-SEM Microscope Scanners

The evolution of biomedical imaging technology is allowing the digitization of hundreds of glass slides at once. There are multiple microscope scanners available in the market including low-cost solutions that can serve small centers. Moreover, new technology is being researched to acquire images and new modalities are appearing in the market such as electron microscopy. This reality offers new diagnostics tools to clinical practice but emphasizes also the lack of multivendor system’s interoperability. Without the adoption of standard data formats and communications methods, it will be impossible to build this industry through the installation of vendor-neutral archives and the establishment of telepathology services in the cloud. The DICOM protocol is a feasible solution to the aforementioned problem because it already provides an interface for visible light and whole slide microscope imaging modalities. While some scanners currently have DICOM interfaces, the vast majority of manufacturers continue to use proprietary solutions. This article proposes an automated DICOMization pipeline that can efficiently transform distinct proprietary microscope images from CLSM, FIB-SEM, and WSI scanners into standard DICOM with their biological information maintained within their metadata. The system feasibility and performance were evaluated with fifteen distinct proprietary modalities, including stacked WSI samples. The results demonstrated that the proposed methodology is accurate and can be used in production. The normalized objects were stored through the standard communications in the Dicoogle open-source archive.

Multiple Molecular Pathways Are Influenced by Progranulin in a Neuronal Cell Model-A Parallel Omics Approach

Progranulin (PGRN) is critical in supporting a healthy CNS. Its haploinsufficiency results in frontotemporal dementia, while in experimental models of age-related neurodegenerative diseases, the targeted expression of PGRN greatly slows the onset of disease phenotypes. Nevertheless, much remains unclear about how PGRN affects its target cells. In previous studies we found that PGRN showed a remarkable ability to support the survival of NSC-34 motor neuron cells under conditions that would otherwise lead to their apoptosis. Here we used the same model to investigate other phenotypes of PGRN expression in NSC-34 cells. PGRN significantly influenced morphological differentiation, resulting in cells with enlarged cell bodies and extended projections. At a molecular level this correlated with pathways associated with the cytoskeleton and synaptic differentiation. Depletion of PGRN led to increased expression of several neurotrophic receptors, which may represent a homeostatic mechanism to compensate for loss of neurotrophic support from PGRN. The exception was RET, a neurotrophic tyrosine receptor kinase, which, when PGRN levels are high, shows increased expression and enhanced tyrosine phosphorylation. Other receptor tyrosine kinases also showed higher tyrosine phosphorylation when PGRN was elevated, suggesting a generalized enhancement of receptor activity. PGRN was found to bind to multiple plasma membrane proteins, including RET, as well as proteins in the ER/Golgi apparatus/lysosome pathway. Understanding how these various pathways contribute to PGRN action may provide routes toward improving neuroprotective therapies.

Image enhancement to leverage the 3D morphological reconstruction of single-cell neurons

MOTIVATION

To digitally reconstruct the 3D neuron morphologies has long been a major bottleneck in neuroscience. One of the obstacles to automate the procedure is the low signal-background contrast (SBC) and the large dynamic range of signal and background both within and across images.

RESULTS

We developed a pipeline to enhance the neurite signal and to suppress the background, with the goal of high SBC and better within- and between-image homogeneity. The performance of the image enhancement was quantitatively verified according to the different figures of merit benchmarking the image quality. In addition, the method could improve the neuron reconstruction in approximately 1/3 of the cases, with very few cases of degrading the reconstruction. This significantly outperformed three other approaches of image enhancement. Moreover, the compression rate was increased five times by average comparing the enhanced to the raw image. All results demonstrated the potential of the proposed method in leveraging the neuroscience by providing better 3D morphological reconstruction and lower cost of data storage and transfer.

AVAILABILITY AND IMPLEMENTATION

The study is conducted based on the Vaa3D platform and python 3.7.9. The Vaa3D platform is available on the GitHub (https://github.com/Vaa3D). The source code of the proposed image enhancement as a Vaa3D plugin, the source code to benchmark the image quality and the example image blocks are available under the repository of vaa3d_tools/hackathon/SGuo/imPreProcess. The original fMost images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling

With advances in microscopy and computer science, the technique of digitally reconstructing, modeling, and quantifying microscopic anatomies has become central to many fields of biological research. MBF Bioscience has chosen to openly document their digital reconstruction file format, the Neuromorphological File Specification, available at www.mbfbioscience.com/filespecification (Angstman et al., 2020). The format, created and maintained by MBF Bioscience, is broadly utilized by the neuroscience community. The data format's structure and capabilities have evolved since its inception, with modifications made to keep pace with advancements in microscopy and the scientific questions raised by worldwide experts in the field. More recent modifications to the neuromorphological file format ensure it abides by the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles promoted by the International Neuroinformatics Coordinating Facility (INCF; Wilkinson et al., Scientific Data, 3, 160018,, 2016). The incorporated metadata make it easy to identify and repurpose these data types for downstream applications and investigation. This publication describes key elements of the file format and details their relevant structural advantages in an effort to encourage the reuse of these rich data files for alternative analysis or reproduction of derived conclusions.

Classifying the tracing difficulty of 3D neuron image blocks based on deep learning

Quickly and accurately tracing neuronal morphologies in large-scale volumetric microscopy data is a very challenging task. Most automatic algorithms for tracing multi-neuron in a whole brain are designed under the Ultra-Tracer framework, which begins the tracing of a neuron from its soma and traces all signals via a block-by-block strategy. Some neuron image blocks are easy for tracing and their automatic reconstructions are very accurate, and some others are difficult and their automatic reconstructions are inaccurate or incomplete. The former are called low Tracing Difficulty Blocks (low-TDBs), while the latter are called high Tracing Difficulty Blocks (high-TDBs). We design a model named 3D-SSM to classify the tracing difficulty of 3D neuron image blocks, which is based on 3D Residual neural Network (3D-ResNet), Fully Connected Neural Network (FCNN) and Long Short-Term Memory network (LSTM). 3D-SSM contains three modules: Structure Feature Extraction (SFE), Sequence Information Extraction (SIE) and Model Fusion (MF). SFE utilizes a 3D-ResNet and a FCNN to extract two kinds of features in 3D image blocks and their corresponding automatic reconstruction blocks. SIE uses two LSTMs to learn sequence information hidden in 3D image blocks. MF adopts a concatenation operation and a FCNN to combine outputs from SIE. 3D-SSM can be used as a stop condition of an automatic tracing algorithm in the Ultra-Tracer framework. With its help, neuronal signals in low-TDBs can be traced by the automatic algorithm and in high-TDBs may be reconstructed by annotators. 12732 training samples and 5342 test samples are constructed on neuron images of a whole mouse brain. The 3D-SSM achieves classification accuracy rates 87.04% on the training set and 84.07% on the test set. Furthermore, the trained 3D-SSM is tested on samples from another whole mouse brain and its accuracy rate is 83.21%.

Endoglycan (PODXL2) is proteolytically processed by ADAM10 (a disintegrin and metalloprotease 10) and controls neurite branching in primary neurons

Cell adhesion is tightly controlled in multicellular organisms, for example, through proteolytic ectodomain shedding of the adhesion-mediating cell surface transmembrane proteins. In the brain, shedding of cell adhesion proteins is required for nervous system development and function, but the shedding of only a few adhesion proteins has been studied in detail in the mammalian brain. One such adhesion protein is the transmembrane protein endoglycan (PODXL2), which belongs to the CD34-family of highly glycosylated sialomucins. Here, we demonstrate that endoglycan is broadly expressed in the developing mouse brains and is proteolytically shed in vitro in mouse neurons and in vivo in mouse brains. Endoglycan shedding in primary neurons was mediated by the transmembrane protease a disintegrin and metalloprotease 10 (ADAM10), but not by its homolog ADAM17. Functionally, endoglycan deficiency reduced the branching of neurites extending from primary neurons in vitro, whereas deletion of ADAM10 had the opposite effect and increased neurite branching. Taken together, our study discovers a function for endoglycan in neurite branching, establishes endoglycan as an ADAM10 substrate and suggests that ADAM10 cleavage of endoglycan may contribute to neurite branching.

Comparing Automated Morphology Quantification Software on Dendrites of Uninjured and Injured Drosophila Neurons

Dendrites shape inputs and integration of depolarization that controls neuronal activity in the nervous system. Neuron pathologies can damage dendrite architecture and cause abnormalities in morphologies after injury. Dendrite regeneration can be quantified by various parameters, including total dendrite length and number of dendrite branches using manual or automated image analysis approaches. However, manual quantification is tedious and time consuming and automated approaches are often trained using wildtype neurons, making them poorly suited for analysis of genetically manipulated or injured dendrite arbors. In this study, we tested how well automated image analysis software performed on class IV Drosophila neurons, which have several hundred individual dendrite branches. We applied each software to automatically quantify features of uninjured neurons and neurons that regenerated new dendrites after injury. Regenerated arbors exhibit defects across multiple features of dendrite morphology, which makes them challenging for automated pipelines to analyze. We compared the performances of three automated pipelines against manual quantification using Simple Neurite Tracer in ImageJ: one that is commercially available (Imaris) and two developed by independent research groups (DeTerm and Tireless Tracing Genie). Out of the three software tested, we determined that Imaris is the most efficient at reconstructing dendrite architecture, but does not accurately measure total dendrite length even after intensive manual editing. Imaris outperforms both DeTerm and Tireless Tracing Genie for counting dendrite branches, and is better able to recreate previous conclusions from this same dataset. This thorough comparison of strengths and weaknesses of each software demonstrates their utility for analyzing regenerated neuron phenotypes in future studies.

GR24, A Synthetic Strigolactone Analog, and Light Affect the Organization of Cortical Microtubules in Arabidopsis Hypocotyl Cells

Strigolactones are plant hormones regulating cytoskeleton-mediated developmental events in roots, such as lateral root formation and elongation of root hairs and hypocotyls. The latter process was addressed herein by the exogenous application of a synthetic strigolactone, GR24, and an inhibitor of strigolactone biosynthesis, TIS108, on hypocotyls of wild-type Arabidopsis and a strigolactone signaling mutant max2-1 (more axillary growth 2-1). Owing to the interdependence between light and strigolactone signaling, the present work was extended to seedlings grown under a standard light/dark regime, or under continuous darkness. Given the essential role of the cortical microtubules in cell elongation, their organization and dynamics were characterized under the conditions of altered strigolactone signaling using fluorescence microscopy methods with different spatiotemporal capacities, such as confocal laser scanning microscopy (CLSM) and structured illumination microscopy (SIM). It was found that GR24-dependent inhibition of hypocotyl elongation correlated with changes in cortical microtubule organization and dynamics, observed in living wild-type and max2-1 seedlings stably expressing genetically encoded fluorescent molecular markers for microtubules. Quantitative assessment of microscopic datasets revealed that chemical and/or genetic manipulation of strigolactone signaling affected microtubule remodeling, especially under light conditions. The application of GR24 in dark conditions partially alleviated cytoskeletal rearrangement, suggesting a new mechanistic connection between cytoskeletal behavior and the light-dependence of strigolactone signaling.

Expansion microscopy: A powerful nanoscale imaging tool for neuroscientists

One of the biggest unsolved questions in neuroscience is how molecules and neuronal circuitry create behaviors, and how their misregulation or dysfunction results in neurological disease. Light microscopy is a vital tool for the study of neural molecules and circuits. However, the fundamental optical diffraction limit precludes the use of conventional light microscopy for sufficient characterization of critical signaling compartments and nanoscopic organizations of synapse-associated molecules. We have witnessed rapid development of super-resolution microscopy methods that circumvent the resolution limit by controlling the number of emitting molecules in specific imaging volumes and allow highly resolved imaging in the 10-100 nm range. Most recently, Expansion Microscopy (ExM) emerged as an alternative solution to overcome the diffraction limit by physically magnifying biological specimens, including nervous systems. Here, we discuss how ExM works in general and currently available ExM methods. We then review ExM imaging in a wide range of nervous systems, including Caenorhabditis elegans, Drosophila, zebrafish, mouse, and human, and their applications to synaptic imaging, neuronal tracing, and the study of neurological disease. Finally, we provide our prospects for expansion microscopy as a powerful nanoscale imaging tool in the neurosciences.

Microenvironmental innate immune signaling and cell mechanical responses promote tumor growth

Tissue homeostasis is achieved by balancing stem cell maintenance, cell proliferation and differentiation, as well as the purging of damaged cells. Elimination of unfit cells maintains tissue health; however, the underlying mechanisms driving competitive growth when homeostasis fails, for example, during tumorigenesis, remain largely unresolved. Here, using a Drosophila intestinal model, we find that tumor cells outcompete nearby enterocytes (ECs) by influencing cell adhesion and contractility. This process relies on activating the immune-responsive Relish/NF-κB pathway to induce EC delamination and requires a JNK-dependent transcriptional upregulation of the peptidoglycan recognition protein PGRP-LA. Consequently, in organisms with impaired PGRP-LA function, tumor growth is delayed and lifespan extended. Our study identifies a non-cell-autonomous role for a JNK/PGRP-LA/Relish signaling axis in mediating death of neighboring normal cells to facilitate tumor growth. We propose that intestinal tumors "hijack" innate immune signaling to eliminate enterocytes in order to support their own growth.

GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population

Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem. GTree offers an error-screening system for the fast localization of submicron errors in densely packed neurites and along with long projections across the whole brain, thus achieving reconstruction close to the ground truth. Moreover, GTree integrates a series of our previous algorithms to significantly reduce manual interference and achieve high-level automation. When applied to an entire mouse brain dataset, GTree is shown to be five times faster than widely used commercial software. Finally, using GTree, we demonstrate the reconstruction of 35 long-projection neurons around one injection site of a mouse brain. GTree is also applicable to large datasets (10 TB or higher) from various light microscopes.

Efficient 3D Junction Detection in Biomedical Images Based on a Circular Sampling Model and Reverse Mapping

Detection and localization of terminations and junctions is a key step in the morphological reconstruction of tree-like structures in images. Previously, a ray-shooting model was proposed to detect termination points automatically. In this paper, we propose an automatic method for 3D junction points detection in biomedical images, relying on a circular sampling model and a 2D-to-3D reverse mapping approach. First, the existing ray-shooting model is improved to a circular sampling model to extract the pixel intensity distribution feature across the potential branches around the point of interest. The computation cost can be reduced dramatically compared to the existing ray-shooting model. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed to detect 2D junction points in maximum intensity projections (MIPs) of sub-volume images in a given 3D image, by determining the number of branches in the candidate junction region. Further, a 2D-to-3D reverse mapping approach is used to map these detected 2D junction points in MIPs to the 3D junction points in the original 3D images. The proposed 3D junction point detection method is implemented as a build-in tool in the Vaa3D platform. Experiments on multiple 2D images and 3D images show average precision and recall rates of 87.11% and 88.33% respectively. In addition, the proposed algorithm is dozens of times faster than the existing deep-learning based model. The proposed method has excellent performance in both detection precision and computation efficiency for junction detection even in large-scale biomedical images.

Can in vivo confocal microscopy differentiate between sub-types of dry eye disease? A review

Many studies utilised in vivo confocal microscopy (IVCM) to associate variations in corneal structures with dry eye disease (DED). However, DED is an umbrella term that covers various aetiologies and presentations. This review analyses populations by DED aetiology to determine the relationships between IVCM parameters and specific DED sub-types. It focuses on the most commonly examined structures, sub-basal nerves and dendritic cells. Across the literature, most studies found individuals with immune-mediated DED had lower sub-basal nerve fibre number and density than controls, with smaller differences between non-immune DED and controls. However, wide ranges of values reported across studies demonstrate considerable overlap between DED sub-types and controls, rendering these metrics less helpful when diagnosing an individual patient. Dendritic cell density was considerably higher in individuals with immune-mediated DED than in non-immune DED or controls. As such, dendritic cell density may be a better indicator of DED associated with a systemic immune-mediated process.

The Key Regulator of Necroptosis, RIP1 Kinase, Contributes to the Formation of Astrogliosis and Glial Scar in Ischemic Stroke

Necroptosis initiation relies on the receptor-interacting protein 1 kinase (RIP1K). We recently reported that genetic and pharmacological inhibition of RIP1K produces protection against ischemic stroke-induced astrocytic injury. However, the role of RIP1K in ischemic stroke-induced formation of astrogliosis and glial scar remains unknown. Here, in a transient middle cerebral artery occlusion (tMCAO) rat model and an oxygen and glucose deprivation and reoxygenation (OGD/Re)-induced astrocytic injury model, we show that RIP1K was significantly elevated in the reactive astrocytes. Knockdown of RIP1K or delayed administration of RIP1K inhibitor Nec-1 down-regulated the glial scar markers, improved ischemic stroke-induced necrotic morphology and neurologic deficits, and reduced the volume of brain atrophy. Moreover, knockdown of RIP1K attenuated astrocytic cell death and proliferation and promoted neuronal axonal generation in a neuron and astrocyte co-culture system. Both vascular endothelial growth factor D (VEGF-D) and its receptor VEGFR-3 were elevated in the reactive astrocytes; simultaneously, VEGF-D was increased in the medium of astrocytes exposed to OGD/Re. Knockdown of RIP1K down-regulated VEGF-D gene and protein levels in the reactive astrocytes. Treatment with 400 ng/ml recombinant VEGF-D induced the formation of glial scar; conversely, the inhibitor of VEGFR-3 suppressed OGD/Re-induced glial scar formation. RIP3K and MLKL may be involved in glial scar formation. Taken together, these results suggest that RIP1K participates in the formation of astrogliosis and glial scar via impairment of normal astrocyte responses and enhancing the astrocytic VEGF-D/VEGFR-3 signaling pathways. Inhibition of RIP1K promotes the brain functional recovery partially via suppressing the formation of astrogliosis and glial scar.

Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images

Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of good seed points. 3D neuron critical points, including terminations, branch points and cross-over points, are good candidates for such seed points. However, a method that can simultaneously detect all types of critical points has barely been explored. In this work, we present a method to simultaneously detect all 3 types of 3D critical points in neuron microscopy images, based on a spherical-patches extraction (SPE) method and a 2D multi-stream convolutional neural network (CNN). SPE uses a set of concentric spherical surfaces centered at a given critical point candidate to extract intensity distribution features around the point. Then, a group of 2D spherical patches is generated by projecting the surfaces into 2D rectangular image patches according to the orders of the azimuth and the polar angles. Finally, a 2D multi-stream CNN, in which each stream receives one spherical patch as input, is designed to learn the intensity distribution features from those spherical patches and classify the given critical point candidate into one of four classes: termination, branch point, cross-over point or non-critical point. Experimental results confirm that the proposed method outperforms other state-of-the-art critical points detection methods. The critical points based neuron reconstruction results demonstrate the potential of the detected neuron critical points to be good seed points for neuron reconstruction. Additionally, we have established a public dataset dedicated for neuron critical points detection, which has been released along with this article.

Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements

Researchers in the field of connectomics are working to reconstruct a map of neural connections in the brain in order to understand at a fundamental level how the brain processes information. Constructing this wiring diagram is done by tracing neurons through high-resolution image stacks acquired with fluorescence microscopy imaging techniques. While a large number of automatic tracing algorithms have been proposed, these frequently rely on local features in the data and fail on noisy data or ambiguous cases, requiring time-consuming manual correction. As a result, manual and semi-automatic tracing methods remain the state-of-the-art for creating accurate neuron reconstructions. We propose a new semi-automatic method that uses topological features to guide users in tracing neurons and integrate this method within a virtual reality (VR) framework previously used for manual tracing. Our approach augments both visualization and interaction with topological elements, allowing rapid understanding and tracing of complex morphologies. In our pilot study, neuroscientists demonstrated a strong preference for using our tool over prior approaches, reported less fatigue during tracing, and commended the ability to better understand possible paths and alternatives. Quantitative evaluation of the traces reveals that users' tracing speed increased, while retaining similar accuracy compared to a fully manual approach.

Accurate Neuronal Soma Segmentation Using 3D Multi-Task Learning U-Shaped Fully Convolutional Neural Networks

Neuronal soma segmentation is a crucial step for the quantitative analysis of neuronal morphology. Automated neuronal soma segmentation methods have opened up the opportunity to improve the time-consuming manual labeling required during the neuronal soma morphology reconstruction for large-scale images. However, the presence of touching neuronal somata and variable soma shapes in images brings challenges for automated algorithms. This study proposes a neuronal soma segmentation method combining 3D U-shaped fully convolutional neural networks with multi-task learning. Compared to existing methods, this technique applies multi-task learning to predict the soma boundary to split touching somata, and adopts U-shaped architecture convolutional neural network which is effective for a limited dataset. The contour-aware multi-task learning framework is applied to the proposed method to predict the masks of neuronal somata and boundaries simultaneously. In addition, a spatial attention module is embedded into the multi-task model to improve neuronal soma segmentation results. The Nissl-stained dataset captured by the micro-optical sectioning tomography system is used to validate the proposed method. Following comparison to four existing segmentation models, the proposed method outperforms the others notably in both localization and segmentation. The novel method has potential for high-throughput neuronal soma segmentation in large-scale optical imaging data for neuron morphology quantitative analysis.

3D Neuron Microscopy Image Segmentation via the Ray-Shooting Model and a DC-BLSTM Network

The morphology reconstruction (tracing) of neurons in 3D microscopy images is important to neuroscience research. However, this task remains very challenging because of the low signal-to-noise ratio (SNR) and the discontinued segments of neurite patterns in the images. In this paper, we present a neuronal structure segmentation method based on the ray-shooting model and the Long Short-Term Memory (LSTM)-based network to enhance the weak-signal neuronal structures and remove background noise in 3D neuron microscopy images. Specifically, the ray-shooting model is used to extract the intensity distribution features within a local region of the image. And we design a neural network based on the dual channel bidirectional LSTM (DC-BLSTM) to detect the foreground voxels according to the voxel-intensity features and boundary-response features extracted by multiple ray-shooting models that are generated in the whole image. This way, we transform the 3D image segmentation task into multiple 1D ray/sequence segmentation tasks, which makes it much easier to label the training samples than many existing Convolutional Neural Network (CNN) based 3D neuron image segmentation methods. In the experiments, we evaluate the performance of our method on the challenging 3D neuron images from two datasets, the BigNeuron dataset and the Whole Mouse Brain Sub-image (WMBS) dataset. Compared with the neuron tracing results on the segmented images produced by other state-of-the-art neuron segmentation methods, our method improves the distance scores by about 32% and 27% in the BigNeuron dataset, and about 38% and 27% in the WMBS dataset.

A simple method defines 3D morphology and axon projections of filled neurons in a small CNS volume: Steps toward understanding functional network circuitry

BACKGROUND

Fundamental to understanding neuronal network function is defining neuron morphology, location, properties, and synaptic connectivity in the nervous system. A significant challenge is to reconstruct individual neuron morphology and connections at a whole CNS scale and bring together functional and anatomical data to understand the whole network.

NEW METHOD

We used a PC controlled micropositioner to hold a fixed whole mount of Xenopus tadpole CNS and replace the stage on a standard microscope. This allowed direct recording in 3D coordinates of features and axon projections of one or two neurons dye-filled during whole-cell recording to study synaptic connections. Neuron reconstructions were normalised relative to the ventral longitudinal axis of the nervous system. Coordinate data were stored as simple text files.

RESULTS

Reconstructions were at 1 μm resolution, capturing axon lengths in mm. The output files were converted to SWC format and visualised in 3D reconstruction software NeuRomantic. Coordinate data are tractable, allowing correction for histological artefacts. Through normalisation across multiple specimens we could infer features of network connectivity of mapped neurons of different types.

COMPARISON WITH EXISTING METHODS

Unlike other methods using fluorescent markers and utilising large-scale imaging, our method allows direct acquisition of 3D data on neurons whose properties and synaptic connections have been studied using whole-cell recording.

CONCLUSIONS

This method can be used to reconstruct neuron 3D morphology and follow axon projections in the CNS. After normalisation to a common CNS framework, inferences on network connectivity at a whole nervous system scale contribute to network modelling to understand CNS function.

Retrieving similar substructures on 3D neuron reconstructions

Since manual tracing is time consuming and the performance of automatic tracing is unstable, it is still a challenging task to generate accurate neuron reconstruction efficiently and effectively. One strategy is generating a reconstruction automatically and then amending its inaccurate parts manually. Aiming at finding inaccurate substructures efficiently, we propose a pipeline to retrieve similar substructures on one or more neuron reconstructions, which are very similar to a marked problematic substructure. The pipeline consists of four steps: getting a marked substructure, constructing a query substructure, generating candidate substructures and retrieving most similar substructures. The retrieval procedure was tested on 163 gold standard reconstructions provided by the BigNeuron project and a reconstruction of a mouse’s large neuron. Experimental results showed that the implementation of the proposed methods is very efficient and all retrieved substructures are very similar to the marked one in numbers of nodes and branches, and degree of curvature.

The HMGB1/RAGE axis induces bone pain associated with colonization of 4T1 mouse breast cancer in bone

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The 4T1 mouse breast cancer injected in tibiae induced bone pain.

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The 4T1 breast cancer secreted high mobility group box 1 (HMGB1) that promotes axogenesis of sensory neurons.

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Bone pain was reduced by HMGB1 antibody and an antagonist for the receptor for advanced glycation end products.

Bone pain is a common complication of breast cancer (BC) bone metastasis and is a major cause of increased morbidity and mortality. Although the mechanism of BC-associated bone pain (BCABP) remains poorly understood, involvement of BC products in the pathophysiology of BCABP has been proposed. Aggressive cancers secrete damage-associated molecular patterns (DAMPs) that bind to specific DAMP receptors and modulate cancer microenvironment. A prototypic DAMP, high mobility group box 1 (HMGB1), which acts as a ligand for the receptor for advanced glycation end products (RAGE) and toll-like receptors (TLRs), is increased in its expression in BC patients with poor outcomes. Here we show that 4T1 mouse BC cells colonizing bone up-regulate the expression of molecular pain markers, phosphorylated ERK1/2 (pERK) and pCREB, in the dorsal root ganglia (DRGs) innervating bone and induced BCABP as evaluated by hind-paw mechanical hypersensitivity. Importantly, silencing HMGB1 in 4T1 BC cells by shRNA reduced pERK and pCREB and BCABP with decreased HMGB1 levels in bone. Further, administration of a neutralizing antibody to HMGB1 or an antagonist for RAGE, FPS-ZM1, ameliorated pERK, pCREB and BCABP, while a TLR4 antagonist, TAK242, showed no effects. Consistent with these in vivo results, co-cultures of F11 sensory neuron-like cells with 4T1 BC cells in microfluidic culture platforms increased neurite outgrowth of F11 cells, which was blocked by HMGB1 antibody. Our results show that HMGB1 secreted by BC cells induces BCABP via binding to RAGE of sensory neurons and suggest that the HMGB1/RAGE axis may be a potential novel therapeutic target for BCABP.

Development of an N-Cadherin Biofunctionalized Hydrogel to Support the Formation of Synaptically Connected Neural Networks

In vitro models of the human central nervous system (CNS), particularly those derived from induced pluripotent stem cells (iPSCs), are becoming increasingly recognized as useful complements to animal models for studying neurological diseases and developing therapeutic strategies. However, many current three-dimensional (3D) CNS models suffer from deficits that limit their research utility. In this work, we focused on improving the interactions between the extracellular matrix (ECM) and iPSC-derived neurons to support model development. The most common ECMs used to fabricate 3D CNS models often lack the necessary bioinstructive cues to drive iPSC-derived neurons to a mature and synaptically connected state. These ECMs are also typically difficult to pattern into complex structures due to their mechanical properties. To address these issues, we functionalized gelatin methacrylate (GelMA) with an N-cadherin (Cad) extracellular peptide epitope to create a biomaterial termed GelMA-Cad. After photopolymerization, GelMA-Cad forms soft hydrogels (on the order of 2 kPa) that can maintain patterned architectures. The N-cadherin functionality promotes survival and maturation of single-cell suspensions of iPSC-derived glutamatergic neurons into synaptically connected networks as determined by viral tracing and electrophysiology. Immunostaining reveals a pronounced increase in presynaptic and postsynaptic marker expression in GelMA-Cad relative to Matrigel, as well as extensive colocalization of these markers, thus highlighting the biological activity of the N-cadherin peptide. Overall, given its ability to enhance iPSC-derived neuron maturity and connectivity, GelMA-Cad should be broadly useful for in vitro studies of neural circuitry in health and disease.

Skeleton optimization of neuronal morphology based on three-dimensional shape restrictions

Background

Neurons are the basic structural unit of the brain, and their morphology is a key determinant of their classification. The morphology of a neuronal circuit is a fundamental component in neuron modeling. Recently, single-neuron morphologies of the whole brain have been used in many studies. The correctness and completeness of semimanually traced neuronal morphology are credible. However, there are some inaccuracies in semimanual tracing results. The distance between consecutive nodes marked by humans is very long, spanning multiple voxels. On the other hand, the nodes are marked around the centerline of the neuronal fiber, not on the centerline. Although these inaccuracies do not seriously affect the projection patterns that these studies focus on, they reduce the accuracy of the traced neuronal skeletons. These small inaccuracies will introduce deviations into subsequent studies that are based on neuronal morphology files.

Results

We propose a neuronal digital skeleton optimization method to evaluate and make fine adjustments to a digital skeleton after neuron tracing. Provided that the neuronal fiber shape is smooth and continuous, we describe its physical properties according to two shape restrictions. One restriction is designed based on the grayscale image, and the other is designed based on geometry. These two restrictions are designed to finely adjust the digital skeleton points to the neuronal fiber centerline. With this method, we design the three-dimensional shape restriction workflow of neuronal skeleton adjustment computation. The performance of the proposed method has been quantitatively evaluated using synthetic and real neuronal image data. The results show that our method can reduce the difference between the traced neuronal skeleton and the centerline of the neuronal fiber. Furthermore, morphology metrics such as the neuronal fiber length and radius become more precise.

Conclusions

This method can improve the accuracy of a neuronal digital skeleton based on traced results. The greater the accuracy of the digital skeletons that are acquired, the more precise the neuronal morphologies that are analyzed will be.

DW14006 as a Direct AMPKα Activator Ameliorates Diabetic Peripheral Neuropathy in Mice

Diabetic peripheral neuropathy (DPN) is a long-term complication of diabetes with a complicated pathogenesis. AMP-activated protein kinase (AMPK) senses oxidative stress, and mitochondrial function plays a central role in the regulation of DPN. Here, we reported that DW14006 (2-[3-(7-chloro-6-[2'-hydroxy-(1,1'-biphenyl)-4-yl]-2-oxo-1,2-dihydroquinolin-3-yl)phenyl]acetic acid) as a direct AMPKα activator efficiently ameliorated DPN in both streptozotocin (STZ)-induced type 1 and BKS db/db type 2 diabetic mice. DW14006 administration highly enhanced neurite outgrowth of dorsal root ganglion neurons and improved neurological function in diabetic mice. The underlying mechanisms have been intensively investigated. DW14006 treatment improved mitochondrial bioenergetics profiles and restrained oxidative stress and inflammation in diabetic mice by targeting AMPKα, which has been verified by assay against the STZ-induced diabetic mice injected with adeno-associated virus 8-AMPKα-RNAi. To our knowledge, our work might be the first report on the amelioration of the direct AMPKα activator on DPN by counteracting multiple risk factors including mitochondrial dysfunction, oxidative stress, and inflammation, and DW14006 has been highlighted as a potential leading compound in the treatment of DPN.

A bird's-eye view of deep learning in bioimage analysis

Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

AutoNeuriteJ: An ImageJ plugin for measurement and classification of neuritic extensions

Morphometry characterization is an important procedure in describing neuronal cultures and identifying phenotypic differences. This task usually requires labor-intensive measurements and the classification of numerous neurites from large numbers of neurons in culture. To automate these measurements, we wrote AutoNeuriteJ, an imageJ/Fiji plugin that measures and classifies neurites from a very large number of neurons. We showed that AutoNeuriteJ is able to detect variations of neuritic growth induced by several compounds known to affect the neuronal growth. In these experiments measurement of more than 5000 mouse neurons per conditions was obtained within a few hours. Moreover, by analyzing mouse neurons deficient for the microtubule associated protein 6 (MAP6) and wild type neurons we illustrate that AutoNeuriteJ is capable to detect subtle phenotypic difference in axonal length. Overall the use of AutoNeuriteJ will provide rapid, unbiased and accurate measurement of neuron morphologies.