Neuron tracing in perspective

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.

New morphological features based on the Sholl analysis for automatic classification of traced neurons

BACKGROUND

This article addresses the automatic classification of reconstructed neurons through their morphological features. The purpose was to extend the capabilities of the L-Measure software.

METHODS

New morphological features were developed, based on modifications of the conventional Sholl analysis. The lengths of the compartments, as well as their volumes, were added to the features used in the classical analysis in order to improve the results during automatic neuron classification. FSM were used to obtain subsets of lower cardinality from the full feature sets and the usefulness of these subsets was tested through their use in supervised classification tasks. The study was based on two types of neurons belonging to mice: pyramidal and GABAergic interneurons. Furthermore, a set of pyramidal neurons belonging to Later 4 and Layer 5 was analyzed.

RESULTS

RF classifier shown the best performance combined with a Wrapper method.U-WNAD set allowed to obtain higher values than WN, A and D in all cases and better results than LM for the filters and wrappers FSM. U-LM-WNAD set, led to the highest AUC values for all the FSM studied. Similar results for different regions of cortex were obtained. Comparison with Existing Methods The new features exhibited high discriminatory power with which the values of AUC and Acc obtained in the experiments exceeded those obtained using only the features provided by L-Measure.

CONCLUSIONS

The highest values of AUC and Acc were obtained from the sets U-WNAD and U-LM-WNAD, evidencing the discriminatory power of the new proposed features.

Homogeneity and heterogeneity of biological characteristics in mesenchymal stem cells from human umbilical cords and exfoliated deciduous teeth

Mesenchymal stem cells (MSCs) have proven powerful potential for cell-based therapy both in regenerative medicine and disease treatment. Human umbilical cords and exfoliated deciduous teeth are the main sources of MSCs with no donor injury or ethical issues. The goal of this study was to investigate the differences in the biological characteristics of human umbilical cord mesenchymal stem cells (UCMSCs) and stem cells from human exfoliated deciduous teeth (SHEDs). UCMSCs and SHEDs were identified by flow cytometry. The proliferation, differentiation, migration, chemotaxis, paracrine, immunomodulatory, neurite growth-promoting capabilities, and acetaldehyde dehydrogenase (ALDH) activity were comparatively studied between these two MSCs in vitro. The results showed that both SHEDs and UCMSCs expressed cell surface markers characteristic of MSCs. Furthermore, SHEDs exhibited better capacity for proliferation, migration, promotion of neurite growth, and chondrogenic differentiation. Meanwhile, UCMSCs showed more outstanding adipogenic differentiation and chemotaxy. Additionally, there were no significant differences in osteogenic differentiation, immunomodulatory capacity, and the proportion of ALDHBright compartment. Our findings indicate that although both UCMSCs and SHEDs are mesenchymal stem cells and presented some similar biological characteristics, they also have differences in many aspects, which might be helpful for developing future clinical cellular therapies.

Measurements of neuronal morphological variation across the rat neocortex

Neuron morphology is highly variable across the mammalian brain. It is thought that these attributes of neuronal cell shape, such as soma surface area and branching frequency, are determined by biological function and information processing. In this study, a large data set of neurons across the rat neocortex were clustered by their anatomical characters for evidence of distinctiveness among neocortical regions and the somatosensory layers. This data set of neuronal morphologies was compiled from 31 different lab sources with a validation procedure so that data records are potentially comparable across research studies. With this large set of heterogeneous data and by clustering analysis, this study shows that neuronal morphological traits overlap among neocortical and somatosensory regions. In the context of past neuroanatomical studies, this result is not congruent with tissue level analysis and strongly suggests further sampling of neuronal data to lessen the effect of confounding factors, such as the influence of different methodologies from use of heterogeneous samples of neuronal data.

Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation

Microscopic images of neuronal cells provide essential structural information about the key constituents of the brain and form the basis of many neuroscientific studies. Computational analyses of the morphological properties of the captured neurons require first converting the structural information into digital tree-like reconstructions. Many dedicated computational methods and corresponding software tools have been and are continuously being developed with the aim to automate this step while achieving human-comparable reconstruction accuracy. This pursuit is hampered by the immense diversity and intricacy of neuronal morphologies as well as the often low quality and ambiguity of the images. Here we present a novel method we developed in an effort to improve the robustness of digital reconstruction against these complicating factors. The method is based on probabilistic filtering by sequential Monte Carlo estimation and uses prediction and update models designed specifically for tracing neuronal branches in microscopic image stacks. Moreover, it uses multiple probabilistic traces to arrive at a more robust, ensemble reconstruction. The proposed method was evaluated on fluorescence microscopy image stacks of single neurons and dense neuronal networks with expert manual annotations serving as the gold standard, as well as on synthetic images with known ground truth. The results indicate that our method performs well under varying experimental conditions and compares favorably to state-of-the-art alternative methods.

Activation of 5-HT1A postsynaptic receptors by NLX-101 results in functional recovery and an increase in neuroplasticity in mice with brain ischemia

Pharmacological interventions that selectively activate serotonin 5-hydroxytryptramine-1A (5-HT_1A) heteroreceptors may prevent or attenuate the consequences of brain ischemic episodes. The present study investigated whether the preferential 5-HT_1A postsynaptic receptor agonist NLX-101 (a.k.a. F15599) mitigates cognitive and emotional impairments and affects neuroplasticity in mice that are subjected to the bilateral common carotid artery occlusion (BCCAO) model of brain ischemia. The selective serotonin reuptake inhibitor escitalopram (Esc) was used for comparative purposes because it is able to decrease morbidity and improve recovery in stroke patients and ischemic rodents. Sham and BCCAO mice received daily doses of NLX-101 (0.32 mg/kg, i.p) or Esc (20 mg/kg, i.p) for 28 days. During this period, they were evaluated for locomotor activity, anxiety- and despair-related behaviors and hippocampus-dependent cognitive function, using the open field, elevated zero maze, forced swim test and object location test, respectivelly. The mice's brains were processed for biochemical and histological analyses. BCCAO mice exhibited high anxiety and despair-like behaviors and performed worse than controls in the cognitive assessment. BCCAO induced neuronal and dendritic spine loss and decreases in the protein levels of neuronal plasticity markers, including brain-derived neurotrophic factor (BDNF), synaptophysin (SYN), and postsynaptic density protein-95 (PSD-95), in prefrontal cortex (PFC) and hippocampus. NLX-101 and Esc attenuated cognitive impairments and despair-like behaviors in BCCAO mice. Only Esc decreased anxiety-like behaviors due to brain ischemia. Both NLX-101 and Esc blocked the increase in plasma corticosterone levels and, restored BDNF, SYN and PSD-95 protein levels in the hippocampus. Moreover, both compounds impacted positively dentritic remodeling in the hippocampus and PFC of ischemic mice. In the PFC, NLX-101 increased the BDNF protein levels, while Esc in turn, attenuated the decrease in the PSD-95 protein levels induced by BCCAO. The present results suggest that activation of post-synaptic 5-HT_1A receptors is the molecular mechanism for serotonergic protective effects in BCCAO. Moreover, post-synaptic biased agonists such as NLX-101 might constitute promising therapeutics for treatment of functional and neurodegenerative outcomes of brain ischemia.

Maturation of newborn neurons predicts social memory persistence in mice

Memory transience is essential to gain cognitive flexibility. Recently, hippocampal neurogenesis is emerging as one of the mechanisms involved in the balance between persistence and forgetting. Social recognition memory (SRM) has its duration prolonged by neurogenesis. However, it is still to be determined whether boosting neurogenesis in distinct phases of SRM may favor forgetting over persistence. In the present study, we used enriched environment (EE) and memantine (MEM) to increase neurogenesis. SRM was ubiquitously prolonged by both, while EE after the memory acquisition did not favor forgetting. Interestingly, the proportion of newborn neurons with mature morphology in the dorsal hippocampus was higher in animals where persistence prevailed. Finally, one of the main factors for dendritic growth is the formation of cytoskeleton. We found that Latrunculin A, an inhibitor of actin polymerization, blunted the promnesic effect of EE. Altogether, our results indicate that the mechanisms triggered by EE to improve SRM are not limited to increasing the number of newborn neurons.

DeepBranch: Deep Neural Networks for Branch Point Detection in Biomedical Images

Morphology reconstruction of tree-like structures in volumetric images, such as neurons, retinal blood vessels, and bronchi, is of fundamental interest for biomedical research. 3D branch points play an important role in many reconstruction applications, especially for graph-based or seed-based reconstruction methods and can help to visualize the morphology structures. There are a few hand-crafted models proposed to detect the branch points. However, they are highly dependent on the empirical setting of the parameters for different images. In this paper, we propose a DeepBranch model for branch point detection with two-level designed convolutional networks, a candidate region segmenter and a false positive reducer. On the first level, an improved 3D U-Net model with anisotropic convolution kernels is employed to detect initial candidates. Compared with the traditional sliding window strategy, the improved 3D U-Net can avoid massive redundant computations and dramatically speed up the detection process by employing dense-inference with fully convolutional neural networks (FCN). On the second level, a method based on multi-scale multi-view convolutional neural networks (MSMV-Net) is proposed for false positive reduction by feeding multi-scale views of 3D volumes into multiple streams of 2D convolution neural networks (CNNs), which can take full advantage of spatial contextual information as well as fit different sizes. Experiments on multiple 3D biomedical images of neurons, retinal blood vessels and bronchi confirm that the proposed 3D branch point detection method outperforms other state-of-the-art detection methods, and is helpful for graph-based or seed-based reconstruction methods.

Image-derived models of cell organization changes during differentiation and drug treatments

PC12 cells are a popular model system to study changes driving and accompanying neuronal differentiation. While attention has been paid to changes in transcriptional regulation and protein signaling, much less is known about the changes in organization that accompany PC12 differentiation. Fluorescence microscopy can provide extensive information about these changes, although it is difficult to continuously observe changes over many days of differentiation. We describe a generative model of differentiation-associated changes in cell and nuclear shape and their relationship to mitochondrial distribution constructed from images of different cells at discrete time points. We show that the model accurately represents complex cell and nuclear shapes and learn a regression model that relates cell and nuclear shape to mitochondrial distribution; the predictive accuracy of the model increases during differentiation. Most importantly, we propose a method, based on cell matching and interpolation, to produce realistic simulations of the dynamics of cell differentiation from only static images. We also found that the distribution of cell shapes is hollow: most shapes are very different from the average shape. Finally, we show how the method can be used to model nuclear shape changes of human-induced pluripotent stem cells resulting from drug treatments.

A Smart Region-Growing Algorithm for Single-Neuron Segmentation From Confocal and 2-Photon Datasets

Accurately digitizing the brain at the micro-scale is crucial for investigating brain structure-function relationships and documenting morphological alterations due to neuropathies. Here we present a new Smart Region Growing algorithm (SmRG) for the segmentation of single neurons in their intricate 3D arrangement within the brain. Its Region Growing procedure is based on a homogeneity predicate determined by describing the pixel intensity statistics of confocal acquisitions with a mixture model, enabling an accurate reconstruction of complex 3D cellular structures from high-resolution images of neural tissue. The algorithm's outcome is a 3D matrix of logical values identifying the voxels belonging to the segmented structure, thus providing additional useful volumetric information on neurons. To highlight the algorithm's full potential, we compared its performance in terms of accuracy, reproducibility, precision and robustness of 3D neuron reconstructions based on microscopic data from different brain locations and imaging protocols against both manual and state-of-the-art reconstruction tools.

Whole-Neuron Synaptic Mapping Reveals Spatially Precise Excitatory/Inhibitory Balance Limiting Dendritic and Somatic Spiking

The balance between excitatory and inhibitory (E and I) synapses is thought to be critical for information processing in neural circuits. However, little is known about the spatial principles of E and I synaptic organization across the entire dendritic tree of mammalian neurons. We developed a new open-source reconstruction platform for mapping the size and spatial distribution of E and I synapses received by individual genetically-labeled layer 2/3 (L2/3) cortical pyramidal neurons (PNs) in vivo. We mapped over 90,000 E and I synapses across twelve L2/3 PNs and uncovered structured organization of E and I synapses across dendritic domains as well as within individual dendritic segments. Despite significant domain-specific variation in the absolute density of E and I synapses, their ratio is strikingly balanced locally across dendritic segments. Computational modeling indicates that this spatially precise E/I balance dampens dendritic voltage fluctuations and strongly impacts neuronal firing output.

NeuroPath2Path: Classification and elastic morphing between neuronal arbors using path-wise similarity

Neuron shape and connectivity affect function. Modern imaging methods have proven successful at extracting morphological information. One potential path to achieve analysis of this morphology is through graph theory. Encoding by graphs enables the use of high throughput informatic methods to extract and infer brain function. However, the application of graph-theoretic methods to neuronal morphology comes with certain challenges in term of complex subgraph matching and the difficulty in computing intermediate shapes in between two imaged temporal samples. Here we report a novel, efficacious graph-theoretic method that rises to the challenges. The morphology of a neuron, which consists of its overall size, global shape, local branch patterns, and cell-specific biophysical properties, can vary significantly with the cell's identity, location, as well as developmental and physiological state. Various algorithms have been developed to customize shape based statistical and graph related features for quantitative analysis of neuromorphology, followed by the classification of neuron cell types using the features. Unlike the classical feature extraction based methods from imaged or 3D reconstructed neurons, we propose a model based on the rooted path decomposition from the soma to the dendrites of a neuron and extract morphological features from each constituent path. We hypothesize that measuring the distance between two neurons can be realized by minimizing the cost of continuously morphing the set of all rooted paths of one neuron to another. To validate this claim, we first establish the correspondence of paths between two neurons using a modified Munkres algorithm. Next, an elastic deformation framework that employs the square root velocity function is established to perform the continuous morphing, which, as an added benefit, provides an effective visualization tool. We experimentally show the efficacy of NeuroPath2Path, NeuroP2P, over the state of the art.

Golgi-associated microtubules are fast cargo tracks and required for persistent cell migration

Microtubules derived from the Golgi (Golgi MTs) have been implicated to play critical roles in persistent cell migration, but the underlying mechanisms remain elusive, partially due to the lack of direct observation of Golgi MT-dependent vesicular trafficking. Here, using super-resolution stochastic optical reconstruction microscopy (STORM), we discovered that post-Golgi cargos are more enriched on Golgi MTs and also surprisingly move much faster than on non-Golgi MTs. We found that, compared to non-Golgi MTs, Golgi MTs are morphologically more polarized toward the cell leading edge with significantly fewer inter-MT intersections. In addition, Golgi MTs are more stable and contain fewer lattice repair sites than non-Golgi MTs. Our STORM/live-cell imaging demonstrates that cargos frequently pause at the sites of both MT intersections and MT defects. Furthermore, by optogenetic maneuvering of cell direction, we demonstrate that Golgi MTs are essential for persistent cell migration but not for cells to change direction. Together, our study unveils the role of Golgi MTs in serving as a group of "fast tracks" for anterograde trafficking of post-Golgi cargos.

A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures: Two image analytic approaches using adaptive thresholds or machine learning

BACKGROUND

Assessments of axonal outgrowth and dendritic development are essential readouts in many in vitro models in the field of neuroscience. Available analysis software is based on the assessment of fixed immunolabelled tissue samples, making it impossible to follow the dynamic development of neurite outgrowth. Thus, automated algorithms that efficiently analyse brightfield images, such as those obtained during time-lapse microscopy, are needed.

NEW METHOD

We developed and validated algorithms to quantitatively assess neurite outgrowth from living and unstained spinal cord slice cultures (SCSCs) and dorsal root ganglion cultures (DRGCs) based on an adaptive thresholding approach called NeuriteSegmantation. We used a machine learning approach to evaluate dendritic development from dissociate neuron cultures.

RESULTS

NeuriteSegmentation successfully recognized axons in brightfield images of SCSCs and DRGCs. The temporal pattern of axonal growth was successfully assessed. In dissociate neuron cultures the total number of cells and their outgrowth of dendrites were successfully assessed using machine learning.

COMPARISON WITH EXISTING METHODS

The methods were positively correlated and were more time-saving than manual counts, having performing times varying from 0.5-2 min. In addition, NeuriteSegmentation was compared to NeuriteJ®, that uses global thresholding, being more reliable in recognizing axons in areas of intense background.

CONCLUSION

The developed image analysis methods were more time-saving and user-independent than established approaches. Moreover, by using adaptive thresholding, we could assess images with large variations in background intensity. These tools may prove valuable in the quantitative analysis of axonal and dendritic outgrowth from numerous in vitro models used in neuroscience.

Neuronal Morphology and Synapse Count in the Nematode Worm

The somatic nervous system of the nematode worm Caenorhabditis elegans is a model for understanding the physical characteristics of the neurons and their interconnections. Its neurons show high variation in morphological attributes. This study investigates the relationship of neuronal morphology to the number of synapses per neuron. Morphology is also examined for any detectable association with neuron cell type or ganglion membership.

FMST: an Automatic Neuron Tracing Method Based on Fast Marching and Minimum Spanning Tree

Neuron reconstruction is an important technique in computational neuroscience. Although there are many reconstruction algorithms, few can generate robust results. In this paper, we propose a reconstruction algorithm called fast marching spanning tree (FMST). FMST is based on a minimum spanning tree method (MST) and improve its performance in two aspects: faster implementation and no loss of small branches. The contributions of the proposed method are as follows. Firstly, the Euclidean distance weight of edges in MST is improved to be a more reasonable value, which is related to the probability of the existence of an edge. Secondly, a strategy of pruning nodes is presented, which is based on the radius of a node's inscribed ball. Thirdly, separate branches of broken neuron reconstructions can be merged into a single tree. FMST and many other state of the art reconstruction methods were implemented on two datasets: 120 Drosophila neurons and 163 neurons with gold standard reconstructions. Qualitative and quantitative analysis on experimental results demonstrates that the performance of FMST is good compared with many existing methods. Especially, on the 91 fruitfly neurons with gold standard and evaluated by five metrics, FMST is one of two methods with best performance among all 27 state of the art reconstruction methods. FMST is a good and practicable neuron reconstruction algorithm, and can be implemented in Vaa3D platform as a neuron tracing plugin.

Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning

The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patients suffering from neurodegenerative diseases, increasingly involves image-based high-content screening and analysis. The first critical step toward fully automated high-content image analyses in such studies is to detect all neuronal cells and distinguish them from possible non-neuronal cells or artifacts in the images. Here we investigate the performance of well-established machine learning techniques for this purpose. These include support vector machines, random forests, k-nearest neighbors, and generalized linear model classifiers, operating on an extensive set of image features extracted using the compound hierarchy of algorithms representing morphology, and the scale-invariant feature transform. We present experiments on a dataset of rat hippocampal neurons from our own studies to find the most suitable classifier(s) and subset(s) of features in the common practical setting where there is very limited annotated data for training. The results indicate that a random forests classifier using the right feature subset ranks best for the considered task, although its performance is not statistically significantly better than some support vector machine based classification models.

Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method

Neuronal shape reconstruction is a helpful technique for establishing neuron identity, inferring neuronal connections, mapping neuronal circuits, and so on. Advances in optical imaging techniques have enabled data collection that includes the shape of a neuron across the whole brain, considerably extending the scope of neuronal anatomy. However, such datasets often include many fuzzy neurites and many crossover regions that neurites are closely attached, which make neuronal shape reconstruction more challenging. In this study, we proposed a convex image segmentation model for neuronal shape reconstruction that segments a neurite into cross sections along its traced skeleton. Both the sparse nature of gradient images and the rule that fuzzy neurites usually have a small radius are utilized to improve neuronal shape reconstruction in regions with fuzzy neurites. Because the model is closely related to the traced skeleton point, we can use this relationship for identifying neurite with crossover regions. We demonstrated the performance of our model on various datasets, including those with fuzzy neurites and neurites with crossover regions, and we verified that our model could robustly reconstruct the neuron shape on a brain-wide scale.

A Multiscale Ray-Shooting Model for Termination Detection of Tree-Like Structures in Biomedical Images

Digital reconstruction (tracing) of tree-like structures, such as neurons, retinal blood vessels, and bronchi, from volumetric images and 2D images is very important to biomedical research. Many existing reconstruction algorithms rely on a set of good seed points. The 2D or 3D terminations are good candidates for such seed points. In this paper, we propose an automatic method to detect terminations for tree-like structures based on a multiscale ray-shooting model and a termination visual prior. The multiscale ray-shooting model detects 2D terminations by extracting and analyzing the multiscale intensity distribution features around a termination candidate. The range of scale is adaptively determined according to the local neurite diameter estimated by the Rayburst sampling algorithm in combination with the gray-weighted distance transform. The termination visual prior is based on a key observation-when observing a 3D termination from three orthogonal directions without occlusion, we can recognize it in at least two views. Using this prior with the multiscale ray-shooting model, we can detect 3D terminations with high accuracies. Experiments on 3D neuron image stacks, 2D neuron images, 3D bronchus image stacks, and 2D retinal blood vessel images exhibit average precision and recall rates of 87.50% and 90.54%. The experimental results confirm that the proposed method outperforms other the state-of-the-art termination detection methods.

NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks

Motivation

From image stacks to computational models, processing digital representations of neuronal morphologies is essential to neuroscientific research. Workflows involve various techniques and tools, leading in certain cases to convoluted and fragmented pipelines. The existence of an integrated, extensible and free framework for processing, analysis and visualization of those morphologies is a challenge that is still largely unfulfilled.

Results

We present NeuroMorphoVis, an interactive, extensible and cross-platform framework for building, visualizing and analyzing digital reconstructions of neuronal morphology skeletons extracted from microscopy stacks. Our framework is capable of detecting and repairing tracing artifacts, allowing the generation of high fidelity surface meshes and high resolution volumetric models for simulation and in silico imaging studies. The applicability of NeuroMorphoVis is demonstrated with two case studies. The first simulates the construction of three-dimensional profiles of neuronal somata and the other highlights how the framework is leveraged to create volumetric models of neuronal circuits for simulating different types of in vitro imaging experiments.

Availability and implementation

The source code and documentation are freely available on https://github.com/BlueBrain/NeuroMorphoVis under the GNU public license. The morphological analysis, visualization and surface meshing are implemented as an extensible Python API (Application Programming Interface) based on Blender, and the volume reconstruction and analysis code is written in C++ and parallelized using OpenMP. The framework features are accessible from a user-friendly GUI (Graphical User Interface) and a rich CLI (Command Line Interface).

Supplementary information

Supplementary data are available at Bioinformatics online.

The Anti-inflammatory Compound Candesartan Cilexetil Improves Neurological Outcomes in a Mouse Model of Neonatal Hypoxia

Recent studies suggest that mild hypoxia-induced neonatal seizures can trigger an acute neuroinflammatory response leading to long-lasting changes in brain excitability along with associated cognitive and behavioral deficits. The cellular elements and signaling pathways underlying neuroinflammation in this setting remain incompletely understood but could yield novel therapeutic targets. Here we show that brief global hypoxia-induced neonatal seizures in mice result in transient cytokine production, a selective expansion of microglia and long-lasting changes to the neuronal structure of pyramidal neurons in the hippocampus. Treatment of neonatal mice after hypoxia-seizures with the novel anti-inflammatory compound candesartan cilexetil suppressed acute seizure-damage and mitigated later-life aggravated seizure responses and hippocampus-dependent learning deficits. Together, these findings improve our understanding of the effects of neonatal seizures and identify potentially novel treatments to protect against short and long-lasting harmful effects.

Maternal exposure to swainsonine impaired the early postnatal development of mouse dentate gyrus of offspring

Neurogenesis in the dentate gyrus (DG) plays a key role in the normal of structure and function of the hippocampus-learning and memory. After eating the locoweeds, animals develop a chronic neurological disease called "locoism". Swainsonine (SW) is the main toxin in locoweeds. Studies have shown that SW induces neuronal apoptosis in vitro and impairs learning and memory in adult mouse. The present study explored effects of SW exposure to dams on the postnatal neurogenesis of DG of offspring. Pregnant ICR mice were orally gavaged with SW at a dose of 0, 5.6 or 8.4 mg/kg/day from gestation day 10 to postnatal day (PND) 21, respectively. We found that SW impaired the proliferation capacity of neural progenitor cells in the DG so that the number of newborn cells was reduced at PND 8. Using the postnatal in vivo electroporation, we showed that the dendritic branching and total length of granule cells were significantly decreased due to SW exposure. In addition, on PND 21, the density of NeuN-positive and Reelin-positive interneurons increased in the hilus, implying the disorder of neuronal migration. These results suggest that maternal exposure to SW, the neurogenesis of DG on offspring was disrupted, finally leading to the functional disorder of DG.

Lavender essential oil ameliorates depression-like behavior and increases neurogenesis and dendritic complexity in rats

Depression is a major health issue that causes severe societal economic and health burden. Aromatherapy, a practice that uses essential oils for preventive and therapeutic purposes, represents a promising therapeutic alternative for the alleviation of depressive symptoms. Lavender essential oil (LEO) has been the focus of clinical studies due to its positive effect on mood. An animal model of chronic administration of high dose corticosterone to induce depression- and anxiety-like behavior and reduced neurogenesis was used to explore the biological changes brought by aromatherapy. Twenty-four adult male Sprague Dawley rats were randomly assigned into four groups: Control, corticosterone (Cort) group with high dose of corticosterone, LEO group with daily exposure to LEO by inhalation, and LEO + Cort. At the end of the 14-day treatment period, behavioral tests were carried out. Serum samples were collected 2-3 days after the 14-day period treatment and before perfusion to carry out biochemical analyses to measure BDNF, corticosterone and oxytocin. After perfusion, brains were collected for immunohistochemical analysis to detect BrdU and DCX positive cells in the hippocampus and subventricular zone. Results showed that treatment with LEO ameliorated the depression-like behavior induced by the chronic administration of corticosterone as observed in the LEO + Cort group. Cort treatment reduced the number of BrdU positive cells in the hippocampus and the subventricular zone. Treatment with LEO prevented the corticosterone-induced reduction in the number of BrdU positive cells (LEO + Cort group) demonstrating the neurogenic effect of LEO under high corticosterone conditions. Chronic administration of high dose of corticosterone significantly reduced the dendritic complexity of immature neurons. On the contrary, treatment with LEO increased dendritic complexity of immature neurons under high corticosterone conditions (LEO + Cort group). The improved neurogenesis and dendritic complexity observed in the LEO + Cort group demonstrated a clear restorative effect of LEO under high corticosterone conditions. However, 2-3 days after the treatment, the levels of BDNF were upregulated in the LEO and LEO + Cort groups. Furthermore, the concentration of oxytocin in serum, 2-3 days after the treatment, showed to be upregulated in the LEO group alone. The present study has provided evidence of the biological effect of LEO on neuroplasticity and neurogenesis. Also, this study contributes to the understanding of the mechanism of action of LEO in an animal model where depression- and anxiety-like behavior and reduced neurogenesis were induced by high corticosterone administration.

Quantitative Determination of Cellular-and Neurite Motility Speed in Dense Cell Cultures

Mobility quantification of single cells and cellular processes in dense cultures is a challenge, because single cell tracking is impossible. We developed a software for cell structure segmentation and implemented 2 algorithms to measure motility speed. Complex algorithms were tested to separate cells and cellular components, an important prerequisite for the acquisition of meaningful motility data. Plasma membrane segmentation was performed to measure membrane contraction dynamics and organelle trafficking. The discriminative performance and sensitivity of the algorithms were tested on different cell types and calibrated on computer-simulated cells to obtain absolute values for cellular velocity. Both motility algorithms had advantages in different experimental setups, depending on the complexity of the cellular movement. The correlation algorithm (COPRAMove) performed best under most tested conditions and appeared less sensitive to variable cell densities, brightness and focus changes than the differentiation algorithm (DiffMove). In summary, our software can be used successfully to analyze and quantify cellular and subcellular movements in dense cell cultures.

Artificial neural network filters for enhancing 3D optical microscopy images of neurites

The ability to extract accurate morphology of labeled neurons from microscopy images is crucial for mapping brain connectivity and for understanding changes in connectivity that underlie learning and neurological disorders. There are, however, two problems, specific to optical microscopy imaging of neurons, which make accurate neuron tracing exceedingly challenging: (i) neurites can appear broken due to inhomogeneous labeling and (ii) neurites can appear fused in 3D due to limited resolution. Here, we propose and evaluate several artificial neural network (ANN) architectures and conventional image enhancement filters with the aim of alleviating both problems. We developed four image quality metrics to evaluate the effects of the proposed filters: normalized intensity in the cross-over regions between neurites, effective radius of neurites, coefficient of variation of intensity along neurites, and local background to neurite intensity ratio. Our results show that ANN-based filters, trained on optimized semi-manual traces of neurites, can significantly outperform conventional filters. In particular, U-Net based filtering can virtually eliminate background intensity, while also reducing the effective radius of neurites to nearly 1 voxel. In addition, this filter significantly decreases intensity in the cross-over regions between neurites and reduces fluctuations of intensity on neurites' centerlines. These results suggest that including an ANN-based filtering step, which does not require substantial extra time or computing power, can be beneficial for automated neuron tracing projects.

Experimental and Computational Methods for the Study of Cerebral Organoids: A Review

Cerebral (or brain) organoids derived from human cells have enormous potential as physiologically relevant downscaled in vitro models of the human brain. In fact, these stem cell-derived neural aggregates resemble the three-dimensional (3D) cytoarchitectural arrangement of the brain overcoming not only the unrealistic somatic flatness but also the planar neuritic outgrowth of the two-dimensional (2D) in vitro cultures. Despite the growing use of cerebral organoids in scientific research, a more critical evaluation of their reliability and reproducibility in terms of cellular diversity, mature traits, and neuronal dynamics is still required. Specifically, a quantitative framework for generating and investigating these in vitro models of the human brain is lacking. To this end, the aim of this review is to inspire new computational and technology driven ideas for methodological improvements and novel applications of brain organoids. After an overview of the organoid generation protocols described in the literature, we review the computational models employed to assess their formation, organization and resource uptake. The experimental approaches currently provided to structurally and functionally characterize brain organoid networks for studying single neuron morphology and their connections at cellular and sub-cellular resolution are also discussed. Well-established techniques based on current/voltage clamp, optogenetics, calcium imaging, and Micro-Electrode Arrays (MEAs) are proposed for monitoring intra- and extra-cellular responses underlying neuronal dynamics and functional connections. Finally, we consider critical aspects of the established procedures and the physiological limitations of these models, suggesting how a complement of engineering tools could improve the current approaches and their applications.

Optimization of Traced Neuron Skeleton Using Lasso-Based Model

Reconstruction of neuronal morphology from images involves mainly the extraction of neuronal skeleton points. It is an indispensable step in the quantitative analysis of neurons. Due to the complex morphology of neurons, many widely used tracing methods have difficulties in accurately acquiring skeleton points near branch points or in structures with tortuosity. Here, we propose two models to solve these problems. One is based on an L1-norm minimization model, which can better identify tortuous structure, namely, a local structure with large curvature skeleton points; the other detects an optimized branch point by considering the combination patterns of all neurites that link to this point. We combined these two models to achieve optimized skeleton detection for a neuron. We validate our models in various datasets including MOST and BigNeuron. In addition, we demonstrate that our method can optimize the traced skeletons from large-scale images. These characteristics of our approach indicate that it can reduce manual editing of traced skeletons and help to accelerate the accurate reconstruction of neuronal morphology.

Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites

Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the whole brain. However, high-level automation reconstruction of neurons, i.e., the reconstruction with a few of manual edits requires discrimination of weak foreground points from the inhomogeneous background. We constructed an identification model, where empirical observations made from neuronal images were summarized into rules for designing feature vectors that to classify foreground and background, and a support vector machine (SVM) was used to learn these feature vectors. We embedded this constructed SVM classifier into a previously developed tool, SparseTracer, to obtain SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace sparsely distributed neurites with weak signals (contrast-to-noise ratio < 1.5) against an inhomogeneous background in datasets imaged by widely used light-microscopy techniques like confocal microscopy and two-photon microscopy. Moreover, 12 sub-blocks were extracted from different brain regions. The average recall and precision rates were 99% and 97%, respectively. These results indicated that ST-LFV is well suited for weak signal identification with varying image characteristics. We also applied ST-LFV to trace long-range neurites from images where neurites are sparsely distributed but their image intensities are weak in some cases. When tracing this long-range neurites, manual edit was required once to obtain results equivalent to the ground truth, compared with 20 times of manual edits required by SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the large scale.

PAT-Probabilistic Axon Tracking for Densely Labeled Neurons in Large 3-D Micrographs

A major goal of contemporary neuroscience research is to map the structural connectivity of mammalian brain using microscopy imaging data. In this context, the reconstruction of densely labeled axons from two-photon microscopy images is a challenging and important task. The visually overlapping, crossing, and often strongly distorted images of the axons allow many ambiguous interpretations to be made. We address the problem of tracking axons in densely labeled samples of neurons in large image data sets acquired from marmoset brains. Our high-resolution images were acquired using two-photon microscopy and they provided whole brain coverage, occupying terabytes of memory. Both the image distortions and the large data set size frequently make it impractical to apply present-day neuron tracing algorithms to such data due to the optimization of such algorithms to the precise tracing of either single or sparse sets of neurons. Thus, new tracking techniques are needed. We propose a probabilistic axon tracking algorithm (PAT). PAT tackles the tracking of axons in two steps: locally (L-PAT) and globally (G-PAT). L-PAT is a probabilistic tracking algorithm that can tackle distorted, cluttered images of densely labeled axons. L-PAT divides a large micrograph into smaller image stacks. It then processes each image stack independently before mapping the axons in each image to a sparse model of axon trajectories. G-PAT merges the sparse L-PAT models into a single global model of axon trajectories by minimizing a global objective function using a probabilistic optimization method. We demonstrate the superior performance of PAT over standard approaches on synthetic data. Furthermore, we successfully apply PAT to densely labeled axons in large images acquired from marmoset brains.

Computing neurite outgrowth and arborization in superior cervical ganglion neurons

Dendrites are the primary site of synaptic activity in neurons and changes in synapses are often the first pathological stage in neurodegenerative diseases. Molecular studies of these changes rely on morphological analysis of the imaging of somas and dendritic arbors of cultured or primary neurons. As research on preventing or reversing synaptic degeneration develops, demands increase for user-friendly 2D neurite analyzers without undermining accuracy and reproducibility. The most common method of 2D neurite analysis is manual by using ImageJ. This method relies completely on the user's ability to distinguish the shape and size of dendrites and trace morphology with a series of straight connected lines. Semi-automatic methods have also been developed, such as the NeuronJ plugin for ImageJ. These methods still rely on the user to identify the start and end of the dendrites, but automatically determine the shape, reducing the likelihood of user bias and speeding the process. Some automatic methods have been developed through image processing software, like ImagePro. These programs tend to be expensive, but have been shown to be fast and effective, limiting user interaction. In this study, we compare three methods of neurite analysis-ImageJ, NeuronJ, and ImagePro-in measuring the soma size, number of dendrites, and length of dendrites per cell of embryonic sympathetic rat neurons with BMP-7-induced dendritic growth. Our results indicate that ImageJ and NeuronJ measurements were of similar effectiveness and consistent throughout various images and multiple trials. NeuronJ required less user interaction in measuring the length of dendrites than the manual method and therefore, was faster and less labor intensive. Conversely, ImagePro tended to be inconsistent across images, overestimating both soma size and the number of dendrites per cell while underestimating the length of dendrites. Overall, NeuronJ, in conjunction with ImageJ, is the most reliable and efficient method of 2D neurite analysis tested in the present study.

Modeling Diabetic Corneal Neuropathy in a 3D In Vitro Cornea System

Diabetes mellitus is a disease caused by innate or acquired insulin deficiency, resulting in altered glucose metabolism and high blood glucose levels. Chronic hyperglycemia is linked to development of several ocular pathologies affecting the anterior segment, including diabetic corneal neuropathy and keratopathy, neovascular glaucoma, edema, and cataracts leading to significant visual defects. Due to increasing disease prevalence, related medical care costs, and visual impairment resulting from diabetes, a need has arisen to devise alternative systems to study molecular mechanisms involved in disease onset and progression. In our current study, we applied a novel 3D in vitro model of the human cornea comprising of epithelial, stromal, and neuronal components cultured in silk scaffolds to study the pathological effects of hyperglycemia on development of diabetic corneal neuropathy. Specifically, exposure to sustained levels of high glucose, ranging from 35 mM to 45 mM, were applied to determine concentration-dependent effects on nerve morphology, length and density of axons, and expression of metabolic enzymes involved in glucose metabolism. By comparing these metrics to in vivo studies, we have developed a functional 3D in vitro model for diabetic corneal neuropathy as a means to investigate corneal pathophysiology resulting from prolonged exposure to hyperglycemia.

MicroRNA-451a overexpression induces accelerated neuronal differentiation of Ntera2/D1 cells and ablation affects neurogenesis in microRNA-451a-/- mice

MiR-451a is best known for its role in erythropoiesis and for its tumour suppressor features. Here we show a role for miR-451a in neuronal differentiation through analysis of endogenous and ectopically expressed or silenced miR-451a in Ntera2/D1 cells during neuronal differentiation. Furthermore, we compared neuronal differentiation in the dentate gyrus of hippocampus of miR-451a^-/- and wild type mice. MiR-451a overexpression in lentiviral transduced Ntera2/D1 cells was associated with a significant shifting of mRNA expression of the developmental markers Nestin, βIII Tubulin, NF200, DCX and MAP2 to earlier developmental time points, compared to control vector transduced cells. In line with this, accelerated neuronal network formation in AB.G.miR-451a transduced cells, as well as an increase in neurite outgrowth both in number and length was observed. MiR-451a targets genes MIF, AKT1, CAB39, YWHAZ, RAB14, TSC1, OSR1, POU3F2, TNS4, PSMB8, CXCL16, CDKN2D and IL6R were, moreover, either constantly downregulated or exhibited shifted expression profiles in AB.G.miR-451a transduced cells. Lentiviral knockdown of endogenous miR-451a expression in Ntera2/D1 cells resulted in decelerated differentiation. Endogenous miR-451a expression was upregulated during development in the hippocampus of wildtype mice. In situ hybridization revealed intensively stained single cells in the subgranular zone and the hilus of the dentate gyrus of wild type mice, while genetic ablation of miR-451a was observed to promote an imbalance between proliferation and neuronal differentiation in neurogenic brain regions, suggested by Ki67 and DCX staining. Taken together, these results provide strong support for a role of miR-451a in neuronal maturation processes in vitro and in vivo.

Tropomodulin's Actin-Binding Abilities Are Required to Modulate Dendrite Development

There are many unanswered questions about the roles of the actin pointed end capping and actin nucleation by tropomodulins (Tmod) in regulating neural morphology. Previous studies indicate that Tmod1 and Tmod2 regulate morphology of the dendritic arbor and spines. Tmod3, which is expressed in the brain, had only a minor influence on morphology. Although these studies established a defined role of Tmod in regulating dendritic and synaptic morphology, the mechanisms by which Tmods exert these effects are unknown. Here, we overexpressed a series of mutated forms of Tmod1 and Tmod2 with disrupted actin-binding sites in hippocampal neurons and found that Tmod1 and Tmod2 require both of their actin-binding sites to regulate dendritic morphology and dendritic spine shape. Proximity ligation assays (PLAs) indicate that these mutations impact the interaction of Tmod1 and Tmod2 with tropomyosins Tpm3.1 and Tpm3.2. This impact on Tmod/Tpm interaction may contribute to the morphological changes observed. Finally, we use molecular dynamics simulations (MDS) to characterize the structural changes, caused by mutations in the C-terminal helix of the leucine-rich repeat (LRR) domain of Tmod1 and Tmod2 alone and when bound onto actin monomers. Our results expand our understanding of how neurons utilize the different Tmod isoforms in development.

Mining Big Neuron Morphological Data

The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive research in the field of automatic neurons classification and presents the existing challenges, methods, tools, and future directions for automatic neuromorphology analytics. We summarize the major automatic techniques applicable in the field and propose a systematic data processing pipeline for automatic neuron classification, covering data capturing, preprocessing, analyzing, classification, and retrieval. Various techniques and algorithms in machine learning are illustrated and compared to the same dataset to facilitate ongoing research in the field.

KLB, encoding β-Klotho, is mutated in patients with congenital hypogonadotropic hypogonadism

Congenital hypogonadotropic hypogonadism (CHH) is a rare genetic form of isolated gonadotropin‐releasing hormone (GnRH) deficiency caused by mutations in > 30 genes. Fibroblast growth factor receptor 1 (FGFR1) is the most frequently mutated gene in CHH and is implicated in GnRH neuron development and maintenance. We note that a CHH FGFR1 mutation (p.L342S) decreases signaling of the metabolic regulator FGF21 by impairing the association of FGFR1 with β‐Klotho (KLB), the obligate co‐receptor for FGF21. We thus hypothesized that the metabolic FGF21/KLB/FGFR1 pathway is involved in CHH. Genetic screening of 334 CHH patients identified seven heterozygous loss‐of‐function KLB mutations in 13 patients (4%). Most patients with KLB mutations (9/13) exhibited metabolic defects. In mice, lack of Klb led to delayed puberty, altered estrous cyclicity, and subfertility due to a hypothalamic defect associated with inability of GnRH neurons to release GnRH in response to FGF21. Peripheral FGF21 administration could indeed reach GnRH neurons through circumventricular organs in the hypothalamus. We conclude that FGF21/KLB/FGFR1 signaling plays an essential role in GnRH biology, potentially linking metabolism with reproduction.

Short-Term Effects of Overnight Orthokeratology on Corneal Sub-basal Nerve Plexus Morphology and Corneal Sensitivity

OBJECTIVE

To assess the effects of a short period of orthokeratology (OK) on corneal sub-basal nerve plexus (SBNP) morphology and corneal sensitivity.

METHODS

Measurements were made in 56 right eyes of 56 subjects with low-to-moderate myopia who wore 2 OK lens designs (Group CRT: HDS 100 Paragon CRT, n=35; Group SF: Seefree; n=21) for a period of 1 month and in 15 right eyes of noncontact lens wearers as controls. The variables determined in each participant were corneal sensitivity using a Cochet-Bonnet esthesiometer and 12 SBNP variables determined on laser scanning confocal microscopy images using 3 different software packages. Correlation between SBNP architecture and corneal sensitivity was also examined.

RESULTS

Few changes were observed over the 1-month period in the variables examined in the OK treatment and control groups. However, significant reductions were detected over time in the number of nerves in the central cornea in the groups CRT (P=0.029) and SF (P=0.043) and in central corneal sensitivity in CRT (P=0.047) along with significant increases in central and midperipheral corneal Langerhans cell counts in SF (P=0.001 and 0.048, respectively).

CONCLUSIONS

This study provides useful data to better understand the anatomical changes induced by OK in corneal SBNP. The different response observed to the 2 OK lens designs requires further investigation.

Ensemble Neuron Tracer for 3D Neuron Reconstruction

Tracing of neuron paths is important in neuroscience. Recent studies have shown that it is possible to segment and reconstruct three-dimensional morphology of axons and dendrites using fully automatic neuron tracing methods. A specific tracer may be better than others for a specific dataset, but another tracer could perform better for some other datasets. Ensemble of learners is an effective way to improve learning accuracy in machine learning. We developed automatic ensemble neuron tracers, which consistently perform well on 57 datasets of 5 species collected from 7 laboratories worldwide. Quantitative evaluation based on the data generated by human annotators shows that the proposed ensemble tracers are valuable for 3D neuron tracing and can be widely applied to different datasets.