It is important to digitally reconstruct the 3D morphology of neurons and brain vasculatures. A number of previous methods have been proposed to automate the reconstruction process. However, in many cases, noise and low signal contrast with respect to the image background still hamper our ability to use automation methods directly. Here, we propose an adaptive image enhancement method specifically designed to improve the signal-to-noise ratio of several types of individual neurons and brain vasculature images. Our method is based on detecting the salient features of fibrous structures, e.g. the axon and dendrites combined with adaptive estimation of the optimal context windows where such saliency would be detected. We tested this method for a range of brain image datasets and imaging modalities, including bright-field, confocal and multiphoton fluorescent images of neurons, and magnetic resonance angiograms. Applying our adaptive enhancement to these datasets led to improved accuracy and speed in automated tracing of complicated morphology of neurons and vasculatures.

Topographic projection of afferent terminals into 2D maps in the CNS is a general strategy used by the nervous system to encode the locations of sensory stimuli. In vertebrates, it is known that although guidance cues are critical for establishing a coarse topographic map, neural activity directs fine-scale topography between adjacent afferent terminals [1-4]. However, the molecular mechanism underlying activity-dependent regulation of fine-scale topography is poorly understood. Molecular analysis of the spatial relationship between adjacent afferent terminals requires reliable localization of the presynaptic terminals of single neurons as well as genetic manipulations with single-cell resolution in vivo. Although both requirements can potentially be met in Drosophila melanogaster [5, 6], no activity-dependent topographic system has been identified in flies [7]. Here we report a topographic system that is shaped by neuronal activity in Drosophila. With this system, we found that topographic separation of the presynaptic terminals of adjacent nociceptive neurons requires different levels of Trim9, an evolutionarily conserved signaling molecule [8-11]. Neural activity regulates Trim9 protein levels to direct fine-scale topography of sensory afferents. This study offers both a novel mechanism by which neural activity directs fine-scale topography of axon terminals and a new system to study this process at single-neuron resolution.

Glioma glutamate release has been shown to promote the growth of glioma cells and induce neuronal injuries from epilepsy to neuronal death. However, potential counteractions from normal astrocytes against glioma glutamate release have not been fully evaluated. In this study, we investigated the glutamate/glutamine cycling between glioma cells and astrocytes and their impact on neuronal function. Co-cultures of glioma cells with astrocytes (CGA) in direct contact were established under different mix ratio of astrocyte/glioma. Culture medium conditioned in these CGAs were sampled for HPLC measurement, for neuronal ratiometric calcium imaging, and for neuronal survival assay. We found: (1) High levels of glutaminase expression in glioma cells, but not in astrocytes, glutaminase enables glioma cells to release large amount of glutamate in the presence of glutamine. (2) Glutamate levels in CGAs were directly determined by the astrocyte/glioma ratios, indicating a balance between glioma glutamate release and astrocyte glutamate uptake. (3) Culture media from CGAs of higher glioma/astrocyte ratios induced stronger neuronal Ca(2+) response and more severe neuronal death. (4) Co-culturing with astrocytes significantly reduced the growth rate of glioma cells. These results indicate that normal astrocytes in the brain play pivotal roles in glioma growth inhibition and in reducing neuronal injuries from glioma glutamate release. However, as tumor growth, the protective role of astrocytes gradually succumb to glioma cells.

In this work, we propose SmartTracing, an automatic tracing framework that does not require substantial human intervention. There are two major novelties in SmartTracing. First, given an input image, SmartTracing invokes a user-provided existing neuron tracing method to produce an initial neuron reconstruction, from which the likelihood of every neuron reconstruction unit is estimated. This likelihood serves as a confidence score to identify reliable regions in a neuron reconstruction. With this score, SmartTracing automatically identifies reliable portions of a neuron reconstruction generated by some existing neuron tracing algorithms, without human intervention. These reliable regions are used as training exemplars. Second, from the training exemplars the most characteristic wavelet features are automatically selected and used in a machine learning framework to predict all image areas that most probably contain neuron signal. Since the training samples and their most characterizing features are selected from each individual image, the whole process is automatically adaptive to different images. Notably, SmartTracing can improve the performance of an existing automatic tracing method. In our experiment, with SmartTracing we have successfully reconstructed complete neuron morphology of 120 Drosophila neurons. In the future, the performance of SmartTracing will be tested in the BigNeuron project (bigneuron.org). It may lead to more advanced tracing algorithms and increase the throughput of neuron morphology-related studies.

Efficient and accurate digital reconstruction of neurons from large-scale 3D microscopic images remains a challenge in neuroscience. We propose a new automatic 3D neuron reconstruction algorithm, TReMAP, which utilizes 3D Virtual Finger (a reverse-mapping technique) to detect 3D neuron structures based on tracing results on 2D projection planes. Our fully automatic tracing strategy achieves close performance with the state-of-the-art neuron tracing algorithms, with the crucial advantage of efficient computation (much less memory consumption and parallel computation) for large-scale images.

In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain.

Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons.

The musculoskeletal system plays a critical role in providing the physical scaffold and movement to the mammalian body. Musculoskeletal disorders severely affect mobility and quality of life and pose a heavy burden to society. This new field of musculoskeletal tissue engineering has great potential as an alternative approach to treating large musculoskeletal defects. Natural and synthetic polymers are widely used in musculoskeletal tissue engineering owing to their good biocompatibility and biodegradability. Even more promising is the use of natural and synthetic polymer composites, as well as the combination of polymers and inorganic materials, to repair musculoskeletal tissue. Therefore, this review summarizes the progress of polymer-based scaffolds for applications of musculoskeletal tissue engineering and briefly discusses the challenges and future perspectives.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.

Studies have shown that mobile apps have the potential to serve as nonpharmacological interventions for dementia care, improving the quality of life of people living with dementia and their informal caregivers. However, little is known about the needs for and privacy aspects of these mobile apps in dementia care.

A major challenge in brain organoid technologies is the lack of vasculature. In recent years, innovative approaches have been taken to meet this challenge. A 2020 paper published in PLOS Biology exemplifies the approaches used in this booming field.

We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed whole-brain single-cell neuronal networks using an extensive dataset of 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census, and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole-brain networks at the single-cell level.

Knowledge of the molecular and genetic mechanisms underlying the separation of dendritic and axonal compartments is not only crucial for understanding the assembly of neural circuits, but also for developing strategies to correct defective dendrites or axons in diseases with subcellular precision. Previous studies have uncovered regulators dedicated to either dendritic or axonal growth. Here we investigate a novel regulatory mechanism that differentially directs dendritic and axonal growth within the same neuron in vivo. We find that the dual leucine zipper kinase (DLK) signaling pathway in Drosophila, which consists of Highwire and Wallenda and controls axonal growth, regeneration, and degeneration, is also involved in dendritic growth in vivo. Highwire, an evolutionarily conserved E3 ubiquitin ligase, restrains axonal growth but acts as a positive regulator for dendritic growth in class IV dendritic arborization neurons in the larva. While both the axonal and dendritic functions of highwire require the DLK kinase Wallenda, these two functions diverge through two downstream transcription factors, Fos and Knot, which mediate the axonal and dendritic regulation, respectively. This study not only reveals a previously unknown function of the conserved DLK pathway in controlling dendrite development, but also provides a novel paradigm for understanding how neuronal compartmentalization and the diversity of neuronal morphology are achieved.

Three-dimensional (3D) bioimaging, visualization and data analysis are in strong need of powerful 3D exploration techniques. We develop virtual finger (VF) to generate 3D curves, points and regions-of-interest in the 3D space of a volumetric image with a single finger operation, such as a computer mouse stroke, or click or zoom from the 2D-projection plane of an image as visualized with a computer. VF provides efficient methods for acquisition, visualization and analysis of 3D images for roundworm, fruitfly, dragonfly, mouse, rat and human. Specifically, VF enables instant 3D optical zoom-in imaging, 3D free-form optical microsurgery, and 3D visualization and annotation of terabytes of whole-brain image volumes. VF also leads to orders of magnitude better efficiency of automated 3D reconstruction of neurons and similar biostructures over our previous systems. We use VF to generate from images of 1,107 Drosophila GAL4 lines a projectome of a Drosophila brain.

Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories.

Expression of the Down syndrome cell-adhesion molecule (Dscam) is increased in the brains of patients with several neurological disorders. Although Dscam is critically involved in many aspects of neuronal development, little is known about either the mechanism that regulates its expression or the functional consequences of dysregulated Dscam expression. Here, we show that Dscam expression levels serve as an instructive code for the size control of presynaptic arbor. Two convergent pathways, involving dual leucine zipper kinase (DLK) and fragile X mental retardation protein (FMRP), control Dscam expression through protein translation. Defects in this regulation of Dscam translation lead to exuberant presynaptic arbor growth in Drosophila somatosensory neurons. Our findings uncover a function of Dscam in presynaptic size control and provide insights into how dysregulated Dscam may contribute to the pathogenesis of neurological disorders.

Understanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons.

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.

We established a collection of 7,000 transgenic lines of Drosophila melanogaster. Expression of GAL4 in each line is controlled by a different, defined fragment of genomic DNA that serves as a transcriptional enhancer. We used confocal microscopy of dissected nervous systems to determine the expression patterns driven by each fragment in the adult brain and ventral nerve cord. We present image data on 6,650 lines. Using both manual and machine-assisted annotation, we describe the expression patterns in the most useful lines. We illustrate the utility of these data for identifying novel neuronal cell types, revealing brain asymmetry, and describing the nature and extent of neuronal shape stereotypy. The GAL4 lines allow expression of exogenous genes in distinct, small subsets of the adult nervous system. The set of DNA fragments, each driving a documented expression pattern, will facilitate the generation of additional constructs for manipulating neuronal function.

Digital reconstruction of 3D neuron structures is an important step toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low single-to-noise ratio and discontinued segments of neurite patterns.