Ju Lu

Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system

Detailed analysis of neuronal network architecture requires the development of new methods. Here we present strategies to visualize synaptic circuits by genetically labelling neurons with multiple, distinct colours. In Brainbow transgenes, Cre/lox recombination is used to create a stochastic choice of expression between three or more fluorescent proteins (XFPs). Integration of tandem Brainbow copies in transgenic mice yielded combinatorial XFP expression, and thus many colours, thereby providing a way to distinguish adjacent neurons and visualize other cellular interactions. As a demonstration, we reconstructed hundreds of neighbouring axons and multiple synaptic contacts in one small volume of a cerebellar lobe exhibiting approximately 90 colours. The expression in some lines also allowed us to map glial territories and follow glial cells and neurons over time in vivo. The ability of the Brainbow system to label uniquely many individual cells within a population may facilitate the analysis of neuronal circuitry on a large scale.

The Interscutularis Muscle Connectome

The complete connectional map (connectome) of a neural circuit is essential for understanding its structure and function. Such maps have only been obtained in Caenorhabditis elegans. As an attempt at solving mammalian circuits, we reconstructed the connectomes of six interscutularis muscles from adult transgenic mice expressing fluorescent proteins in all motor axons. The reconstruction revealed several organizational principles of the neuromuscular circuit. First, the connectomes demonstrate the anatomical basis of the graded tensions in the size principle. Second, they reveal a robust quantitative relationship between axonal caliber, length, and synapse number. Third, they permit a direct comparison of the same neuron on the left and right sides of the same vertebrate animal, and reveal significant structural variations among such neurons, which contrast with the stereotypy of identified neurons in invertebrates. Finally, the wiring length of axons is often longer than necessary, contrary to the widely held view that neural wiring length should be minimized. These results show that mammalian muscle function is implemented with a variety of wiring diagrams that share certain global features but differ substantially in anatomical form. This variability may arise from the dominant role of synaptic competition in establishing the final circuit.

Lysosomal activity associated with developmental axon pruning.

Clearance of cellular debris is a critical feature of the developing nervous system, as evidenced by the severe neurological consequences of lysosomal storage diseases in children. An important developmental process, which generates considerable cellular debris, is synapse elimination, in which many axonal branches are pruned. The fate of these pruned branches is not known. Here, we investigate the role of lysosomal activity in neurons and glia in the removal of axon branches during early postnatal life. Using a probe for lysosomal activity, we observed robust staining associated with retreating motor axons. Lysosomal function was involved in axon removal because retreating axons were cleared more slowly in a mouse model of a lysosomal storage disease. In addition, we found lysosomal activity in the cerebellum at the time of, and at sites where, climbing fibers are eliminated. We propose that lysosomal activity is a central feature of synapse elimination. Moreover, staining for lysosomal activity may serve as a marker for regions of the developing nervous system undergoing axon pruning.

Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images

We introduce a method for large scale reconstruction of complex bundles of neural processes from fluorescent image stacks. We imaged yellow fluorescent protein labeled axons that innervated a whole muscle, as well as dendrites in cerebral cortex, in transgenic mice, at the diffraction limit with a confocal microscope. Each image stack was digitally re-sampled along an orientation such that the majority of axons appeared in cross-section. A region growing algorithm was implemented in the open-source Reconstruct software and applied to the semi-automatic tracing of individual axons in three dimensions. The progression of region growing is constrained by user-specified criteria based on pixel values and object sizes, and the user has full control over the segmentation process. A full montage of reconstructed axons was assembled from the ∼200 individually reconstructed stacks. Average reconstruction speed is ∼0.5 mm per hour. We found an error rate in the automatic tracing mode of ∼1 error per 250 um of axonal length. We demonstrated the capacity of the program by reconstructing the connectome of motor axons in a small mouse muscle.

Repulsive force based snake model to segment and track neuronal axons in 3D microscopy image stacks

The branching patterns of axons and dendrites are fundamental structural properties that affect the synaptic connectivity of axons. Although today three-dimensional images of fluorescently labeled processes can be obtained to study axonal branching, there are no robust methods of tracing individual axons. This paper describes a repulsive force based snake model to segment and track axonal profiles in 3D images. This new method segments all the axonal profiles in a 2D image and then uses the results obtained from that image as prior information to help segment the adjacent 2D image. In this way, the segmentation successfully connects axonal profiles over hundreds of images in a 3D image stack. Individual axons can then be extracted based on the segmentation results. The utility and performance of the method are demonstrated using 3D axonal images obtained from transgenic mice that express fluorescent protein.

Super-Resolution Laser Scanning Microscopy through Spatiotemporal Modulation

Super-resolution optical microscopy has attracted great interest among researchers in many fields, especially in biology where the scale of physical structures and molecular processes fall below the diffraction limit of resolution for light. As one of the emerging techniques, structured illumination microscopy can double the resolution by shifting unresolvable spatial frequencies into the pass-band of the microscope through spatial frequency mixing with a wide-field structured illumination pattern. However, such a wide-field scheme typically can only image optically thin samples and is incompatible with multiphoton processes such as two-photon fluorescence, which require point scanning with a focused laser beam. Here, we propose two new super-resolution schemes for laser scanning microscopy by generalizing the concept of a spatially nonuniform imaging system. One scheme, scanning patterned illumination (SPIN) microscopy, employs modulation of the excitation combined with temporally cumulative imaging by a nondescanned array detector. The other scheme, scanning patterned detection (SPADE) microscopy, utilizes detection modulation together with spatially cumulative imaging, in this case by a nondescanned single-element detector. When combined with multiphoton excitation, both schemes can image thick samples with three-dimensional optical sectioning and much improved resolution.

Using nonlinear diffusion and mean shift to detect and connect cross-sections of axons in 3D optical microscopy images.

The morphology of neuronal axons has been actively investigated by researchers to understand functionalities of neuronal networks, for example, in developmental neurology. Todays optical microscope and labeling techniques allow us to obtain high-resolution images about axons in three dimensions (3D), however, it remains challenging to segment and reconstruct the 3D morphology of axons. These include differentiating adjacent axons and detecting the axon branches. In this paper we present a method to track axons in 3D by identifying cross-sections of axons on 2D images and connecting the cross-sections over a series of 2D images to reconstruct the 3D morphology. The method can separate adjacent axons and detect the split and merge of axons. The method consists of three steps, modified nonlinear diffusion to remove noise and enhance edges in 2D, morphological operations to detect edges of the cross-sections of axons in 2D, and mean shift to track the cross-sections of axons in 3D. Performance of the method is demonstrated by processing real data acquired by confocal laser scanning microscopy.

Reconstruction of the neuromuscular junction connectome

Motivation: Unraveling the structure and behavior of the brain and central nervous system (CNS) has always been a major goal of neuroscience. Understanding the wiring diagrams of the neuromuscular junction connectomes (full connectivity of nervous system neuronal components) is a starting point for this, as it helps in the study of the organizational and developmental properties of the mammalian CNS. The phenomenon of synapse elimination during developmental stages of the neuronal circuitry is such an example. Due to the organizational specificity of the axons in the connectomes, it becomes important to label and extract individual axons for morphological analysis. Features such as axonal trajectories, their branching patterns, geometric information, the spatial relations of groups of axons, etc. are of great interests for neurobiologists in the study of wiring diagrams. However, due to the complexity of spatial structure of the axons, automatically tracking and reconstructing them from microscopy images in 3D is an unresolved problem. In this article, AxonTracker-3D, an interactive 3D axon tracking and labeling tool is built to obtain quantitative information by reconstruction of the axonal structures in the entire innervation field. The ease of use along with accuracy of results makes AxonTracker-3D an attractive tool to obtain valuable quantitative information from axon datasets. Availability: The software is freely available for download at http://www.cbi-tmhs.org/AxonTracker/ Contact: stwong@tmhs.org

3d axon structure extraction and analysis in confocal fluorescence microscopy images

The morphological properties of axons, such as their branching patterns and oriented structures, are of great interest for biologists in the study of the synaptic connectivity of neurons. In these studies, researchers use triple immunofluorescent confocal microscopy to record morphological changes of neuronal processes. Three-dimensional (3D) microscopy image analysis is then required to extract morphological features of the neuronal structures. In this article, we propose a highly automated 3D centerline extraction tool to assist in this task. For this project, the most difficult part is that some axons are overlapping such that the boundaries distinguishing them are barely visible. Our approach combines a 3D dynamic programming (DP) technique and marker-controlled watershed algorithm to solve this problem. The approach consists of tracking and updating along the navigation directions of multiple axons simultaneously. The experimental results show that the proposed method can rapidly and accurately extract multiple axon centerlines and can handle complicated axon structures such as cross-over sections and overlapping objects.

DYNAMIC LOCAL TRACING FOR 3D AXON CURVILINEAR STRUCTURE DETECTION FROM MICROSCOPIC IMAGE STACK

To study the morphologic structure of axons can help neuro-scientists understand the neuronal function and development. The modern microscopes provide the fundamental tool for visual inspection of axonal structure. Due to the high volume of generated microscopic axon image data, it is critical to develop an automated technique for robustly and rapidly detecting 3D axonal structure. In this paper, we present a pure 3D approach to extract the curvilinear structure of axonal axes from microscopic image stacks. The method mimics the axon tracing procedure in 3D space as walking along a path with minimized cost value, which corresponds to the shortest path problem (SPP) in graph theory. The global solution for SPP, such as Dijkstras algorithm, is infeasible for the real axon tracing problem because of the computation cost. We simplify this problem using a dynamic local tracing technique with linear computation complexity. The merits of the proposed method lie in that it can handle the short turn and non-vertical problems and also can separate closely distributed axons from each other