Fast extraction of neuron morphologies from large-scale SBFSEM image stacks

Neuron morphology is frequently used to classify cell-types in the mammalian cortex. Apart from the shape of the soma and the axonal projections, morphological classification is largely defined by the dendrites of a neuron and their subcellular compartments, referred to as dendritic spines. The dimensions of a neuron’s dendritic compartment, including its spines, is also a major determinant of the passive and active electrical excitability of dendrites. Furthermore, the dimensions of dendritic branches and spines change during postnatal development and, possibly, following some types of neuronal activity patterns, changes depending on the activity of a neuron. Due to their small size, accurate quantitation of spine number and structure is difficult to achieve (Larkman, J Comp Neurol 306:332, 1991). Here we follow an analysis approach using high-resolution EM techniques. Serial block-face scanning electron microscopy (SBFSEM) enables automated imaging of large specimen volumes at high resolution. The large data sets generated by this technique make manual reconstruction of neuronal structure laborious. Here we present NeuroStruct, a reconstruction environment developed for fast and automated analysis of large SBFSEM data sets containing individual stained neurons using optimized algorithms for CPU and GPU hardware. NeuroStruct is based on 3D operators and integrates image information from image stacks of individual neurons filled with biocytin and stained with osmium tetroxide. The focus of the presented work is the reconstruction of dendritic branches with detailed representation of spines. NeuroStruct delivers both a 3D surface model of the reconstructed structures and a 1D geometrical model corresponding to the skeleton of the reconstructed structures. Both representations are a prerequisite for analysis of morphological characteristics and simulation signalling within a neuron that capture the influence of spines

Measuring memetic algorithm performance on image fingerprints dataset

Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four partial tests and at the last of work we measure all computation time

An investigation into structural plasticity in peripheral taste neurons associated with taste cell turnover.

The continual replacement of taste cells creates interesting questions regarding how the innervating neurons are impacted during this process. Here we ask how innervation within taste buds is affected when taste cell entry is inhibited and reestablished. Inhibition of sonic hedgehog signaling (Shh) is thought to inhibit taste cell turnover. Consistently, fewer new cells were added to individual taste buds after treatment with a Shh-inhibitor compared to vehicle treatment, and taste bud volume decreased after 16 days of treatment. We next examined how taste nerve fiber extension into the gustatory epithelium is affected by preventing taste cell turnover. Ten days of Shh inhibitor caused a loss of innervation in the epithelium of fungiform papillae. Seven days of recovery does not restore fibers within the epithelium, suggesting that recovery of normal branch morphology requires more than 7 days of cell turnover. These results provide evidence for the hypothesis that normal branch morphology within the taste bud is supported by taste cell turnover and provide a pharmacologic manipulation for controlling taste cell entry into taste buds. The perception of taste relies on new taste bud cells integrating with existing neural circuitry, yet how these new cells connect with a taste ganglion neuron is unknown. Do taste ganglion neurons remodel to accommodate taste bud cell renewal? If so, how much of the taste axon structure is fixed and how much remodels? Here we measured the motility and branching of individual taste arbors (the portion of the axon innervating taste buds) over time with two-photon in vivo microscopy. Terminal branches of taste arbors continuously and rapidly remodel within the taste bud. This remodeling is faster than predicted by taste bud cell renewal, with terminal branches added and lost concurrently. Surprisingly, ablating new taste cells with chemotherapeutic agents revealed that remodeling of the terminal branches of taste arbors does not rely of the renewal of taste bud cells. Although the arbor structure remodeling was fast and intrinsically controlled, no new arbors were added, and few were lost over 100 days. Taste ganglion neurons maintain a stable number of nerve arbors that are each capable of high-speed remodeling. Arbor structural plasticity would permit arbors to locate new taste bud cells, while stability of arbor number could support constancy in the degree of connectivity and function for each neuron over time

Use of Serial Block Face-Scanning Electron Microscopy to Study the Ultrastructure of Vertebrate and Invertebrate Biology

PhD ThesisThe development of Serial Block Face Scanning Electron Microscopy (SBF-SEM) allows for acquisition of serially sectioned, imaged data of ultrastructure at high resolution. In this project, optimisation of both SBF-SEM methodology and 3-D image segmentation analysis was applied to the ultrastructural examination of two types of biological tissues, each requiring a different experimental approach. The first project was a connectomic based study, to determine the relationship between the neurons that synapse upon the Lobula Giant Movement Detector 2 (LGMD 2) neuron, within the optic lobe of the locust. A substantial portion of the LGMD 2 neuron was reconstructed along with the afferent neurons, enabling the discovery of retinotopic mapping from the photoreceptors of the eye onto the LGMD 2 neuron. A sub-class of afferent neurons was also found, most likely vital in the process of signal integration across the large LGMD 2 neuron. For the second project, two types of skeletal muscle (psoas and soleus) obtained from fetal and adult guinea pigs were analysed to assess tissue-specific changes in mitochondrial morphology with muscle maturation. Distinct mitochondrial shapes were found across both muscles and age groups and a classification system was developed. It was found that, in both muscles, by late fetal gestation the mitochondrial network is well developed and akin to that found in the adult. Quantitative and qualitative differences in mitochondria morphology and complexity were found between the two muscles in the adult group. These differences are likely to be related to functional specialisation. All data collected during the experiments have also been made available online on Zenodo, roughly 240GB, which can be used for further studies. Overall SBF-SEM was proven to be a robust method of gaining new insights into the ultrastructure in both models and has wide ranging capabilities for a variety of experimental objectives