An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy

University of Texas at Austin, United States of America
PLoS Biology (Impact Factor: 9.34). 10/2010; 8(10). DOI: 10.1371/journal.pbio.1000502
Source: PubMed


The analysis of microcircuitry (the connectivity at the level of individual neuronal processes and synapses), which is indispensable for our understanding of brain function, is based on serial transmission electron microscopy (TEM) or one of its modern variants. Due to technical limitations, most previous studies that used serial TEM recorded relatively small stacks of individual neurons. As a result, our knowledge of microcircuitry in any nervous system is very limited. We applied the software package TrakEM2 to reconstruct neuronal microcircuitry from TEM sections of a small brain, the early larval brain of Drosophila melanogaster. TrakEM2 enables us to embed the analysis of the TEM image volumes at the microcircuit level into a light microscopically derived neuro-anatomical framework, by registering confocal stacks containing sparsely labeled neural structures with the TEM image volume. We imaged two sets of serial TEM sections of the Drosophila first instar larval brain neuropile and one ventral nerve cord segment, and here report our first results pertaining to Drosophila brain microcircuitry. Terminal neurites fall into a small number of generic classes termed globular, varicose, axiform, and dendritiform. Globular and varicose neurites have large diameter segments that carry almost exclusively presynaptic sites. Dendritiform neurites are thin, highly branched processes that are almost exclusively postsynaptic. Due to the high branching density of dendritiform fibers and the fact that synapses are polyadic, neurites are highly interconnected even within small neuropile volumes. We describe the network motifs most frequently encountered in the Drosophila neuropile. Our study introduces an approach towards a comprehensive anatomical reconstruction of neuronal microcircuitry and delivers microcircuitry comparisons between vertebrate and insect neuropile.

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    • "It recovers the crisp image of these structures and facilitates recognition of neural structures. The experiments on Drosophila first instar larva ventral nerve cord (VNC) dataset Cardona and et al. (2010) demonstrate significant improvement over the baselines. "
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    ABSTRACT: In biological imaging the data is often represented by a sequence of anisotropic frames — the resolution in one dimension is significantly lower than in the other dimensions. E.g. in electron microscopy it arises from the thickness of a scanned section. This leads to blurred images and raises problems in tasks like neuronal image segmentation. We present the details and additional evaluation of an approach originally introduced in Laptev et al. (2014) called SuperSlicing to decompose the observed frame into a sequence of plausible hidden sub-frames. Based on sub-frame decomposition by SuperSlicing we propose a novel automated method to perform neuronal structure segmentation. We test our approach on a popular connectomics benchmark, where SuperSlicing preserves topological structures significantly better than other algorithms. We also generalize the approach for video anisotropicity that comes from the long exposure time and show that our method outperforms baseline methods on a reconstruction of low frame rate videos of natural scenes.
    Neural Connectomics Workshop, ECML-PKDD 2014; 09/2014
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    • "One promising approach is (three-dimensional) reconstruction from serial section transmission electron microscopy (TEM), which is nowadays a well-established way of analyzing circuitry of neural networks [1–3]. However, several hundreds of sections or even more have to be cut without any loss of sections, inspected and photographed with the TEM, resulting in an enormous data volume, which is followed by a complex elastic alignment to compensate inevitable image distortions using an elastic alignment program (for example, TrakEM2 [4,5]). Hence, the main criterion in selecting a suitable subject for such a study is a small size. "
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    ABSTRACT: The research field of connectomics arose just recently with the development of new three-dimensional-electron microscopy (EM) techniques and increasing computing power. So far, only a few model species (for example, mouse, the nematode Caenorhabditis elegans, and the fruit fly Drosophila melanogaster) have been studied using this approach. Here, we present a first attempt to expand this circle to include pycnogonids, which hold a key position for the understanding of arthropod evolution. The visual neuropils in Achelia langi are studied using a focused ion beam-scanning electron microscope (FIB-SEM) crossbeam-workstation, and a three-dimensional serial reconstruction of the connectome is presented. The two eyes of each hemisphere of the sea spider’s eye tubercle are connected to a first and a second visual neuropil. The first visual neuropil is subdivided in two hemineuropils, each responsible for one eye and stratified into three layers. Six different neuron types postsynaptic to the retinula (R-cells) axons are characterized by their morphology: five types of descending unipolar neurons and one type of ascending neurons. These cell types are also identified by Golgi impregnations. Mapping of all identifiable chemical synapses indicates that the descending unipolar neurons are postsynaptic to the R-cells and, hence, are second-order neurons. The ascending neurons are predominantly presynaptic and sometimes postsynaptic to the R-cells and may play a feedback role. Comparing these results with the compound eye visual system of crustaceans and insects – the only arthropod visual system studied so far in such detail – we found striking similarities in the morphology and synaptic organization of the different neuron types. Hence, the visual system of pycnogonids shows features of both chelicerate median and mandibulate lateral eyes.
    BMC Biology 08/2014; 12(1):59. DOI:10.1186/s12915-014-0059-3 · 7.98 Impact Factor
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    • "Various methods have been proposed (and implemented) to go from images to neuronal morphology and connectivity. Cardona et al. (2010) used manual tracing of the midline of neuronal processes, along with manual annotation of synapses, to analyze a neuronal circuit. Helmstaedter et al. (2011) refined this approach by having multiple individuals trace the same neuron, thus allowing them to estimate the error rate of their traces. "
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    ABSTRACT: The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.
    Frontiers in Neuroinformatics 04/2014; 8:34. DOI:10.3389/fninf.2014.00034 · 3.26 Impact Factor
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