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41 Publications

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    Svoboda LabHarris LabFetter Lab
    11/12/13 | Thalamocortical input onto layer 5 pyramidal neurons measured using quantitative large-scale array tomography.
    Rah J, Bas E, Colonell J, Mishchenko Y, Karsh B, Fetter RD, Myers EW, Chklovskii DB, Svoboda K, Harris TD, Isaac JT
    Frontiers in Neural Circuits. 2013;7:177. doi: 10.3389/fncir.2013.00177

    The subcellular locations of synapses on pyramidal neurons strongly influences dendritic integration and synaptic plasticity. Despite this, there is little quantitative data on spatial distributions of specific types of synaptic input. Here we use array tomography (AT), a high-resolution optical microscopy method, to examine thalamocortical (TC) input onto layer 5 pyramidal neurons. We first verified the ability of AT to identify synapses using parallel electron microscopic analysis of TC synapses in layer 4. We then use large-scale array tomography (LSAT) to measure TC synapse distribution on L5 pyramidal neurons in a 1.00 × 0.83 × 0.21 mm(3) volume of mouse somatosensory cortex. We found that TC synapses primarily target basal dendrites in layer 5, but also make a considerable input to proximal apical dendrites in L4, consistent with previous work. Our analysis further suggests that TC inputs are biased toward certain branches and, within branches, synapses show significant clustering with an excess of TC synapse nearest neighbors within 5-15 μm compared to a random distribution. Thus, we show that AT is a sensitive and quantitative method to map specific types of synaptic input on the dendrites of entire neurons. We anticipate that this technique will be of wide utility for mapping functionally-relevant anatomical connectivity in neural circuits.

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    08/07/13 | A visual motion detection circuit suggested by Drosophila connectomics.
    Takemura S, Bharioke A, Lu Z, Nern A, Vitaladevuni S, Rivlin PK, Katz WT, Olbris DJ, Plaza SM, Winston P, Zhao T, Horne JA, Fetter RD, Takemura S, Blazek K, Chang L, Ogundeyi O, Saunders MA, Shapiro V, Sigmund C, Rubin GM, Scheffer LK, Meinertzhagen IA, Chklovskii DB
    Nature. 2013 Aug 7;500(7461):175–81. doi: doi:10.1038/nature12450

    Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.

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    08/02/13 | Electron microscopy reconstruction of brain structure using sparse representations over learned dictionaries.
    Hu T, Nunez-Iglesias J, Vitaladevuni S, Scheffer L, Xu S, Bolorizadeh M, Hess H, Fetter R, Chklovskii D
    IEEE Transactions on Medical Imaging. 2013 Aug 2;32(12):2179-88. doi: 10.1109/TMI.2013.2276018

    A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.

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    Cardona LabSaalfeld LabFetter Lab
    07/01/12 | Elastic volume reconstruction from series of ultra-thin microscopy sections.
    Saalfeld S, Fetter RD, Cardona A, Tomancak P
    Nature Methods. 2012 Jul;9(7):717-20. doi: 10.1038/nmeth.2072

    Anatomy of large biological specimens is often reconstructed from serially sectioned volumes imaged by high-resolution microscopy. We developed a method to reassemble a continuous volume from such large section series that explicitly minimizes artificial deformation by applying a global elastic constraint. We demonstrate our method on a series of transmission electron microscopy sections covering the entire 558-cell Caenorhabditis elegans embryo and a segment of the Drosophila melanogaster larval ventral nerve cord.

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    Fetter Lab
    12/08/11 | Glial-derived prodegenerative signaling in the Drosophila neuromuscular system.
    Keller LC, Cheng L, Locke CJ, Müller M, Fetter RD, Davis GW
    Neuron. 2011 Dec 8;72(5):760-75. doi: 10.1016/j.neuron.2011.09.031

    We provide evidence for a prodegenerative, glial-derived signaling framework in the Drosophila neuromuscular system that includes caspase and mitochondria-dependent signaling. We demonstrate that Drosophila TNF-α (eiger) is expressed in a subset of peripheral glia, and the TNF-α receptor (TNFR), Wengen, is expressed in motoneurons. NMJ degeneration caused by disruption of the spectrin/ankyrin skeleton is suppressed by an eiger mutation or by eiger knockdown within a subset of peripheral glia. Loss of wengen in motoneurons causes a similar suppression providing evidence for glial-derived prodegenerative TNF-α signaling. Neither JNK nor NFκβ is required for prodegenerative signaling. However, we provide evidence for the involvement of both an initiator and effector caspase, Dronc and Dcp-1, and mitochondrial-dependent signaling. Mutations that deplete the axon and nerve terminal of mitochondria suppress degeneration as do mutations in Drosophila Bcl-2 (debcl), a mitochondria-associated protein, and Apaf-1 (dark), which links mitochondrial signaling with caspase activity in other systems.

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    Fetter Lab
    03/24/11 | Hts/Adducin controls synaptic elaboration and elimination.
    Pielage J, Bulat V, Zuchero JB, Fetter RD, Davis GW
    Neuron. 2011 Mar 24;69(6):1114-31. doi: 10.1016/j.neuron.2011.02.007

    Neural development requires both synapse elaboration and elimination, yet relatively little is known about how these opposing activities are coordinated. Here, we provide evidence Hts/Adducin can serve this function. We show that Drosophila Hts/Adducin is enriched both pre- and postsynaptically at the NMJ. We then demonstrate that presynaptic Hts/Adducin is necessary and sufficient to control two opposing processes associated with synapse remodeling: (1) synapse stabilization as determined by light level and ultrastructural and electrophysiological assays and (2) the elaboration of actin-based, filopodia-like protrusions that drive synaptogenesis and growth. Synapse remodeling is sensitive to Hts/Adducin levels, and we provide evidence that the synaptic localization of Hts/Adducin is controlled via phosphorylation. Mechanistically, Drosophila Hts/Adducin protein has actin-capping activity. We propose that phosphorylation-dependent regulation of Hts/Adducin controls the level, localization, and activity of Hts/Adducin, influencing actin-based synapse elaboration and spectrin-based synapse stabilization. Hts/Adducin may define a mechanism to switch between synapse stability and dynamics.

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    03/15/11 | Subnuclear segregation of genes and core promoter factors in myogenesis. (With commentary)
    Yao J, Fetter RD, Hu P, Betzig E, Tjian R
    Genes & Development. 2011 Mar 15;25(6):569-80. doi: 10.1073/pnas.1100640108

    Recent findings implicate alternate core promoter recognition complexes in regulating cellular differentiation. Here we report a spatial segregation of the alternative core factor TAF3, but not canonical TFIID subunits, away from the nuclear periphery, where the key myogenic gene MyoD is preferentially localized in myoblasts. This segregation is correlated with the differential occupancy of TAF3 versus TFIID at the MyoD promoter. Loss of this segregation by modulating either the intranuclear location of the MyoD gene or TAF3 protein leads to altered TAF3 occupancy at the MyoD promoter. Intriguingly, in differentiated myotubes, the MyoD gene is repositioned to the nuclear interior, where TAF3 resides. The specific high-affinity recognition of H3K4Me3 by the TAF3 PHD (plant homeodomain) finger appears to be required for the sequestration of TAF3 to the nuclear interior. We suggest that intranuclear sequestration of core transcription components and their target genes provides an additional mechanism for promoter selectivity during differentiation.

    Commentary: Jie Yao in Bob Tijan’s lab used a combination of confocal microscopy and dual label PALM in thin sections cut from resin-embedded cells to show that certain core transcription components and their target genes are spatially segregated in myoblasts, but not in differentiated myotubes, suggesting that such spatial segregation may play a role in guiding cellular differentiation.


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    01/01/11 | High resolution segmentation of neuronal tissues from low depth-resolution EM imagery.
    Glasner D, Hu T, Nunez-Iglesias J, Scheffer L, Xu C, Hess H, Fetter R, Chklovskii D, Basri R
    8th International Conference of Energy Minimization Methods in Computer Vision and Pattern Recognition Energy Minimization Methods in Computer Vision and Pattern Recognition. 2011;6819:261-72

    The challenge of recovering the topology of massive neuronal circuits can potentially be met by high throughput Electron Microscopy (EM) imagery. Segmenting a 3-dimensional stack of EM images into the individual neurons is difficult, due to the low depth-resolution in existing high-throughput EM technology, such as serial section Transmission EM (ssTEM). In this paper we propose methods for detecting the high resolution locations of membranes from low depth-resolution images. We approach this problem using both a method that learns a discriminative, over-complete dictionary and a kernel SVM. We test this approach on tomographic sections produced in simulations from high resolution Focused Ion Beam (FIB) images and on low depth-resolution images acquired with ssTEM and evaluate our results by comparing it to manual labeling of this data.

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    Fetter Lab
    08/01/10 | Approaches toward super-resolution fluorescence imaging of mitochondrial proteins using PALM.
    Brown TA, Fetter RD, Tkachuk AN, Clayton DA
    Methods. 2010 Aug;51(4):458-63. doi: 10.1016/j.ymeth.2010.01.001

    Mitochondria are difficult targets for microscopy because of their small size and highly compartmentalized, membranous interior. Super-resolution fluorescence microscopy methods have recently been developed that exceed the historical limits of optical imaging. Here we outline considerations and techniques in preparing to image the relative location of mitochondrial proteins using photoactivated localization microscopy (PALM). PALM and similar methods have the capacity to dramatically increase our ability to image proteins within mitochondria, and to expand our knowledge of the location of macromolecules beyond the current limits of immunoEM.

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    01/01/10 | Increasing depth resolution of electron microscopy of neural circuits using sparse tomographic reconstruction.
    Veeraraghavan A, Genkin AV, Vitaladevuni S, Scheffer L, Xu C, Hess H, Fetter R, Cantoni M, Knott G, Chklovskii DB
    Computer Vision and Pattern Recognition (CVPR). 2010:1767-74. doi: 10.1109/CVPR.2010.5539846