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5 Janelia Publications

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    Hess LabFetter LabFlyEM
    02/16/15 | Ultrastructurally smooth thick partitioning and volume stitching for large-scale connectomics.
    Hayworth KJ, Xu CS, Lu Z, Knott GW, Fetter RD, Tapia JC, Lichtman JW, Hess HF
    Nature Methods. 2015 Feb 16;12(4):319-22. doi: 10.1038/nmeth.3292

    Focused-ion-beam scanning electron microscopy (FIB-SEM) has become an essential tool for studying neural tissue at resolutions below 10 nm × 10 nm × 10 nm, producing data sets optimized for automatic connectome tracing. We present a technical advance, ultrathick sectioning, which reliably subdivides embedded tissue samples into chunks (20 μm thick) optimally sized and mounted for efficient, parallel FIB-SEM imaging. These chunks are imaged separately and then 'volume stitched' back together, producing a final three-dimensional data set suitable for connectome tracing.

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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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    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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    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
    Hess LabFetter Lab
    03/03/09 | Interferometric fluorescent super-resolution microscopy resolves 3D cellular ultrastructure.
    Shtengel G, Galbraith JA, Galbraith CG, Lippincott-Schwartz J, Gillette JM, Manley S, Sougrat R, Waterman CM, Kanchanawong P, Davidson MW, Fetter RD, Hess HF
    Proceedings of the National Academy of Sciences of the United States of America. 2009 Mar 3;106:3125-30. doi: 10.1073/pnas.0813131106

    Understanding molecular-scale architecture of cells requires determination of 3D locations of specific proteins with accuracy matching their nanometer-length scale. Existing electron and light microscopy techniques are limited either in molecular specificity or resolution. Here, we introduce interferometric photoactivated localization microscopy (iPALM), the combination of photoactivated localization microscopy with single-photon, simultaneous multiphase interferometry that provides sub-20-nm 3D protein localization with optimal molecular specificity. We demonstrate measurement of the 25-nm microtubule diameter, resolve the dorsal and ventral plasma membranes, and visualize the arrangement of integrin receptors within endoplasmic reticulum and adhesion complexes, 3D protein organization previously resolved only by electron microscopy. iPALM thus closes the gap between electron tomography and light microscopy, enabling both molecular specification and resolution of cellular nanoarchitecture.

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