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

Cardona, Albert; Saalfeld, Stephan; Preibisch, Stephan; Schmid, Benjamin; Cheng, Anchi; Pulokas, James; Tomančák, Pavel; Hartenstein, Volker

doi:10.1371/journal.pbio.1000502

Cited by 339 publications

(293 citation statements)

References 71 publications

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“…The Drosophila larva is likely to be the next animal, after C. elegans, that offers a complete wiring diagram of an entire nervous system with synaptic resolution (41). Advances in optical neurophysiology may soon make it possible to record the activities of large ensembles of neurons in the Drosophila larva, as has recently been accomplished in C. elegans and zebrafish larva (42)(43)(44)(45).…”

Section: Discussionmentioning

confidence: 99%

Sensory determinants of behavioral dynamics in Drosophila thermotaxis

Klein

Afonso

Vonner

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Complex animal behaviors are built from dynamical relationships between sensory inputs, neuronal activity, and motor outputs in patterns with strategic value. Connecting these patterns illuminates how nervous systems compute behavior. Here, we study Drosophila larva navigation up temperature gradients toward preferred temperatures (positive thermotaxis). By tracking the movements of animals responding to fixed spatial temperature gradients or random temperature fluctuations, we calculate the sensitivity and dynamics of the conversion of thermosensory inputs into motor responses. We discover three thermosensory neurons in each dorsal organ ganglion (DOG) that are required for positive thermotaxis. Random optogenetic stimulation of the DOG thermosensory neurons evokes behavioral patterns that mimic the response to temperature variations. In vivo calcium and voltage imaging reveals that the DOG thermosensory neurons exhibit activity patterns with sensitivity and dynamics matched to the behavioral response. Temporal processing of temperature variations carried out by the DOG thermosensory neurons emerges in distinct motor responses during thermotaxis. N avigation toward environmental conditions that improve survival and fitness is of near-universal importance in motile biological organisms. Quantitative analysis of such animal behaviors to defined sensory inputs is a powerful approach to elucidate how behavior is encoded in underlying neurons and circuits. The advantage of studying navigation in small, optically transparent, genetically modifiable animals like Caenorhabditis elegans (1) or Drosophila larvae (2) is the opportunity to dissect sensory, neuronal, and behavioral dynamics in live animals by using optical neurophysiology and optogenetics throughout the nervous system.The Drosophila melanogaster larva navigates gradients of many sensory cues, including light, temperature, odors, and tastes, but with fewer neurons in its sensory periphery and brain than the adult. Moreover, the simpler body plan and crawling movements of the larva facilitate the precise quantification of behavioral dynamics. Poikilotherms like C. elegans or Drosophila use sensitive thermosensory mechanisms to navigate moderate temperature ranges, thereby enabling them to use their environments to regulate their own body temperatures (3, 4). Here, we study sensory and behavioral dynamics during positive thermotaxis (i.e., cool avoidance) by the Drosophila larva. Tracking the movements of Drosophila exploring temperature, olfactory, or gaseous gradients has shown that their navigation is generated by a sequence of two alternating motor programs: runs involving peristaltic forward movement that are interrupted by turns involving probing side-to-side head sweeps until the initiation of a new run (5-8). Larvae negotiating temperature gradients stochastically transition between runs and turns by strategies that cause runs pointed in favorable directions to be more frequent and longer than runs pointed in unfavorable directions. These transitions b...

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Section: Discussionmentioning

confidence: 99%

Sensory determinants of behavioral dynamics in Drosophila thermotaxis

Klein

Afonso

Vonner

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

149

206

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“…15 and 16). The ability to extract better axonal shapes will aid in the comparison of EM and light microscopy data for neuronal circuit reconstruction at vastly different scales 3,13 .…”

Section: -11)mentioning

confidence: 99%

“…Sections can be imaged as mosaics of overlapping image tiles, either manually or using a motorized stage, allowing the imaging of very large fields of view. In combination, these advantages render serial section microscopy particularly useful for large scale high resolution reconstructions, for example, of dense neuronal tissue, where the method employing Electron Microscopy (EM) recently experienced a renaissance [1][2][3][4][5][6] .…”

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Elastic volume reconstruction from series of ultra-thin microscopy sections

et al. 2012

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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. DOI: https://doi.org/10. 1038/nmeth.2072 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-127852 Accepted Version Originally published at: Saalfeld, Stephan; Fetter, Richard; Cardona, Albert; Tomancak, Pavel (2012). Elastic volume reconstruction from series of ultra-thin microscopy sections. Nature Methods, 9(7):717-720. DOI: https://doi.org/10.1038/nmeth.2072 Elastic Volume Reconstruction from Series of Ultra-thin Microscopy SectionsStephan The downside of the method is that by physically cutting a block into sections the continuity between sections is lost and individual sections are deformed. To recover the imaged volume and extract biologically interesting information such as the reconstruction of neuronal circuits 2,3,5 , sections need to be aligned and distortion must be removed.Alignment can be achieved by maximizing the overlap of similar image content between adjacent sections. However, there are two unknowns that change image content across the section series: specimen's shape and independent section distortion introduced during preparation. Naively warping one section into another would compensate for the shape of the specimen and by that introduce artificial deformation. Our method exploits the fact that the biological specimen's shape typically changes smoothly across sections whereas the independent distortion in each section is random and uncorrelated with neighboring sections. We align all sections not only to their direct neighbors in the series but to all sections in a local neighborhood by modelling sections as two-dimensional elastic sheets that penalize non-rigid deformation ( Fig. 1a and Supplementary Fig. 1).All sections are treated as moving targets in a template-free global alignment. The elastic constraint is implemented as a spring-connected particle system where each section is represented as a triangular spring-mesh ( Fig. 1b and Supplementary Video 1 and Online methods).For each vertex of the spring-mesh, we search for the corresponding location in other sections by pairwise block-matching using normalized cross-correlation (NCC). To that end, we explore all translation vectors in an immediate neighborhood which requires sections to be in approximate alignment (Fig. 1c,d). We estimate this approximate alignment using automatically extracted landmark correspondences from invariant local image features as described previously 7,8 . Originally proposed for robust object recognition under partial occlus...

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“…The dataset was acquired at a resolution of 3×3×30 nm, but as the human-traced segmentation is provided only at the downsampled resolution of 6 × 6 × 30 nm, we used the same downsampled resolution for all of our experiments. In addition, we tested EMISAC on another publicly available dataset, Cardona et al 2010, collecting using a different imaging technique, ssTEM rather than ATUM-SEM, and of Drosophila larva ventral nerve cord rather than mouse cortex, with a resolution of 4 × 4 × 50 nm [7,8].…”

Section: Evaluation On Electron Microscopy Datamentioning

confidence: 99%

Optimization-Based Artifact Correction for Electron Microscopy Image Stacks

Azadi

Maitin-Shepard

Abbeel

2014

Computer Vision – ECCV 2014

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Abstract. Investigations of biological ultrastructure, such as comprehensive mapping of connections within a nervous system, increasingly rely on large, high-resolution electron microscopy (EM) image volumes. However, discontinuities between the registered section images from which these volumes are assembled, due to variations in imaging conditions and section thickness, among other artifacts, impede truly 3-D analysis of these volumes. We propose an optimization procedure, called EMISAC (EM Image Stack Artifact Correction), to correct these discontinuities. EMISAC optimizes the parameters of spatially varying linear transformations of the data in order to minimize the squared norm of the gradient along the section axis, subject to detail-preserving regularization. Assessment on a mouse cortex dataset demonstrates the effectiveness of our approach. Relative to the original data, EMISAC produces a large improvement both in NIQE score, a measure of statistical similarity between orthogonal cross-sections and the original image sections, as well as in accuracy of neurite segmentation, a critical task for this type of data. Compared to a recent independently-developed gradient-domain algorithm, EMISAC achieves significantly better NIQE image quality scores, and equivalent segmentation accuracy; future segmentation algorithms may be able to take advantage of the higher image quality. In addition, on several time-lapse videos, EMISAC significantly reduces lighting artifacts, resulting in greatly improved video quality. A software release is available at http://rll.berkeley.edu/2014_ECCV_ EMISAC.

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

Abstract: A new software package allows for dense electron microscopy reconstructions of neuronal networks in the fruit fly brain, and reveals specific differences in microcircuits between insects and vertebrates.

Cited by 339 publications

References 71 publications

Sensory determinants of behavioral dynamics in Drosophila thermotaxis

Sensory determinants of behavioral dynamics in Drosophila thermotaxis

Elastic volume reconstruction from series of ultra-thin microscopy sections

Optimization-Based Artifact Correction for Electron Microscopy Image Stacks

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