Filter
Associated Lab
- Aguilera Castrejon Lab (1) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (47) Apply Ahrens Lab filter
- Aso Lab (39) Apply Aso Lab filter
- Baker Lab (19) Apply Baker Lab filter
- Betzig Lab (99) Apply Betzig Lab filter
- Beyene Lab (8) Apply Beyene Lab filter
- Bock Lab (14) Apply Bock Lab filter
- Branson Lab (45) Apply Branson Lab filter
- Card Lab (34) Apply Card Lab filter
- Cardona Lab (44) Apply Cardona Lab filter
- Chklovskii Lab (10) Apply Chklovskii Lab filter
- Clapham Lab (12) Apply Clapham Lab filter
- Cui Lab (19) Apply Cui Lab filter
- Darshan Lab (8) Apply Darshan Lab filter
- Dickson Lab (32) Apply Dickson Lab filter
- Druckmann Lab (21) Apply Druckmann Lab filter
- Dudman Lab (34) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (4) Apply Egnor Lab filter
- Espinosa Medina Lab (12) Apply Espinosa Medina Lab filter
- Feliciano Lab (6) Apply Feliciano Lab filter
- Fetter Lab (31) Apply Fetter Lab filter
- Fitzgerald Lab (15) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (35) Apply Funke Lab filter
- Gonen Lab (59) Apply Gonen Lab filter
- Grigorieff Lab (34) Apply Grigorieff Lab filter
- Harris Lab (49) Apply Harris Lab filter
- Heberlein Lab (13) Apply Heberlein Lab filter
- Hermundstad Lab (19) Apply Hermundstad Lab filter
- Hess Lab (70) Apply Hess Lab filter
- Ilanges Lab (1) Apply Ilanges Lab filter
- Jayaraman Lab (40) Apply Jayaraman Lab filter
- Ji Lab (33) Apply Ji Lab filter
- Johnson Lab (1) Apply Johnson Lab filter
- Karpova Lab (13) Apply Karpova Lab filter
- Keleman Lab (8) Apply Keleman Lab filter
- Keller Lab (61) Apply Keller Lab filter
- Koay Lab (1) Apply Koay Lab filter
- Lavis Lab (127) Apply Lavis Lab filter
- Lee (Albert) Lab (29) Apply Lee (Albert) Lab filter
- Leonardo Lab (19) Apply Leonardo Lab filter
- Li Lab (3) Apply Li Lab filter
- Lippincott-Schwartz Lab (89) Apply Lippincott-Schwartz Lab filter
- Liu (Zhe) Lab (56) Apply Liu (Zhe) Lab filter
- Looger Lab (136) Apply Looger Lab filter
- Magee Lab (31) Apply Magee Lab filter
- Menon Lab (12) Apply Menon Lab filter
- Murphy Lab (6) Apply Murphy Lab filter
- O'Shea Lab (5) Apply O'Shea Lab filter
- Otopalik Lab (1) Apply Otopalik Lab filter
- Pachitariu Lab (30) Apply Pachitariu Lab filter
- Pastalkova Lab (5) Apply Pastalkova Lab filter
- Pavlopoulos Lab (7) Apply Pavlopoulos Lab filter
- Pedram Lab (3) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (43) Apply Reiser Lab filter
- Riddiford Lab (20) Apply Riddiford Lab filter
- Romani Lab (29) Apply Romani Lab filter
- Rubin Lab (101) Apply Rubin Lab filter
- Saalfeld Lab (44) Apply Saalfeld Lab filter
- Satou Lab (1) Apply Satou Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (46) Apply Schreiter Lab filter
- Shroff Lab (24) Apply Shroff Lab filter
- Simpson Lab (18) Apply Simpson Lab filter
- Singer Lab (37) Apply Singer Lab filter
- Spruston Lab (55) Apply Spruston Lab filter
- Stern Lab (70) Apply Stern Lab filter
- Sternson Lab (47) Apply Sternson Lab filter
- Stringer Lab (27) Apply Stringer Lab filter
- Svoboda Lab (131) Apply Svoboda Lab filter
- Tebo Lab (7) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (14) Apply Tillberg Lab filter
- Tjian Lab (17) Apply Tjian Lab filter
- Truman Lab (58) Apply Truman Lab filter
- Turaga Lab (34) Apply Turaga Lab filter
- Turner Lab (25) Apply Turner Lab filter
- Vale Lab (7) Apply Vale Lab filter
- Voigts Lab (2) Apply Voigts Lab filter
- Wang (Meng) Lab (11) Apply Wang (Meng) Lab filter
- Wang (Shaohe) Lab (5) Apply Wang (Shaohe) Lab filter
- Wu Lab (8) Apply Wu Lab filter
- Zlatic Lab (26) Apply Zlatic Lab filter
- Zuker Lab (5) Apply Zuker Lab filter
Associated Project Team
- CellMap (8) Apply CellMap filter
- COSEM (3) Apply COSEM filter
- FIB-SEM Technology (1) Apply FIB-SEM Technology filter
- Fly Descending Interneuron (10) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (14) Apply Fly Functional Connectome filter
- Fly Olympiad (5) Apply Fly Olympiad filter
- FlyEM (51) Apply FlyEM filter
- FlyLight (46) Apply FlyLight filter
- GENIE (40) Apply GENIE filter
- Integrative Imaging (1) Apply Integrative Imaging filter
- Larval Olympiad (2) Apply Larval Olympiad filter
- MouseLight (16) Apply MouseLight filter
- NeuroSeq (1) Apply NeuroSeq filter
- ThalamoSeq (1) Apply ThalamoSeq filter
- Tool Translation Team (T3) (26) Apply Tool Translation Team (T3) filter
- Transcription Imaging (45) Apply Transcription Imaging filter
Publication Date
- 2024 (190) Apply 2024 filter
- 2023 (168) Apply 2023 filter
- 2022 (166) Apply 2022 filter
- 2021 (174) Apply 2021 filter
- 2020 (177) Apply 2020 filter
- 2019 (177) Apply 2019 filter
- 2018 (206) Apply 2018 filter
- 2017 (186) Apply 2017 filter
- 2016 (191) Apply 2016 filter
- 2015 (195) Apply 2015 filter
- 2014 (190) Apply 2014 filter
- 2013 (136) Apply 2013 filter
- 2012 (112) Apply 2012 filter
- 2011 (98) Apply 2011 filter
- 2010 (61) Apply 2010 filter
- 2009 (56) Apply 2009 filter
- 2008 (40) Apply 2008 filter
- 2007 (21) Apply 2007 filter
- 2006 (3) Apply 2006 filter
Type of Publication
- Remove Janelia filter Janelia
2547 Publications
Showing 2111-2120 of 2547 resultsThe most established method of reconstructing neural circuits from animals involves slicing tissue very thin, then taking mosaics of electron microscope (EM) images. To trace neurons across different images and through different sections, these images must be accurately aligned, both with the others in the same section and to the sections above and below. Unfortunately, sectioning and imaging are not ideal processes - some of the problems that make alignment difficult include lens distortion, tissue shrinkage during imaging, tears and folds in the sectioned tissue, and dust and other artifacts. In addition the data sets are large (hundreds of thousands of images) and each image must be aligned with many neighbors, so the process must be automated and reliable. This paper discusses methods of dealing with these problems, with numeric results describing the accuracy of the resulting alignments.
BACKGROUND: The insect brain can be divided into neuropils that are formed by neurites of both local and remote origin. The complexity of the interconnections obscures how these neuropils are established and interconnected through development. The Drosophila central brain develops from a fixed number of neuroblasts (NBs) that deposit neurons in regional clusters. RESULTS: By determining individual NB clones and pursuing their projections into specific neuropils, we unravel the regional development of the brain neural network. Exhaustive clonal analysis revealed 95 stereotyped neuronal lineages with characteristic cell-body locations and neurite trajectories. Most clones show complex projection patterns, but despite the complexity, neighboring clones often coinnervate the same local neuropil or neuropils and further target a restricted set of distant neuropils. CONCLUSIONS: These observations argue for regional clonal development of both neuropils and neuropil connectivity throughout the Drosophila central brain.
View Publication PageRodents move their whiskers to locate objects in space. Here we used psychophysical methods to show that head-fixed mice can localize objects along the axis of a single whisker, the radial dimension, with one-millimeter precision. High-speed videography allowed us to estimate the forces and bending moments at the base of the whisker, which underlie radial distance measurement. Mice judged radial object location based on multiple touches. Both the number of touches (1-17) and the forces exerted by the pole on the whisker (up to 573 μN; typical peak amplitude, 100 μN) varied greatly across trials. We manipulated the bending moment and lateral force pressing the whisker against the sides of the follicle and the axial force pushing the whisker into the follicle by varying the compliance of the object during behavior. The behavioral responses suggest that mice use multiple variables (bending moment, axial force, lateral force) to extract radial object localization. Characterization of whisker mechanics revealed that whisker bending stiffness decreases gradually with distance from the face over five orders of magnitude. As a result, the relative amplitudes of different stress variables change dramatically with radial object distance. Our data suggest that mice use distance-dependent whisker mechanics to estimate radial object location using an algorithm that does not rely on precise control of whisking, is robust to variability in whisker forces, and is independent of object compliance and object movement. More generally, our data imply that mice can measure the amplitudes of forces in the sensory follicles for tactile sensation.
Tularemia is a deadly, febrile disease caused by infection by the gram-negative bacterium, Francisella tularensis. Members of the ubiquitous serine hydrolase protein family are among current targets to treat diverse bacterial infections. Herein we present a structural and functional study of a novel bacterial carboxylesterase (FTT258) from F. tularensis, a homologue of human acyl protein thioesterase (hAPT1). The structure of FTT258 has been determined in multiple forms, and unexpectedly large conformational changes of a peripheral flexible loop occur in the presence of a mechanistic cyclobutanone ligand. The concomitant changes in this hydrophobic loop and the newly exposed hydrophobic substrate binding pocket suggest that the observed structural changes are essential to the biological function and catalytic activity of FTT258. Using diverse substrate libraries, site-directed mutagenesis, and liposome binding assays, we determined the importance of these structural changes to the catalytic activity and membrane binding activity of FTT258. Residues within the newly exposed hydrophobic binding pocket and within the peripheral flexible loop proved essential to the hydrolytic activity of FTT258, indicating that structural rearrangement is required for catalytic activity. Both FTT258 and hAPT1 also showed significant association with liposomes designed to mimic bacterial or human membranes, respectively, even though similar structural rearrangements for hAPT1 have not been reported. The necessity for acyl protein thioesterases to have maximal catalytic activity near the membrane surface suggests that these conformational changes in the protein may dually regulate catalytic activity and membrane association in bacterial and human homologues.
Two clichés of science journalism have now played out around the ENCODE project. ENCODE’s publicity first presented a misleading "all the textbooks are wrong" narrative about noncoding human DNA. Now several critiques of ENCODE’s narrative have been published, and one was so vitriolic that it fueled "undignified academic squabble" stories that focused on tone more than substance. Neither story line does justice to our actual understanding of genomes, to ENCODE’s results, or to the role of big science in biology.
Electron cryomicroscopy, or cryoEM, is an emerging technique for studying the three-dimensional structures of proteins and large macromolecular machines. Electron crystallography is a branch of cryoEM in which structures of proteins can be studied at resolutions that rival those achieved by X-ray crystallography. Electron crystallography employs two-dimensional crystals of a membrane protein embedded within a lipid bilayer. The key to a successful electron crystallographic experiment is the crystallization, or reconstitution, of the protein of interest. This unit describes ways in which protein can be expressed, purified, and reconstituted into well-ordered two-dimensional crystals. A protocol is also provided for negative stain electron microscopy as a tool for screening crystallization trials. When large and well-ordered crystals are obtained, the structures of both protein and its surrounding membrane can be determined to atomic resolution.
Object detection and classification are key tasks in computer vision that can facilitate high-throughput image analysis of microscopy data. We present a set of local image descriptors for three-dimensional (3D) microscopy datasets inspired by the well-known Haar wavelet framework. We add orientation, illumination and scale information by assuming that the neighborhood surrounding points of interests in the image can be described with ellipsoids, and we increase discriminative power by incorporating edge and shape information into the features. The calculation of the local image descriptors is implemented in a Graphics Processing Unit (GPU) in order to reduce computation time to 1 millisecond per object of interest. We present results for cell division detection in 3D time-lapse fluorescence microscopy with 97.6% accuracy.
Understanding the development of complex multicellular organisms as a function of the underlying cell behavior is one of the most fundamental goals of developmental biology. The ability to quantitatively follow cell dynamics in entire developing embryos is an indispensable step towards such a system-level understanding. In recent years, light-sheet fluorescence microscopy has emerged as a particularly promising strategy for recording the in vivo data required to realize this goal. Using light-sheet fluorescence microscopy, entire complex organisms can be rapidly imaged in three dimensions at sub-cellular resolution, achieving high temporal sampling and excellent signal-to-noise ratio without damaging the living specimen or bleaching fluorescent markers. The resulting datasets allow following individual cells in vertebrate and higher invertebrate embryos over up to several days of development. However, the complexity and size of these multi-terabyte recordings typically preclude comprehensive manual analyses. Thus, new computational approaches are required to automatically segment cell morphologies, accurately track cell identities and systematically analyze cell behavior throughout embryonic development. We review current efforts in light-sheet microscopy and bioimage informatics towards this goal, and argue that comprehensive cell lineage reconstructions are finally within reach for many key model organisms, including fruit fly, zebrafish and mouse.
Objective: While the contribution of α-Synuclein to neurodegeneration in Parkinson’s disease is well accepted, the putative impact of its close homologue, β-Synuclein, is enigmatic. β-Synuclein is widely expressed throughout the central nervous system as is α-Synuclein, but the physiological functions of both proteins remain unknown. Recent findings supported the view that β-Synuclein can act as an ameliorating regulator of α-Synuclein-induced neurotoxicity, having neuroprotective rather than neurodegenerative capabilities, and being non-aggregating due to absence of most part of the aggregation-promoting NAC domain. However, a mutation of β-Synuclein linked to dementia with Lewy bodies rendered the protein neurotoxic in transgenic mice and fibrillation of β-Synuclein has been demonstrated in vitro. Methods / Results: Supporting the hypothesis that β-Synuclein can act as a neurodegeneration-inducing factor we now demonstrate that wild-type β-Synuclein is neurotoxic for cultured primary neurons. Furthermore, β-Synuclein formed proteinase K resistant aggregates in dopaminergic neurons in vivo, leading to pronounced and progressive neurodegeneration in rats. Expression of β-Synuclein caused mitochondrial fragmentation, but this fragmentation did not render mitochondria non-functional in terms of ion handling and respiration even in late stages of neurodegeneration. A comparison of the neurodegenerative effects induced by α-, β-, and γ-Synuclein revealed that β-Synuclein was eventually as neurotoxic as α-Synuclein for nigral dopaminergic neurons, while γ-Synuclein proved to be non-toxic and had very low aggregation propensity. Interpretation: Our results suggest that the role of β-Synuclein as a putative modulator of neuropathology in aggregopathies like Parkinson’s disease and dementia with Lewy bodies needs to be revisited. ANN NEUROL 2013. © 2013 American Neurological Association.
Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural interactions and circuits. At the same time, there exist few general theories or principles that explain brain function. We attribute this disparity, in part, to limitations in current methodologies. Traditional neurophysiological approaches record the activities of one neuron or a few neurons at a time. Neurochemical approaches focus on single neurotransmitters. Yet, there is an increasing realization that neural circuits operate at emergent levels, where the interactions between hundreds or thousands of neurons, utilizing multiple chemical transmitters, generate functional states. Brains function at the nanoscale, so tools to study brains must ultimately operate at this scale, as well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to explore brain function by enabling simultaneous measurement and manipulation of activity of thousands or even millions of neurons. We and others refer to this goal as the Brain Activity Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis tools and in the design and synthesis of nanomaterials have generated optical, electrical, and chemical methods that can readily be adapted for use in neuroscience. These approaches represent exciting areas of technical development and research. Moreover, unique opportunities exist for nanoscientists, nanotechnologists, and other physical scientists and engineers to contribute to tackling the challenging problems involved in understanding the fundamentals of brain function.