Filter
Associated Lab
- Aguilera Castrejon Lab (1) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (46) 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 (5) 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 (48) Apply Harris Lab filter
- Heberlein Lab (13) Apply Heberlein Lab filter
- Hermundstad Lab (18) Apply Hermundstad Lab filter
- Hess Lab (69) 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 (60) Apply Keller Lab filter
- Koay Lab (1) Apply Koay Lab filter
- Lavis Lab (126) 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 (45) 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 (69) Apply Stern Lab filter
- Sternson Lab (47) Apply Sternson Lab filter
- Stringer Lab (26) 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 (24) Apply Turner Lab filter
- Vale Lab (6) Apply Vale Lab filter
- Voigts Lab (2) Apply Voigts Lab filter
- Wang (Meng) Lab (10) 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 (6) Apply CellMap filter
- COSEM (3) Apply COSEM 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) (24) Apply Tool Translation Team (T3) filter
- Transcription Imaging (45) Apply Transcription Imaging filter
Associated Support Team
- Project Pipeline Support (1) Apply Project Pipeline Support filter
- Anatomy and Histology (18) Apply Anatomy and Histology filter
- Cryo-Electron Microscopy (33) Apply Cryo-Electron Microscopy filter
- Electron Microscopy (13) Apply Electron Microscopy filter
- Gene Targeting and Transgenics (11) Apply Gene Targeting and Transgenics filter
- Integrative Imaging (13) Apply Integrative Imaging filter
- Invertebrate Shared Resource (39) Apply Invertebrate Shared Resource filter
- Janelia Experimental Technology (35) Apply Janelia Experimental Technology filter
- Management Team (1) Apply Management Team filter
- Molecular Genomics (15) Apply Molecular Genomics filter
- Primary & iPS Cell Culture (13) Apply Primary & iPS Cell Culture filter
- Project Technical Resources (37) Apply Project Technical Resources filter
- Quantitative Genomics (19) Apply Quantitative Genomics filter
- Scientific Computing Software (63) Apply Scientific Computing Software filter
- Scientific Computing Systems (6) Apply Scientific Computing Systems filter
- Viral Tools (14) Apply Viral Tools filter
- Vivarium (6) Apply Vivarium filter
Publication Date
- 2024 (170) Apply 2024 filter
- 2023 (170) 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
2529 Janelia Publications
Showing 861-870 of 2529 resultsThere is considerable potential for X-ray free electron lasers (XFELs) to enable determination of macromolecular crystal structures that are difficult to solve using current synchrotron sources. Prior XFEL studies often involved the collection of thousands to millions of diffraction images, in part due to limitations of data processing methods. We implemented a data processing system based on classical post-refinement techniques, adapted to specific properties of XFEL diffraction data. When applied to XFEL data from three different proteins collected using various sample delivery systems and XFEL beam parameters, our method improved the quality of the diffraction data as well as the resulting refined atomic models and electron density maps. Moreover, the number of observations for a reflection necessary to assemble an accurate data set could be reduced to a few observations. These developments will help expand the applicability of XFEL crystallography to challenging biological systems, including cases where sample is limited.
The role of cerebellum in controlling eye movements is well established, but its contribution to more complex forms of visual behavior has remained elusive. To study cerebellar activity during visual attention we recorded extracellular activity of dentate nucleus (DN) neurons in two non-human primates (NHPs). NHPs were trained to read the direction indicated by a peripheral visual stimulus while maintaining fixation at the center, and report the direction of the cue by performing a saccadic eye movement into the same direction following a delay. We found that single unit DN neurons modulated spiking activity over the entire time-course of the task, and that their activity often bridged temporally separated intra-trial events, yet in a heterogeneous manner. To better understand the heterogeneous relationship between task structure, behavioral performance and neural dynamics, we constructed a behavioral, an encoding and a decoding model. Both NHPs showed different behavioral strategies, which influenced the performance. Activity of the DN neurons reflected the unique strategies, with the direction of the visual stimulus frequently being encoded long before an upcoming saccade. Retrograde labeling of the recording location indicated that these neurons receive predominantly inputs from Purkinje cells in the lateral cerebellum as well as neurons of the principal olive and medial pons, all regions known to connect with neurons in the prefrontal cortex contributing to planning of saccades. Together, our results highlight that DN neurons can dynamically modulate their activity during a visual attention task, comprising not only sensorimotor but also cognitive attentional components.
Upon inflammation, leukocytes rapidly transmigrate across the endothelium to enter the inflamed tissue. Evidence accumulates that leukocytes use preferred exit sites, though it is not yet clear how these hotspots in the endothelium are defined and how they are recognized by the leukocyte. Using lattice light sheet microscopy, we discovered that leukocytes prefer endothelial membrane protrusions at cell junctions for transmigration. Phenotypically, these junctional membrane protrusions are present in an asymmetric manner, meaning that one endothelial cell shows the protrusion and the adjacent one does not. Consequently, leukocytes cross the junction by migrating underneath the protruding endothelial cell. These protrusions depend on Rac1 activity and by using a photo-activatable Rac1 probe, we could artificially generate local exit-sites for leukocytes. Overall, we have discovered a new mechanism that uses local induced junctional membrane protrusions to facilitate/steer the leukocyte escape/exit from inflamed vessel walls.
Upon inflammation, leukocytes rapidly transmigrate across the endothelium to enter the inflamed tissue. Evidence accumulates that leukocytes use preferred exit sites, though it is not yet clear how these hotspots in the endothelium are defined and how they are recognized by the leukocyte. Using lattice light sheet microscopy, we discovered that leukocytes prefer endothelial membrane protrusions at cell junctions for transmigration. Phenotypically, these junctional membrane protrusions are present in an asymmetric manner, meaning that one endothelial cell shows the protrusion and the adjacent one does not. Consequently, leukocytes cross the junction by migrating underneath the protruding endothelial cell. These protrusions depend on Rac1 activity and by using a photo-activatable Rac1 probe, we could artificially generate local exit-sites for leukocytes. Overall, we have discovered a new mechanism that uses local induced junctional membrane protrusions to facilitate/steer the leukocyte escape/exit from inflamed vessel walls.
The development and maintenance of tissues requires collective cell movement, during which neighbouring cells coordinate the polarity of their migration machineries. Here, we ask how polarity signals are transmitted from one cell to another across symmetrical cadherin junctions, during collective migration. We demonstrate that collectively migrating endothelial cells have polarized VE-cadherin-rich membrane protrusions, ‘cadherin fingers’, which leading cells extend from their rear and follower cells engulf at their front, thereby generating opposite membrane curvatures and asymmetric recruitment of curvature-sensing proteins. In follower cells, engulfment of cadherin fingers occurs along with the formation of a lamellipodia-like zone with low actomyosin contractility, and requires VE-cadherin/catenin complexes and Arp2/3-driven actin polymerization. Lateral accumulation of cadherin fingers in follower cells precedes turning, and increased actomyosin contractility can initiate cadherin finger extension as well as engulfment by a neighbouring cell, to promote follower behaviour. We propose that cadherin fingers serve as guidance cues that direct collective cell migration.
Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) can automatically generate 3D images with superior z-axis resolution, yielding data that needs minimal image registration and related post-processing. Obstacles blocking wider adoption of FIB-SEM include slow imaging speed and lack of long-term system stability, which caps the maximum possible acquisition volume. Here we present techniques that accelerate image acquisition while greatly improving FIB-SEM reliability, allowing the system to operate for months and generating continuously imaged volumes > 10(6) µm(3). These volumes are large enough for connectomics, where the excellent z resolution can help in tracing of small neuronal processes and accelerate the tedious and time-consuming human proofreading effort. Even higher resolution can be achieved on smaller volumes. We present example data sets from mammalian neural tissue, Drosophila brain, and Chlamydomonas reinhardtii to illustrate the power of this novel high-resolution technique to address questions in both connectomics and cell biology.
Gene targeting is an incredibly valuable technique. Sometimes however, it can also be extremely challenging for various intrinsic reasons (e.g. low target accessibility or nature/extent of gene modification). To bypass these barriers, we designed a transgene-based system in Drosophila that increases the number of independent gene targeting events while at the same time enriching for correctly targeted progeny. Unfortunately, with particularly challenging gene targeting experiments, our original design yielded numerous false positives. Here we deliver a much-improved technique named Enhanced Golic+ (E-Golic+). E-Golic+ incorporates genetic modifications to tighten lethality-based selection while simultaneously boosting efficiency. With E-Golic+, we easily achieve previously unattainable gene targeting. Additionally, we built an E-Golic+ based, high-efficiency genetic pipeline for transgene swapping. We demonstrate its utility by transforming GAL4 enhancer-trap lines into tissue-specific Cas9-expressing lines. Given the superior efficiency, specificity and scalability, E-Golic+ promises to expedite development of additional sophisticated genetic/genomic tools in .
Gene translation depends on accurate decoding of mRNA, the structural mechanism of which remains poorly understood. Ribosomes decode mRNA codons by selecting cognate aminoacyl-tRNAs delivered by elongation factor Tu (EF-Tu). Here we present high-resolution structural ensembles of ribosomes with cognate or near-cognate aminoacyl-tRNAs delivered by EF-Tu. Both cognate and near-cognate tRNA anticodons explore the aminoacyl-tRNA-binding site (A site) of an open 30S subunit, while inactive EF-Tu is separated from the 50S subunit. A transient conformation of decoding-centre nucleotide G530 stabilizes the cognate codon-anticodon helix, initiating step-wise 'latching' of the decoding centre. The resulting closure of the 30S subunit docks EF-Tu at the sarcin-ricin loop of the 50S subunit, activating EF-Tu for GTP hydrolysis and enabling accommodation of the aminoacyl-tRNA. By contrast, near-cognate complexes fail to induce the G530 latch, thus favouring open 30S pre-accommodation intermediates with inactive EF-Tu. This work reveals long-sought structural differences between the pre-accommodation of cognate and near-cognate tRNAs that elucidate the mechanism of accurate decoding.
Internal ribosome entry sites (IRESs) mediate cap-independent translation of viral mRNAs. Using electron cryo-microscopy of a single specimen, we present five ribosome structures formed with the Taura syndrome virus IRES and translocase eEF2•GTP bound with sordarin. The structures suggest a trajectory of IRES translocation, required for translation initiation, and provide an unprecedented view of eEF2 dynamics. The IRES rearranges from extended to bent to extended conformations. This inchworm-like movement is coupled with ribosomal inter-subunit rotation and 40S head swivel. eEF2, attached to the 60S subunit, slides along the rotating 40S subunit to enter the A site. Its diphthamide-bearing tip at domain IV separates the tRNA-mRNA-like pseudoknot I (PKI) of the IRES from the decoding center. This unlocks 40S domains, facilitating head swivel and biasing IRES translocation via hitherto-elusive intermediates with PKI captured between the A and P sites. The structures suggest missing links in our understanding of tRNA translocation.
To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that were drawn from three dynamically switching distributions, representing different environmental contexts. We derive predictions for an ideal Bayesian observer that leverages knowledge about the statistical structure of the task to maximize decision accuracy, including knowledge about the dynamics of the environment. We show that its decisions are biased by the dynamically changing task context. The magnitude of this decision bias depends on the observer's continually evolving belief about the current context. The model therefore not only predicts that decision bias will grow as the context is indicated more reliably, but also as the stability of the environment increases, and as the number of trials since the last context switch grows. Analysis of human choice data validates all three predictions, suggesting that the brain leverages knowledge of the statistical structure of environmental change when interpreting ambiguous sensory signals.