Filter
Associated Lab
- Aguilera Castrejon Lab (2) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (57) Apply Ahrens Lab filter
- Aso Lab (42) Apply Aso Lab filter
- Baker Lab (19) Apply Baker Lab filter
- Betzig Lab (103) Apply Betzig Lab filter
- Beyene Lab (9) Apply Beyene Lab filter
- Bock Lab (14) Apply Bock Lab filter
- Branson Lab (51) Apply Branson Lab filter
- Card Lab (37) Apply Card Lab filter
- Cardona Lab (45) Apply Cardona Lab filter
- Chklovskii Lab (10) Apply Chklovskii Lab filter
- Clapham Lab (14) 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 (40) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (4) Apply Egnor Lab filter
- Espinosa Medina Lab (17) Apply Espinosa Medina Lab filter
- Feliciano Lab (10) Apply Feliciano Lab filter
- Fetter Lab (31) Apply Fetter Lab filter
- FIB-SEM Technology (1) Apply FIB-SEM Technology filter
- Fitzgerald Lab (16) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (41) Apply Funke Lab filter
- Gonen Lab (59) Apply Gonen Lab filter
- Grigorieff Lab (34) Apply Grigorieff Lab filter
- Harris Lab (54) Apply Harris Lab filter
- Heberlein Lab (13) Apply Heberlein Lab filter
- Hermundstad Lab (25) Apply Hermundstad Lab filter
- Hess Lab (76) Apply Hess Lab filter
- Ilanges Lab (2) Apply Ilanges Lab filter
- Jayaraman Lab (43) 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 (2) Apply Koay Lab filter
- Lavis Lab (142) Apply Lavis Lab filter
- Lee (Albert) Lab (29) Apply Lee (Albert) Lab filter
- Leonardo Lab (19) Apply Leonardo Lab filter
- Li Lab (6) Apply Li Lab filter
- Lippincott-Schwartz Lab (106) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (2) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (59) Apply Liu (Zhe) Lab filter
- Looger Lab (137) 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 (6) Apply O'Shea Lab filter
- Otopalik Lab (1) Apply Otopalik Lab filter
- Pachitariu Lab (37) Apply Pachitariu Lab filter
- Pastalkova Lab (5) Apply Pastalkova Lab filter
- Pavlopoulos Lab (7) Apply Pavlopoulos Lab filter
- Pedram Lab (4) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (47) Apply Reiser Lab filter
- Riddiford Lab (20) Apply Riddiford Lab filter
- Romani Lab (36) Apply Romani Lab filter
- Rubin Lab (109) Apply Rubin Lab filter
- Saalfeld Lab (47) Apply Saalfeld Lab filter
- Satou Lab (1) Apply Satou Lab filter
- Scheffer Lab (38) Apply Scheffer Lab filter
- Schreiter Lab (51) Apply Schreiter Lab filter
- Sgro Lab (1) Apply Sgro Lab filter
- Shroff Lab (31) Apply Shroff Lab filter
- Simpson Lab (18) Apply Simpson Lab filter
- Singer Lab (37) Apply Singer Lab filter
- Spruston Lab (60) Apply Spruston Lab filter
- Stern Lab (75) Apply Stern Lab filter
- Sternson Lab (47) Apply Sternson Lab filter
- Stringer Lab (36) Apply Stringer Lab filter
- Svoboda Lab (131) Apply Svoboda Lab filter
- Tebo Lab (11) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (18) Apply Tillberg Lab filter
- Tjian Lab (17) Apply Tjian Lab filter
- Truman Lab (58) Apply Truman Lab filter
- Turaga Lab (41) Apply Turaga Lab filter
- Turner Lab (28) Apply Turner Lab filter
- Vale Lab (8) Apply Vale Lab filter
- Voigts Lab (3) Apply Voigts Lab filter
- Wang (Meng) Lab (25) Apply Wang (Meng) Lab filter
- Wang (Shaohe) Lab (6) 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 (12) Apply CellMap filter
- COSEM (3) Apply COSEM filter
- FIB-SEM Technology (5) Apply FIB-SEM Technology filter
- Fly Descending Interneuron (12) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (14) Apply Fly Functional Connectome filter
- Fly Olympiad (5) Apply Fly Olympiad filter
- FlyEM (56) Apply FlyEM filter
- FlyLight (50) Apply FlyLight filter
- GENIE (47) Apply GENIE filter
- Integrative Imaging (6) Apply Integrative Imaging filter
- Larval Olympiad (2) Apply Larval Olympiad filter
- MouseLight (18) Apply MouseLight filter
- NeuroSeq (1) Apply NeuroSeq filter
- ThalamoSeq (1) Apply ThalamoSeq filter
- Tool Translation Team (T3) (28) Apply Tool Translation Team (T3) filter
- Transcription Imaging (45) Apply Transcription Imaging filter
Associated Support Team
- Project Pipeline Support (5) Apply Project Pipeline Support filter
- Anatomy and Histology (18) Apply Anatomy and Histology filter
- Cryo-Electron Microscopy (40) Apply Cryo-Electron Microscopy filter
- Electron Microscopy (18) Apply Electron Microscopy filter
- Gene Targeting and Transgenics (11) Apply Gene Targeting and Transgenics filter
- High Performance Computing (7) Apply High Performance Computing filter
- Integrative Imaging (18) Apply Integrative Imaging filter
- Invertebrate Shared Resource (40) Apply Invertebrate Shared Resource filter
- Janelia Experimental Technology (37) Apply Janelia Experimental Technology filter
- Management Team (1) Apply Management Team filter
- Mass Spectrometry (1) Apply Mass Spectrometry filter
- Molecular Genomics (15) Apply Molecular Genomics filter
- Primary & iPS Cell Culture (14) Apply Primary & iPS Cell Culture filter
- Project Technical Resources (51) Apply Project Technical Resources filter
- Quantitative Genomics (20) Apply Quantitative Genomics filter
- Scientific Computing (96) Apply Scientific Computing filter
- Viral Tools (14) Apply Viral Tools filter
- Vivarium (7) Apply Vivarium filter
Publication Date
- 2025 (210) Apply 2025 filter
- 2024 (211) Apply 2024 filter
- 2023 (157) Apply 2023 filter
- 2022 (166) Apply 2022 filter
- 2021 (175) 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
2768 Janelia Publications
Showing 1601-1610 of 2768 resultsFor the information content of microscopy images to be appropriately interpreted, reproduced, and meet FAIR (Findable Accessible Interoperable and Reusable) principles, they should be accompanied by detailed descriptions of microscope hardware, image acquisition settings, image pixel and dimensional structure, and instrument performance. Nonetheless, the thorough documentation of imaging experiments is significantly impaired by the lack of community-sanctioned easy-to-use software tools to facilitate the extraction and collection of relevant microscopy metadata. Here we present Micro-Meta App, an intuitive open-source software designed to tackle these issues that was developed in the context of nascent global bioimaging community organizations, including BioImaging North America (BINA) and QUAlity Assessment and REProducibility in Light Microscopy (QUAREP-LiMi), whose goal is to improve reproducibility, data quality and sharing value for imaging experiments. The App provides a user-friendly interface for building comprehensive descriptions of the conditions utilized to produce individual microscopy datasets as specified by the recently proposed 4DN-BINA-OME tiered-system of Microscopy Metadata model. To achieve this goal the App provides a visual guide for a microscope-user to: 1) interactively build diagrammatic representations of hardware configurations of given microscopes that can be easily reused and shared with colleagues needing to document similar instruments. 2) Automatically extracts relevant metadata from image files and facilitates the collection of missing image acquisition settings and calibration metrics associated with a given experiment. 3) Output all collected Microscopy Metadata to interoperable files that can be used for documenting imaging experiments and shared with the community. In addition to significantly lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training users that have limited knowledge of the intricacies of light microscopy experiments. To ensure wide-adoption by microscope-users with different needs Micro-Meta App closely interoperates with MethodsJ2 and OMERO.mde, two complementary tools described in parallel manuscripts.
For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes.
Extracellular vesicles (EVs) are important mediators of cell-to-cell communication and have been implicated in several pathologies including those of the central nervous system. They are released by all cell types, including neurons, and are highly heterogenous in size and composition. Yet much remains unknown regarding the biophysical characteristics of different EVs. Here, using cryo-electron microscopy (cryoEM), we analyzed the size distribution and morphology of EVs released from primary cortical neurons. We discovered massive macromolecular clusters on the luminal face of EV membranes. These clusters are predominantly found on medium-sized vesicles, suggesting that they may be specific to microvesicles as opposed to exosomes. We propose that these clusters serve as microdomains for EV signaling and play an important role in EV physiology.
MicroED, a method at the intersection of X-ray crystallography and electron cryo-microscopy, has rapidly progressed by exploiting advances in both fields and has already been successfully employed to determine the atomic structures of several proteins from sub-micron-sized, three-dimensional crystals. A major limiting factor in X-ray crystallography is the requirement for large and well ordered crystals. By permitting electron diffraction patterns to be collected from much smaller crystals, or even single well ordered domains of large crystals composed of several small mosaic blocks, MicroED has the potential to overcome the limiting size requirement and enable structural studies on difficult-to-crystallize samples. This communication details the steps for sample preparation, data collection and reduction necessary to obtain refined, high-resolution, three-dimensional models by MicroED, and presents some of its unique challenges.
The cryoEM method Microcrystal Electron Diffraction (MicroED) involves transmission electron microscope (TEM) and electron detector working in synchrony to collect electron diffraction data by continuous rotation. We previously reported several protein, peptide, and small molecule structures by MicroED using manual control of the microscope and detector to collect data. Here we present a procedure to automate this process using a script developed for the popular open-source software package SerialEM. With this approach, SerialEM coordinates stage rotation, microscope operation, and camera functions for automated continuous-rotation MicroED data collection. Depending on crystal and substrate geometry, more than 300 datasets can be collected overnight in this way, facilitating high-throughput MicroED data collection for large-scale data analyses.
Solving the atomic structure of metallic clusters is fundamental to understanding their optical, electronic, and chemical properties. We report the structure of Au146(p-MBA)57 at subatomic resolution (0.85 {\AA}) using electron diffraction (MicroED) and atomic resolution by X-ray diffraction. The 146 gold atoms may be decomposed into two constituent sets consisting of 119 core and 27 peripheral atoms. The core atoms are organized in a twinned FCC structure whereas the surface gold atoms follow a C2 rotational symmetry about an axis bisecting the twinning plane. The protective layer of 57 p-MBAs fully encloses the cluster and comprises bridging, monomeric, and dimeric staple motifs. Au146(p-MBA)57 is the largest cluster observed exhibiting a bulk-like FCC structure as well as the smallest gold particle exhibiting a stacking fault.
Sodium (Na) is a ubiquitous and important inorganic salt mediating many critical biological processes such as neuronal excitation, signaling, and facilitation of various transporters. The hydration states of Na are proposed to play critical roles in determining the conductance and the selectivity of Na channels, yet they are rarely captured by conventional structural biology means. Here we use the emerging cryo-electron microscopy (cryoEM) method micro-electron diffraction (MicroED) to study the structure of a prototypical tetrameric Na-conducting channel, NaK, to 2.5 Å resolution from nano-crystals. Two new conformations at the external site of NaK are identified, allowing us to visualize a partially hydrated Na ion at the entrance of the channel pore. A process of dilation coupled with Na movement is identified leading to valuable insights into the mechanism of ion conduction and gating. This study lays the ground work for future studies using MicroED in membrane protein biophysics.
Theoretical calculations suggest that crystals exceeding 100 nm thickness are excluded by dynamical scattering from successful structure determination using microcrystal electron diffraction (MicroED). These calculations are at odds with experimental results where MicroED structures have been determined from significantly thicker crystals. Here we systematically evaluate the influence of thickness on the accuracy of MicroED intensities and the ability to determine structures from protein crystals one micrometer thick. To do so, we compare ab initio structures of a human prion protein segment determined from thin crystals to those determined from crystals up to one micrometer thick. We also compare molecular replacement solutions from crystals of varying thickness for a larger globular protein, proteinase K. Our results indicate that structures can be reliably determined from crystals at least an order of magnitude thicker than previously suggested by simulation, opening the possibility for an even broader range of MicroED experiments.
HIV-1 protease (PR) cleavage of the Gag polyprotein triggers the assembly of mature, infectious particles. Final cleavage of Gag occurs at the junction helix between the capsid protein CA and the SP1 spacer peptide. Here we used MicroED to delineate the binding interactions of the maturation inhibitor bevirimat (BVM) using very thin frozen-hydrated, 3D microcrystals of a CTD-SP1 Gag construct with and without bound BVM. The 2.9-Å MicroED structure revealed that a single BVM molecule stabilizes the six-helix bundle via both electrostatic interactions with the dimethylsuccinyl moiety and hydrophobic interactions with the pentacyclic triterpenoid ring. These results provide insight into the mechanism of action of BVM and related maturation inhibitors that will inform further drug discovery efforts. This study also demonstrates the capabilities of MicroED for structure-based drug design.
