Filter
Associated Lab
- Aguilera Castrejon Lab (2) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (55) Apply Ahrens Lab filter
- Aso Lab (40) Apply Aso Lab filter
- Baker Lab (19) Apply Baker Lab filter
- Betzig Lab (101) 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 (38) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (4) Apply Egnor Lab filter
- Espinosa Medina Lab (15) Apply Espinosa Medina Lab filter
- Feliciano Lab (8) 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 (39) Apply Funke Lab filter
- Gonen Lab (59) Apply Gonen Lab filter
- Grigorieff Lab (34) Apply Grigorieff Lab filter
- Harris Lab (53) Apply Harris Lab filter
- Heberlein Lab (13) Apply Heberlein Lab filter
- Hermundstad Lab (24) Apply Hermundstad Lab filter
- Hess Lab (74) Apply Hess Lab filter
- Ilanges Lab (2) Apply Ilanges Lab filter
- Jayaraman Lab (42) 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 (139) Apply Lavis Lab filter
- Lee (Albert) Lab (29) Apply Lee (Albert) Lab filter
- Leonardo Lab (19) Apply Leonardo Lab filter
- Li Lab (4) Apply Li Lab filter
- Lippincott-Schwartz Lab (100) 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 (36) 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 (45) Apply Reiser Lab filter
- Riddiford Lab (20) Apply Riddiford Lab filter
- Romani Lab (31) Apply Romani Lab filter
- Rubin Lab (107) Apply Rubin Lab filter
- Saalfeld Lab (46) Apply Saalfeld Lab filter
- Satou Lab (1) Apply Satou Lab filter
- Scheffer Lab (36) 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 (58) Apply Spruston Lab filter
- Stern Lab (73) Apply Stern Lab filter
- Sternson Lab (47) Apply Sternson Lab filter
- Stringer Lab (33) Apply Stringer Lab filter
- Svoboda Lab (131) Apply Svoboda Lab filter
- Tebo Lab (9) 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 (40) 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 (22) 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 (3) Apply FIB-SEM Technology filter
- Fly Descending Interneuron (11) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (14) Apply Fly Functional Connectome filter
- Fly Olympiad (5) Apply Fly Olympiad filter
- FlyEM (54) Apply FlyEM filter
- FlyLight (49) 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) (27) 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 (39) Apply Cryo-Electron Microscopy filter
- Electron Microscopy (17) 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 (17) 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
- Molecular Genomics (15) Apply Molecular Genomics filter
- Primary & iPS Cell Culture (14) Apply Primary & iPS Cell Culture filter
- Project Technical Resources (50) Apply Project Technical Resources filter
- Quantitative Genomics (19) Apply Quantitative Genomics filter
- Scientific Computing (93) Apply Scientific Computing filter
- Viral Tools (14) Apply Viral Tools filter
- Vivarium (7) Apply Vivarium filter
Publication Date
- 2025 (160) Apply 2025 filter
- 2024 (213) Apply 2024 filter
- 2023 (158) 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
2721 Janelia Publications
Showing 1101-1110 of 2721 resultsPredictive remapping (PRE )—the ability of cells in retinotopic brain structures to transiently exhibit spatiotemporal shifts beyond the spatial extent of their classical anatomical receptive fields—has been proposed as a primary mechanism that stabilizes an organism’s percept of the visual world around the time of a saccadic eye movement. Despite the well-documented effects of PRE , a biologically plausible mathematical framework that specifies a fundamental law and the functional neural architecture that actively mediates this ubiquitous phenomenon does not exist. We introduce the Newtonian model of PRE , where each modular component of PRE manifests as three temporally overlapping forces: centripetal ( fC ), convergent ( fP ), and translational ( fT ), that perturb retinotopic cells from their equilibrium extent. The resultant and transient influences of these forces fC + fP + fT gives rise to a neuronal force field that governs the spatiotemporal dynamics of PRE . This neuronal force field fundamentally obeys an inverse-distance law PRE ∝ 1 r1.6 , akin to Newton’s law of universal gravitation [I. Newton, Newton’s Principia: The Mathematical Principles of Natural Philosophy (Geo. P. Putnam, New-York, 1850)] and activates retinotopic elastic fields elϕ’s. We posit that elϕ’s are transient functional neural structures that are self-generated by visual systems during active vision and approximate the sloppiness (or degrees of spatial freedom) within which receptive fields are allowed to shift, while ensuring that retinotopic organization does not collapse. The predictions of this general model are borne out by the spatiotemporal changes in visual sensitivity to probe stimuli in human subjects around the time of an eye movement and qualitatively match neural sensitivity signatures associated with predictive shifts in the receptive fields of cells in premotor and higher-order retinotopic brain structures. The introduction of this general model opens the search for possible biophysical implementations and provides experimentalists with a simple, elegant, yet powerful mathematical framework they can now use to generate experimentally testable predictions across a range of biological systems.
We demonstrate gas cluster ion beam scanning electron microscopy (SEM), in which wide-area ion milling is performed on a series of thick tissue sections. This three-dimensional electron microscopy technique acquires datasets with <10 nm isotropic resolution of each section, and these can then be stitched together to span the sectioned volume. Incorporating gas cluster ion beam SEM into existing single-beam and multibeam SEM workflows should be straightforward, increasing reliability while improving z resolution by a factor of three or more.
Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) generates 3D datasets optimally suited for segmentation of cell ultrastructure and automated connectome tracing but is limited to small fields of view and is therefore incompatible with the new generation of ultrafast multibeam SEMs. In contrast, section-based techniques are multibeam-compatible but are limited in z-resolution making automatic segmentation of cellular ultrastructure difficult. Here we demonstrate a novel 3D electron microscopy technique, Gas Cluster Ion Beam SEM (GCIB-SEM), in which top-down, wide-area ion milling is performed on a series of thick sections, acquiring < 10 nm isotropic datasets of each which are then stitched together to span the full sectioned volume. Based on our results, incorporating GCIB-SEM into existing single beam and multibeam SEM workflows should be straightforward and should dramatically increase reliability while simultaneously improving z-resolution by a factor of 3 or more.
View Publication PageThe brainstem contains several neuronal populations, heterogeneous in term of neurotransmitter/neuropeptide content, which are important for controlling various aspects of the REM phase of sleep. Among these populations are the Calbindin (Calb)-immunoreactive NPCalb neurons, located in the Nucleus papilio, within the dorsal paragigantocellular nucleus (DPGi), and recently shown to control eye movement during the REM phase of sleep. We performed in depth data-mining of the in-situ hybridization data collected at the Allen Brain Atlas, in order to identify potentially interesting genes expressed in this brainstem nucleus. Our attention focused on genes encoding neuropeptides, including Cart (Cocaine and Amphetamine Regulated Transcripts) and Nesfatin1. While Nesfatin1 appeared ubiquitously expressed in this Calb-positive neuronal population, Cart was co-expressed in only a subset of these glutamatergic NPCalb neurons. Furthermore, a REM sleep deprivation and rebound assay performed with mice revealed that the Cart-positive neuronal population within the DPGi was activated during REM sleep (as measured by c-fos immunoreactivity), suggesting a role of this neuropeptide in regulating some aspects of REM sleep. The assembled information could afford functional clues to investigators, conducive to further experimental pursuits.
During speciation, sex chromosomes often accumulate interspecific genetic incompatibilities faster than the rest of the genome. The drive theory posits that sex chromosomes are susceptible to recurrent bouts of meiotic drive and suppression, causing the evolutionary build-up of divergent cryptic sex-linked drive systems and, incidentally, genetic incompatibilities. To assess the role of drive during speciation, we combine high-resolution genetic mapping of X-linked hybrid male sterility with population genomics analyses of divergence and recent gene flow between the fruitfly species, and . Our findings reveal a high density of genetic incompatibilities and a corresponding dearth of gene flow on the X chromosome. Surprisingly, we find that a known drive element recently migrated between species and, rather than contributing to interspecific divergence, caused a strong reduction in local sequence divergence, undermining the evolution of hybrid sterility. Gene flow can therefore mediate the effects of selfish genetic elements during speciation.
Homology of highly divergent genes often cannot be determined from sequence similarity alone. For example, we recently identified in the aphid Hormaphis cornu a family of rapidly evolving bicycle genes, which encode novel proteins implicated as plant gall effectors, and sequence similarity search methods yielded few putative bicycle homologs in other species. Coding sequence-independent features of genes, such as intron-exon boundaries, often evolve more slowly than coding sequences, however, and can provide complementary evidence for homology. We found that a linear logistic regression classifier using only structural features of bicycle genes identified many putative bicycle homologs in other species. Independent evidence from sequence features and intron locations supported homology assignments. To test the potential roles of bicycle genes in other aphids, we sequenced the genome of a second gall-forming aphid, Tetraneura nigriabdominalis, and found that many bicycle genes are strongly expressed in the salivary glands of the gall forming foundress. In addition, bicycle genes are strongly overexpressed in the salivary glands of a non-gall forming aphid, Acyrthosiphon pisum, and in the non-gall forming generations of Hormaphis cornu. These observations suggest that Bicycle proteins may be used by multiple aphid species to manipulate plants in diverse ways. Incorporation of gene structural features into sequence search algorithms may aid identification of deeply divergent homologs, especially of rapidly evolving genes involved in host-parasite interactions.
The century-old fluoresceins and rhodamines persist as flexible scaffolds for fluorescent and fluorogenic compounds. Extensive exploration of these xanthene dyes has yielded general structure–activity relationships where the development of new probes is limited only by imagination and organic chemistry. In particular, replacement of the xanthene oxygen with silicon has resulted in new red-shifted Si-fluoresceins and Si-rhodamines, whose high brightness and photostability enable advanced imaging experiments. Nevertheless, efforts to tune the chemical and spectral properties of these dyes have been hindered by difficult synthetic routes. Here, we report a general strategy for the efficient preparation of Si-fluoresceins and Si-rhodamines from readily synthesized bis(2-bromophenyl)silane intermediates. These dibromides undergo metal/bromide exchange to give bis-aryllithium or bis(aryl Grignard) intermediates, which can then add to anhydride or ester electrophiles to afford a variety of Si-xanthenes. This strategy enabled efficient (3–5 step) syntheses of known and novel Si-fluoresceins, Si-rhodamines, and related dye structures. In particular, we discovered that previously inaccessible tetrafluorination of the bottom aryl ring of the Si-rhodamines resulted in dyes with improved visible absorbance in solution, and a convenient derivatization through fluoride-thiol substitution. This modular, divergent synthetic method will expand the palette of accessible xanthenoid dyes across the visible spectrum, thereby pushing further the frontiers of biological imaging.
Transgenesis in numerous eukaryotes has been facilitated by the use of site-specific integrases to stably insert transgenes at predefined genomic positions (landing sites). However, the utility of integrase-mediated transgenesis in any system is constrained by the limited number and variable expression properties of available landing sites. By exploiting the nonstandard recombination activity exhibited by a phiC31 integrase mutant, we developed a rapid and inexpensive method for isolating landing sites that exhibit desired expression properties. Additionally, we devised a simple technique for constructing arrays of transgenes at a single landing site, thereby extending the utility of previously characterized landing sites. Using the fruit fly Drosophila melanogaster, we demonstrate the feasibility of these approaches by isolating new landing sites optimized to express transgenes in the nervous system and by building fluorescent reporter arrays at several landing sites. Because these strategies require the activity of only a single exogenous protein, we anticipate that they will be portable to species such as nonmodel organisms, in which genetic manipulation is more challenging, expediting the development of genetic resources in these systems.
By generating and studying mosaic organisms, we are learning how intricate tissues form as cells proliferate and diversify through organism development. FLP/FRT-mediated site-specific mitotic recombination permits the generation of mosaic flies with efficiency and control. With heat-inducible or tissue-specific FLP transgenes at our disposal, we can engineer mosaics carrying clones of homozygous cells that come from specific pools of heterozygous precursors. This permits detailed cell lineage analysis followed by mosaic analysis of gene functions in the underlying developmental processes. Expression of transgenes (e.g., reporters) only in the homozygous cells enables mosaic analysis in the complex nervous system. Tracing neuronal lineages by using mosaics revolutionized mechanistic studies of neuronal diversification and differentiation, exemplifying the power of genetic mosaics in developmental biology. WIREs Dev Biol 2014, 3:69–81. doi: 10.1002/wdev.122
Generating diverse neurons in the central nervous system involves three major steps. First, heterogeneous neural progenitors are specified by positional cues at early embryonic stages. Second, neural progenitors sequentially produce neurons or intermediate precursors that acquire different temporal identities based on their birth-order. Third, sister neurons produced during asymmetrical terminal mitoses are given distinct fates. Determining the molecular mechanisms underlying each of these three steps of cellular diversification will unravel brain development and evolution. Drosophila has a relatively simple and tractable CNS, and previous studies on Drosophila CNS development have greatly advanced our understanding of neuron fate specification. Here we review those studies and discuss how the lessons we have learned from fly teach us the process of neuronal diversification in general.