Filter
Associated Lab
- Aguilera Castrejon Lab (1) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (53) 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 (8) Apply Beyene Lab filter
- Bock Lab (14) Apply Bock Lab filter
- Branson Lab (50) Apply Branson Lab filter
- Card Lab (36) 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 (7) Apply Feliciano Lab filter
- Fetter Lab (31) Apply Fetter Lab filter
- Fitzgerald Lab (16) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (38) 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 (23) 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 (137) 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 (97) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (1) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (58) 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 (105) 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 (50) 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 (57) Apply Spruston Lab filter
- Stern Lab (73) Apply Stern Lab filter
- Sternson Lab (47) Apply Sternson Lab filter
- Stringer Lab (32) 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 (39) Apply Turaga Lab filter
- Turner Lab (27) Apply Turner Lab filter
- Vale Lab (7) Apply Vale Lab filter
- Voigts Lab (3) Apply Voigts Lab filter
- Wang (Meng) Lab (21) 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 (53) Apply FlyEM filter
- FlyLight (49) Apply FlyLight filter
- GENIE (46) Apply GENIE filter
- Integrative Imaging (4) 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) (26) 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 (36) Apply Cryo-Electron Microscopy filter
- Electron Microscopy (16) Apply Electron Microscopy filter
- Gene Targeting and Transgenics (11) Apply Gene Targeting and Transgenics 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 Software (92) Apply Scientific Computing Software filter
- Scientific Computing Systems (7) Apply Scientific Computing Systems filter
- Viral Tools (14) Apply Viral Tools filter
- Vivarium (7) Apply Vivarium filter
Publication Date
- 2025 (126) Apply 2025 filter
- 2024 (215) Apply 2024 filter
- 2023 (159) Apply 2023 filter
- 2022 (167) 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
2691 Janelia Publications
Showing 601-610 of 2691 resultsThe GAL4-UAS system has proven its versatility in studying the function and expression patterns of neurons the Drosophila central nervous system. Although the GAL4 system has been used for 25 years, recent genetic intersectional tools have enabled genetic targeting of very small numbers of neurons aiding in the understanding of their function. This split-GAL4 system is extremely powerful for studying neuronal morphology and the neural basis of animal behavior. However, choosing lines to intersect that have overlapping patterns restricted to one to a few neurons has been cumbersome. This challenge is now growing as the collections of GAL4 driver lines has increased. Here we present a new method and software plug-in for Fiji to dramatically improve the speed of querying large databases of potential lines to intersect and aid in the split-GAL4 creation. We also provide pre-computed datasets for the Janelia GAL4 (5,738 lines) and VT GAL4 (7,429 lines) of the Drosophila central nervous system (CNS). The tool reduced our split-GAL4 creation effort dramatically.
A recent study reports a novel form of lateral inhibition between photoreceptors supporting colour vision in the vinegar fly, Drosophila melanogaster.
Wide-field motion-sensitive neurons in the lobula plate (lobula plate tangential cells, LPTCs) of the fly have been studied for decades. However, it has never been conclusively shown which cells constitute their major presynaptic elements. LPTCs are supposed to be rendered directionally selective by integrating excitatory as well as inhibitory input from many local motion detectors. Based on their stratification in the different layers of the lobula plate, the columnar cells T4 and T5 are likely candidates to provide some of this input. To study their role in motion detection, we performed whole-cell recordings from LPTCs in Drosophila with T4 and T5 cells blocked using two different genetically encoded tools. In these flies, motion responses were abolished, while flicker responses largely remained. We thus demonstrate that T4 and T5 cells indeed represent those columnar cells that provide directionally selective motion information to LPTCs. Contrary to previous assumptions, flicker responses seem to be largely mediated by a third, independent pathway. This work thus represents a further step towards elucidating the complete motion detection circuitry of the fly.
Motor systems flexibly implement diverse motor programs to pattern behavioral sequences, yet their neural underpinnings remain unclear. Here, we investigated the neural circuit mechanisms of flexible courtship behavior in Drosophila. Courting males alternately produce two types of courtship song. By recording calcium signals in the ventral nerve cord (VNC) in behaving flies, we found that different songs are produced by activating overlapping neural populations with distinct motor functions in a combinatorial manner. Recordings from the brain suggest that song is driven by two descending pathways – one defines when to sing and the other specifies what song to sing. Connectomic analysis reveals that these “when” and “what” descending pathways provide structured input to VNC neurons with different motor functions. These results suggest that dynamic changes in the activation patterns of descending pathways drive different combinations of motor modules, thereby flexibly switching between different motor actions.
Programmed genome rearrangements in the unicellular eukaryote Oxytricha trifallax produce a transcriptionally active somatic nucleus from a copy of its germline nucleus during development. This process eliminates noncoding sequences that interrupt coding regions in the germline genome, and joins over 225,000 remaining DNA segments, some of which require inversion or complex permutation to build functional genes. This dynamic genomic organization permits some single DNA segments in the germline to contribute to multiple, distinct somatic genes via alternative processing. Like alternative mRNA splicing, the combinatorial assembly of DNA segments contributes to genetic variation and facilitates the evolution of new genes. In this study, we use comparative genomic analysis to demonstrate that the emergence of alternative DNA splicing is associated with the origin of new genes. Short duplications give rise to alternative gene segments that are spliced to the shared gene segments. Alternative gene segments evolve faster than shared, constitutive segments. Genes with shared segments frequently have different expression profiles, permitting functional divergence. This study reports alternative DNA splicing as a mechanism of new gene origination, illustrating how the process of programmed genome rearrangement gives rise to evolutionary innovation.
Integrin alpha M (ITGAM; CD11b) is a component of the macrophage-1 antigen complex, which mediates leukocyte adhesion, migration and phagocytosis as part of the immune system. We previously identified a missense polymorphism, rs1143679 (R77H), strongly associated with systemic lupus erythematosus (SLE). However, the molecular mechanisms of this variant are incompletely understood. A meta-analysis of published and novel data on 28 439 individuals with European, African, Hispanic and Asian ancestries reinforces genetic association between rs1143679 and SLE [Pmeta = 3.60 × 10(-90), odds ratio (OR) = 1.76]. Since rs1143679 is in the most active region of chromatin regulation and transcription factor binding in ITGAM, we quantitated ITGAM RNA and surface protein levels in monocytes from patients with each rs1143679 genotype. We observed that transcript levels significantly decreased for the risk allele ('A') relative to the non-risk allele ('G'), in a dose-dependent fashion: ('AA' < 'AG' < 'GG'). CD11b protein levels in patients' monocytes were directly correlated with RNA levels. Strikingly, heterozygous individuals express much lower (average 10- to 15-fold reduction) amounts of the 'A' transcript than 'G' transcript. We found that the non-risk sequence surrounding rs1143679 exhibits transcriptional enhancer activity in vivo and binds to Ku70/80, NFKB1 and EBF1 in vitro, functions that are significantly reduced with the risk allele. Mutant CD11b protein shows significantly reduced binding to fibrinogen and vitronectin, relative to non-risk, both in purified protein and in cellular models. This two-pronged contribution (nucleic acid- and protein-level) of the rs1143679 risk allele to decreasing ITGAM activity provides insight into the molecular mechanisms of its potent association with SLE.
While fluorescence microscopy has proven to be an exceedingly useful tool in bioscience, it is difficult to offer simultaneous high resolution, fast speed, large volume, and good biocompatibility in a single imaging technique. Thus, when determining the image data required to quantitatively test a complex biological hypothesis, it often becomes evident that multiple imaging techniques are necessary. Recent years have seen an explosion in development of novel fluorescence microscopy techniques, each of which features a unique suite of capabilities. In this Technical Perspective, we highlight recent studies to illustrate the benefits, and often the necessity, of combining multiple fluorescence microscopy modalities. We provide guidance in choosing optimal technique combinations to effectively address a biological question. Ultimately, we aim to promote a more well-rounded approach in designing fluorescence microscopy experiments, leading to more robust quantitative insight.
Uncertainty is a fundamental aspect of the natural environment, requiring the brain to infer and integrate noisy signals to guide behavior effectively. Sampling-based inference has been proposed as a mechanism for dealing with uncertainty, particularly in early sensory processing. However, it is unclear how to reconcile sampling-based methods with operational principles of higher-order brain areas, such as attractor dynamics of persistent neural representations. In this study, we present a spiking neural network model for the head-direction (HD) system that combines sampling-based inference with attractor dynamics. To achieve this, we derive the required spiking neural network dynamics and interactions to perform sampling from a large family of probability distributions - including variables encoded with Poisson noise. We then propose a method that allows the network to update its estimate of the current head direction by integrating angular velocity samples - derived from noisy inputs - with a pull towards a circular manifold, thereby maintaining consistent attractor dynamics. This model makes specific, testable predictions about the HD system that can be examined in future neurophysiological experiments: it predicts correlated subthreshold voltage fluctuations; distinctive short- and long-term firing correlations among neurons; and characteristic statistics of the movement of the neural activity "bump" representing the head direction. Overall, our approach extends previous theories on probabilistic sampling with spiking neurons, offers a novel perspective on the computations responsible for orientation and navigation, and supports the hypothesis that sampling-based methods can be combined with attractor dynamics to provide a viable framework for studying neural dynamics across the brain.
Tumors are complex ecosystems composed of malignant and non-malignant cells embedded in a dynamic extracellular matrix (ECM). In the tumor microenvironment, molecular phenotypes are controlled by cell-cell and ECM interactions in 3D cellular neighborhoods (CNs). While their inhibition can impede tumor progression, routine molecular tumor profiling fails to capture cellular interactions. Single-cell spatial transcriptomics (ST) maps receptor-ligand interactions but usually remains limited to 2D tissue sections and lacks ECM readouts. Here, we integrate 3D ST with ECM imaging in serial sections from one clinical lung carcinoma to systematically quantify molecular states, cell-cell interactions, and ECM remodeling in CN. Our integrative analysis pinpointed known immune escape and tumor invasion mechanisms, revealing several druggable drivers of tumor progression in the patient under study. This proof-of-principle study highlights the potential of in-depth CN profiling in routine clinical samples to inform microenvironment-directed therapies. A record of this paper's transparent peer review process is included in the supplemental information.
Watanabe et al (Reports, 12 April 2013, p. 195) study the yeast SWR1/SWR-C complex responsible for depositing the histone variant H2A.Z by replacing nucleosomal H2A with H2A.Z. They report that reversal of H2A.Z replacement is mediated by SWR1 and related INO80 on an H2A.Z nucleosome carrying H3K56Q. Using multiple assays and reaction conditions, we find no evidence of such reversal of H2A.Z exchange.