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 1921-1930 of 2768 resultsOlfactory systems encode odours by which neurons respond and by when they respond. In mammals, every sniff evokes a precise, odour-specific sequence of activity across olfactory neurons. Likewise, in a variety of neural systems, ranging from sensory periphery to cognitive centres, neuronal activity is timed relative to sampling behaviour and/or internally generated oscillations. As in these neural systems, relative timing of activity may represent information in the olfactory system. However, there is no evidence that mammalian olfactory systems read such cues. To test whether mice perceive the timing of olfactory activation relative to the sniff cycle (’sniff phase’), we used optogenetics in gene-targeted mice to generate spatially constant, temporally controllable olfactory input. Here we show that mice can behaviourally report the sniff phase of optogenetically driven activation of olfactory sensory neurons. Furthermore, mice can discriminate between light-evoked inputs that are shifted in the sniff cycle by as little as 10 milliseconds, which is similar to the temporal precision of olfactory bulb odour responses. Electrophysiological recordings in the olfactory bulb of awake mice show that individual cells encode the timing of photoactivation in relation to the sniff in both the timing and the amplitude of their responses. Our work provides evidence that the mammalian olfactory system can read temporal patterns, and suggests that timing of activity relative to sampling behaviour is a potent cue that may enable accurate olfactory percepts to form quickly.
To interpret the sensory environment, the brain combines ambiguous sensory measurements with 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 the statistical structure of the task to maximize decision accuracy and show that its decisions are biased by task context. The magnitude of this decision bias is not a fixed property of the sensory measurement but depends on the observer's belief about the current context. The model therefore predicts that decision bias will grow with the reliability of the context cue, the stability of the environment, and with the number of trials since the last context switch. Analysis of human choice data validates all three predictions, providing evidence that the brain continuously updates probabilistic representations of the environment to best interpret an uncertain, ever-changing world.
Life scientists often desire to display the signal from two different molecular probes as a single colour image, so as to convey information about the probes' relative concentrations as well as their spatial corelationship. Traditionally, such colour images are created through a merge display, where each greyscale signal is assigned to different channels of an RGB colour image. However, human perception of colour and greyscale intensity is not equivalent. Thus, a merged image display conveys to the typical viewer only a subset of the absolute and relative intensity information present in and between two greyscale images. The Commission Internationale de l'Eclairage L*a*b* colour space (CIELAB) has been designed to specify colours according to the perceptually defined quantities of hue (perceived colour) and luminosity (perceived brightness). Here, we use the CIELAB colour space to encode two dimensions of information about two greyscale images within these two perceptual dimensions of a single colour image. We term our method a Perceptually Uniform Projection display and show using biological image examples how these displays convey more information about two greyscale signals than comparable RGB colour space-based techniques.
The brain microvascular functions are strongly influenced by the local microenvironment and cellular organization. Intracellular organelles, including primary cilia and centrioles, play critical roles in sensing and transmitting environmental cues and maintaining vascular integrity. However, their distribution across the brain vasculature remains poorly understood. In this study, we utilized publicly available large-volume electron microscopy datasets encompassing the cerebral vasculature from pial arterioles through parenchymal capillaries to pial venules. We systematically analyzed the cellular organization and characterized the distribution of primary cilia and centrioles in the mouse and human brain microvasculature. We found primary cilia exclusively on human cortical endothelial cells (ECs), indicating inter-species differences between mouse and human. Primary cilia were frequently present on mural cells (MCs, smooth muscle cells or pericytes) surrounding venules and capillaries but rarely observed on arterioles in both mouse and human brains. These MC primary cilia exhibited heterogeneity in ciliogenesis, including cells with ciliary pockets, surface cilia, and a hybrid configuration we refer as a partial pocket. In the mouse brain, many MC primary cilia were closely ensheathed by astrocytic endfeet and occasionally extended between them to establish proximity to synapses, whereas all primary cilia in the human brain were confined within the basal lamina. Our analysis of cellular density revealed similar EC densities between arterioles and venules in mice, but not in human. EC centrioles were consistently positioned against the direction of blood flow relative to the nuclei, suggesting that they may serve as a structural marker for flow direction. Collectively, these findings provide a comprehensive characterization of primary cilia and centrioles, highlighting distinct interspecies differences between mouse and human brain microvasculature. The proximity to neural cells and gradient distribution of these subcellular structures suggest that they may act as antennae for sensing mechanical and chemical signals within the brain microvascular environment.
Neuronal dendrites must relay synaptic inputs over long distances, but the mechanisms by which activity-evoked intracellular signals propagate over macroscopic distances remain unclear. Here, we discovered a system of periodically arranged endoplasmic reticulum-plasma membrane (ER-PM) junctions tiling the plasma membrane of dendrites at ∼1 μm intervals, interlinked by a meshwork of ER tubules patterned in a ladder-like array. Populated with Junctophilin-linked plasma membrane voltage-gated Ca channels and ER Ca-release channels (ryanodine receptors), ER-PM junctions are hubs for ER-PM crosstalk, fine-tuning of Ca homeostasis, and local activation of the Ca/calmodulin-dependent protein kinase II. Local spine stimulation activates the Ca modulatory machinery, facilitating signal transmission and ryanodine-receptor-dependent Ca release at ER-PM junctions over 20 μm away. Thus, interconnected ER-PM junctions support signal propagation and Ca release from the spine-adjacent ER. The capacity of this subcellular architecture to modify both local and distant membrane-proximal biochemistry potentially contributes to dendritic computations.
Neuronal dendrites must relay synaptic inputs over long distances, but the mechanisms by which activity-evoked intracellular signals propagate over macroscopic distances remain unclear. Here, we discovered a system of periodically arranged endoplasmic reticulum-plasma membrane (ER-PM) junctions tiling the plasma membrane of dendrites at ∼1 μm intervals, interlinked by a meshwork of ER tubules patterned in a ladder-like array. Populated with Junctophilin-linked plasma membrane voltage-gated Ca channels and ER Ca-release channels (ryanodine receptors), ER-PM junctions are hubs for ER-PM crosstalk, fine-tuning of Ca homeostasis, and local activation of the Ca/calmodulin-dependent protein kinase II. Local spine stimulation activates the Ca modulatory machinery, facilitating signal transmission and ryanodine-receptor-dependent Ca release at ER-PM junctions over 20 μm away. Thus, interconnected ER-PM junctions support signal propagation and Ca release from the spine-adjacent ER. The capacity of this subcellular architecture to modify both local and distant membrane-proximal biochemistry potentially contributes to dendritic computations.
Brain neurons utilize the primary cilium as a privileged compartment to detect and respond to extracellular ligands such as Sonic hedgehog (SHH). However, cilia in cerebellar granule cell (GC) neurons disassemble during differentiation through ultrastructurally unique intermediates, a process we refer to as cilia deconstruction. In addition, mature neurons do not reciliate despite having docked centrioles. Here, we identify molecular changes that accompany cilia deconstruction and centriole docking in GC neurons. We used single cell transcriptomic and immunocytological analyses to compare the transcript levels and subcellular localization of proteins between progenitor, differentiating, and mature GCs. Differentiating GCs lacked transcripts for key activators of premitotic cilia resorption, indicating that cilia disassembly in differentiating cells is distinct from premitotic cilia resorption. Instead, during differentiation, transcripts of many genes required for cilia maintenance-specifically those encoding components of intraflagellar transport, pericentrosomal material, and centriolar satellites-decreased. The abundance of several corresponding proteins in and around cilia and centrosomes also decreased. These changes coincided with downregulation of SHH signaling prior to differentiation, even in a mutant with excessive SHH activation. Finally, mother centrioles in maturing granule neurons recruited the cap complex protein, CEP97. These data suggest that a global, developmentally programmed decrease in cilium maintenance in differentiating GCs mediates cilia deconstruction, while capping of docked mother centrioles prevents cilia regrowth and dysregulated SHH signaling. Our study provides mechanistic insights expanding our understanding of permanent cilia loss in multiple tissue-specific contexts.
Primary cilia on granule cell neuron progenitors in the developing cerebellum detect sonic hedgehog to facilitate proliferation. Following differentiation, cerebellar granule cells become the most abundant neuronal cell type in the brain. While granule cell cilia are essential during early developmental stages, they become infrequent upon maturation. Here, we provide nanoscopic resolution of cilia in situ using large-scale electron microscopy volumes and immunostaining of mouse cerebella. In many granule cells, we found intracellular cilia, concealed from the external environment. Cilia were disassembled in differentiating granule cell neurons-in a process we call cilia deconstruction-distinct from premitotic cilia resorption in proliferating progenitors. In differentiating granule cells, cilia deconstruction involved unique disassembly intermediates, and, as maturation progressed, mother centriolar docking at the plasma membrane. Unlike ciliated neurons in other brain regions, our results show the deconstruction of concealed cilia in differentiating granule cells, which might prevent mitogenic hedgehog responsiveness. Ciliary deconstruction could be paradigmatic of cilia removal during differentiation in other tissues.
Recurrent connections are thought to be a common feature of the neural circuits that encode memories, but how memories are laid down in such circuits is not fully understood. Here we present evidence that courtship memory in Drosophila relies on the recurrent circuit between mushroom body gamma (MBg), M6 output, and aSP13 dopaminergic neurons. We demonstrate persistent neuronal activity of aSP13 neurons and show that it transiently potentiates synaptic transmission from MBγ>M6 neurons. M6 neurons in turn provide input to aSP13 neurons, prolonging potentiation of MBγ>M6 synapses over time periods that match short-term memory. These data support a model in which persistent aSP13 activity within a recurrent circuit lays the foundation for a short-term memory.
The mammalian hippocampus forms a cognitive map using neurons that fire according to an animal's position ("place cells") and many other behavioral and cognitive variables. The responses of these neurons are shaped by their presynaptic inputs and the nature of their postsynaptic integration. In CA1 pyramidal neurons, spatial responses in vivo exhibit a strikingly supralinear dependence on baseline membrane potential. The biophysical mechanisms underlying this nonlinear cellular computation are unknown. Here, through a combination of in vitro, in vivo, and in silico approaches, we show that persistent sodium current mediates the strong membrane potential dependence of place cell activity. This current operates at membrane potentials below the action potential threshold and over seconds-long timescales, mediating a powerful and rapidly reversible amplification of synaptic responses, which drives place cell firing. Thus, we identify a biophysical mechanism that shapes the coding properties of neurons composing the hippocampal cognitive map.
