Filter
Associated Lab
- Aguilera Castrejon Lab (1) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (51) Apply Ahrens Lab filter
- Aso Lab (40) Apply Aso Lab filter
- Baker Lab (19) Apply Baker Lab filter
- Betzig Lab (99) Apply Betzig Lab filter
- Beyene Lab (8) Apply Beyene Lab filter
- Bock Lab (14) Apply Bock Lab filter
- Branson Lab (49) 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 (13) 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 (14) 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 (37) Apply Funke Lab filter
- Gonen Lab (59) Apply Gonen Lab filter
- Grigorieff Lab (34) Apply Grigorieff Lab filter
- Harris Lab (50) Apply Harris Lab filter
- Heberlein Lab (13) Apply Heberlein Lab filter
- Hermundstad Lab (22) Apply Hermundstad Lab filter
- Hess Lab (73) 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 (132) 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 (93) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (1) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (56) 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 (5) Apply O'Shea Lab filter
- Otopalik Lab (1) Apply Otopalik Lab filter
- Pachitariu Lab (34) 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 (45) 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
- Shroff Lab (29) Apply Shroff Lab filter
- Simpson Lab (18) Apply Simpson Lab filter
- Singer Lab (37) Apply Singer Lab filter
- Spruston Lab (56) Apply Spruston Lab filter
- Stern Lab (72) Apply Stern Lab filter
- Sternson Lab (47) Apply Sternson Lab filter
- Stringer Lab (29) Apply Stringer Lab filter
- Svoboda Lab (131) Apply Svoboda Lab filter
- Tebo Lab (8) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (16) Apply Tillberg Lab filter
- Tjian Lab (17) Apply Tjian Lab filter
- Truman Lab (58) Apply Truman Lab filter
- Turaga Lab (36) Apply Turaga Lab filter
- Turner Lab (26) Apply Turner Lab filter
- Vale Lab (7) Apply Vale Lab filter
- Voigts Lab (3) Apply Voigts Lab filter
- Wang (Meng) Lab (17) 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 (2) Apply FIB-SEM Technology 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 (53) Apply FlyEM filter
- FlyLight (48) Apply FlyLight filter
- GENIE (42) Apply GENIE filter
- Integrative Imaging (2) 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 (3) 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 (15) 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 (36) 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 (47) Apply Project Technical Resources filter
- Quantitative Genomics (19) Apply Quantitative Genomics filter
- Scientific Computing Software (88) Apply Scientific Computing Software filter
- Scientific Computing Systems (6) Apply Scientific Computing Systems filter
- Viral Tools (14) Apply Viral Tools filter
- Vivarium (7) Apply Vivarium filter
Publication Date
- 2025 (57) Apply 2025 filter
- 2024 (223) Apply 2024 filter
- 2023 (162) 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
2633 Janelia Publications
Showing 2261-2270 of 2633 resultsThe development of genetically encoded self-labeling protein tags such as the HaloTag and SNAP-tag has expanded the utility of chemical dyes in microscopy. Intracellular labeling using these systems requires small, cell-permeable dyes with high brightness and photostability. We recently discovered a general method to improve the properties of classic fluorophores by replacing N,N-dimethylamino groups with four-membered azetidine rings to create the "Janelia Fluor" dyes. Here, we describe the synthesis of the HaloTag and SNAP-tag ligands of Janelia Fluor 549 and Janelia Fluor 646 as well as standard labeling protocols for use in ensemble and single-molecule cellular imaging.
A unified, convenient, and efficient strategy for the preparation of rhodamines and N,N’-diacylated rhodamines has been developed. Fluorescein ditriflates were found to undergo palladium-catalyzed C-N cross-coupling with amines, amides, carbamates, and other nitrogen nucleophiles to provide direct access to known and novel rhodamine derivatives, including fluorescent dyes, quenchers, and latent fluorophores.
The ultimate goal of neuroscience is to relate the complex activity of cells and cell-networks to behavior and cognition. This requires tools and techniques to visualize neuronal activity. Fluorescence microscopy is an ideal tool to measure activity of cells in the brain due to the high sensitivity of the technique and the growing portfolio of optical hardware and fluorescent sensors. Here, we give a chemist's perspective on the recent progress of fluorescent activity indicators that enable the measurement of cellular events in the living brain. We discuss advances in both chemical and genetically encoded sensors and look forward to hybrid indicators, which incorporate synthetic organic dyes into genetically encoded protein constructs.
Our companion paper (Takemura et al., 2023) introduces the first completely proofread connectome of the nerve cord of an animal that can walk or fly. The base connectome consists of neuronal morphologies and the connections between them. However, in order to efficiently navigate and understand this connectome, it is crucial to have a system of annotations that systematically categorises and names neurons, linking them to the existing literature. In this paper we describe the comprehensive annotation of the VNC connectome, first by a system of hierarchical coarse annotations, then by grouping left-right and serially homologous neurons and eventually by defining systematic cell types for the intrinsic interneurons and sensory neurons of the VNC; descending and motor neurons are typed in (Cheong et al., 2023). We assign a sensory modality to over 5000 sensory neurons, cluster them by connectivity, and identify serially homologous cell types and a layered organisation likely corresponding to peripheral topography. We identify the developmental neuroblast of origin of the large majority of VNC neurons and confirm that (in most cases) all secondary neurons of each hemilineage express a single neurotransmitter. Neuroblast hemilineages are serially repeated along the segments of the nerve cord and generally exhibit consistent hemilineage-to-hemilineage connectivity across neuromeres, supporting the idea that hemilineages are a major organisational feature of the VNC. We also find that more than a third of individual neurons belong to serially homologous cell types, which were crucial for identifying motor neurons and sensory neurons across leg neuropils. Categorising interneurons by their neuropil innervation patterns provides an additional organisation axis. Over half of the intrinsic neurons of the VNC appear dedicated to the legs, with the majority restricted to single leg neuropils; in contrast, inhibitory interneurons connecting different leg neuropils, especially those crossing the midline, appear rarer than anticipated by standard models of locomotor circuitry. Our annotations are being released as part of the neuprint.janelia.org web application and also serve as the basis of programmatic analysis of the connectome through dedicated tools that we describe in this paper.
Animal wings deform during flight in ways that can enhance lift, facilitate flight control, and mitigate damage. Monitoring the structural and aerodynamic state of the wing is challenging because deformations are passive, and the flow fields are unsteady; it requires distributed mechanosensors that respond to local airflow and strain on the wing. Without a complete map of the sensor arrays, it is impossible to model control strategies underpinned by them. Here, we present the first systematic characterization of mechanosensors on the dragonfly's wings: morphology, distribution, and wiring. By combining a cross-species survey of sensor distribution with quantitative neuroanatomy and a high-fidelity finite element analysis, we show that the mechanosensors are well placed to perceive features of the wing dynamics relevant to flight. This work describes the wing sensory apparatus in its entirety and advances our understanding of the sensorimotor loop that facilitates exquisite flight control in animals with highly deformable wings.
Animals evolved in complex environments, producing a wide range of behaviors, including navigation, foraging, prey capture, and conspecific interactions, which vary over timescales ranging from milliseconds to days. Historically, these behaviors have been the focus of study for ecology and ethology, while systems neuroscience has largely focused on short timescale behaviors that can be repeated thousands of times and occur in highly artificial environments. Thanks to recent advances in machine learning, miniaturization, and computation, it is newly possible to study freely moving animals in more natural conditions while applying systems techniques: performing temporally specific perturbations, modeling behavioral strategies, and recording from large numbers of neurons while animals are freely moving. The authors of this review are a group of scientists with deep appreciation for the common aims of systems neuroscience, ecology, and ethology. We believe it is an extremely exciting time to be a neuroscientist, as we have an opportunity to grow as a field, to embrace interdisciplinary, open, collaborative research to provide new insights and allow researchers to link knowledge across disciplines, species, and scales. Here we discuss the origins of ethology, ecology, and systems neuroscience in the context of our own work and highlight how combining approaches across these fields has provided fresh insights into our research. We hope this review facilitates some of these interactions and alliances and helps us all do even better science, together.
Forkhead transcription factors play critical roles in leukocyte homeostasis. To study further the immunological functions of Foxo1, we generated mice that selectively lack Foxo1 in T cells (Foxo1(flox/flox) Lck.cre(+)conditional knockout mice (cKO)). Although thymocyte development appeared relatively normal, Foxo1 cKO mice harbored significantly increased percentages of mature single positive T cells in the thymus as compared with WT mice, yet possessed smaller lymph nodes and spleens that contained fewer T cells. Foxo1 cKO T cells were not more prone to apoptosis, but instead were characterized by a CD62L(lo) CCR7(lo) CD44(hi) surface phenotype, a poorly populated lymphoid compartment in the periphery, and were relatively refractory to TCR stimulation, all of which were associated with reduced expression of Sell, Klf2, Ccr7, and S1pr1. Thus, Foxo1 is critical for naïve T cells to populate the peripheral lymphoid organs by coordinating a molecular program that maintains homeostasis and regulates trafficking.
The bacterial injectisome is a syringe-shaped macromolecular nanomachine utilized by many pathogenic Gram-negative bacteria, including the causative agents of plague, typhoid fever, whooping cough, sexually transmitted infections and major nosocomial infections. Bacterial proteins destined for self-assembly and host-cell targeting are translocated by the injectisome in a process known as type III secretion (T3S). The core structure is the ~4 MDa needle complex (NC), built on a foundation of three highly oligomerized ring-forming proteins that create a hollow scaffold spanning the bacterial inner membrane (IM) (24-mer ring-forming proteins PrgH and PrgK in the Salmonella entericaserovar Typhimurium Salmonella pathogenicity island 1 (SPI-1) type III secretion system (T3SS)) and outer membrane (OM) (15-mer InvG, a member of the broadly conserved secretin pore family). An internalized helical needle projects from the NC and bacterium, ultimately forming a continuous passage to the host, for delivery of virulence effectors. Here, we have captured snapshots of the entire prototypical SPI-1 NC in four distinct needle assembly states, including near-atomic resolution, and local reconstructions in the absence and presence of the needle. These structures reveal the precise localization and molecular interactions of the internalized SpaPQR ‘export apparatus’ complex, which is intimately encapsulated and stabilized within the IM rings in the manner of a nanodisc, and to which the PrgJ rod directly binds and functions as an initiator and anchor of needle polymerization. We also describe the molecular details of the extensive and continuous coupling interface between the OM secretin and IM rings, which is remarkably facilitated by a localized 16-mer stoichiometry in the periplasmic-most coupling domain of the otherwise 15-mer InvG oligomer.
Multiple methods have been introduced over the past 30 years to identify the genomic insertion sites of transposable elements and other DNA elements that integrate into genomes. However, each of these methods suffer from limitations that can frustrate attempts to map multiple insertions in a single genome and to map insertions in genomes of high complexity that contain extensive repetitive DNA. I introduce a new method for transposon mapping that is simple to perform, can accurately map multiple insertions per genome, and generates long sequence reads that facilitate mapping to complex genomes. The method, called TagMap, for Tagmentation-based Mapping, relies on a modified Tn5 tagmentation protocol with a single tagmentation adaptor followed by PCR using primers specific to the tranposable element and the adaptor sequence. Several minor modifications to normal tagmentation reagents and protocols allow easy and rapid preparation of TagMap libraries. Short read sequencing starting from the adaptor sequence generates oriented reads that flank and are oriented toward the transposable element insertion site. The convergent orientation of adjacent reads at the insertion site allows straightforward prediction of the precise insertion site(s). A Linux shell script is provided to identify insertion sites from fastq files.
It is now possible to routinely determine atomic resolution structures by electron cryo-microscopy (cryoEM), facilitated in part by the method known as micro electron-diffraction (MicroED). Since its initial demonstration in 2013, MicroED has helped determine a variety of protein structures ranging in molecular weight from a few hundred Daltons to several hundred thousand Daltons. Some of these structures were novel while others were previously known. The resolutions of structures obtained thus far by MicroED range from 3.2Å to 1.0Å, with most better than 2.5Å. Crystals of various sizes and shapes, with different space group symmetries, and with a range of solvent content have all been studied by MicroED. The wide range of crystals explored to date presents the community with a landscape of opportunity for structure determination from nano crystals. Here we summarize the lessons we have learned during the first few years of MicroED, and from our attempts at the first ab initio structure determined by the method. We re-evaluate theoretical considerations in choosing the appropriate crystals for MicroED and for extracting the most meaning out of measured data. With more laboratories worldwide adopting the technique, we speculate what the first decade might hold for MicroED.