Filter
Associated Lab
- Aguilera Castrejon Lab (16) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (64) Apply Ahrens Lab filter
- Aso Lab (40) Apply Aso Lab filter
- Baker Lab (38) Apply Baker Lab filter
- Betzig Lab (112) Apply Betzig Lab filter
- Beyene Lab (13) Apply Beyene Lab filter
- Bock Lab (17) Apply Bock Lab filter
- Branson Lab (52) Apply Branson Lab filter
- Card Lab (40) Apply Card Lab filter
- Cardona Lab (63) Apply Cardona Lab filter
- Chklovskii Lab (13) Apply Chklovskii Lab filter
- Clapham Lab (14) Apply Clapham Lab filter
- Cui Lab (19) Apply Cui Lab filter
- Darshan Lab (12) Apply Darshan Lab filter
- Dennis Lab (1) Apply Dennis Lab filter
- Dickson Lab (46) Apply Dickson Lab filter
- Druckmann Lab (25) Apply Druckmann Lab filter
- Dudman Lab (50) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (11) Apply Egnor Lab filter
- Espinosa Medina Lab (19) Apply Espinosa Medina Lab filter
- Feliciano Lab (7) Apply Feliciano Lab filter
- Fetter Lab (41) Apply Fetter Lab filter
- Fitzgerald Lab (29) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (38) Apply Funke Lab filter
- Gonen Lab (91) Apply Gonen Lab filter
- Grigorieff Lab (62) Apply Grigorieff Lab filter
- Harris Lab (60) Apply Harris Lab filter
- Heberlein Lab (94) Apply Heberlein Lab filter
- Hermundstad Lab (26) Apply Hermundstad Lab filter
- Hess Lab (76) Apply Hess Lab filter
- Ilanges Lab (2) Apply Ilanges Lab filter
- Jayaraman Lab (46) Apply Jayaraman Lab filter
- Ji Lab (33) Apply Ji Lab filter
- Johnson Lab (6) Apply Johnson Lab filter
- Kainmueller Lab (19) Apply Kainmueller Lab filter
- Karpova Lab (14) Apply Karpova Lab filter
- Keleman Lab (13) Apply Keleman Lab filter
- Keller Lab (76) Apply Keller Lab filter
- Koay Lab (18) Apply Koay Lab filter
- Lavis Lab (148) Apply Lavis Lab filter
- Lee (Albert) Lab (34) Apply Lee (Albert) Lab filter
- Leonardo Lab (23) Apply Leonardo Lab filter
- Li Lab (27) Apply Li Lab filter
- Lippincott-Schwartz Lab (167) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (6) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (61) Apply Liu (Zhe) Lab filter
- Looger Lab (138) Apply Looger Lab filter
- Magee Lab (49) Apply Magee Lab filter
- Menon Lab (18) Apply Menon Lab filter
- Murphy Lab (13) Apply Murphy Lab filter
- O'Shea Lab (6) Apply O'Shea Lab filter
- Otopalik Lab (13) Apply Otopalik Lab filter
- Pachitariu Lab (46) Apply Pachitariu Lab filter
- Pastalkova Lab (18) Apply Pastalkova Lab filter
- Pavlopoulos Lab (19) Apply Pavlopoulos Lab filter
- Pedram Lab (15) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (51) Apply Reiser Lab filter
- Riddiford Lab (44) Apply Riddiford Lab filter
- Romani Lab (43) Apply Romani Lab filter
- Rubin Lab (143) Apply Rubin Lab filter
- Saalfeld Lab (62) Apply Saalfeld Lab filter
- Satou Lab (16) Apply Satou Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (67) Apply Schreiter Lab filter
- Sgro Lab (20) Apply Sgro Lab filter
- Shroff Lab (29) Apply Shroff Lab filter
- Simpson Lab (23) Apply Simpson Lab filter
- Singer Lab (80) Apply Singer Lab filter
- Spruston Lab (93) Apply Spruston Lab filter
- Stern Lab (156) Apply Stern Lab filter
- Sternson Lab (54) Apply Sternson Lab filter
- Stringer Lab (33) Apply Stringer Lab filter
- Svoboda Lab (135) Apply Svoboda Lab filter
- Tebo Lab (33) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (21) Apply Tillberg Lab filter
- Tjian Lab (64) Apply Tjian Lab filter
- Truman Lab (88) Apply Truman Lab filter
- Turaga Lab (49) Apply Turaga Lab filter
- Turner Lab (37) 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 (25) Apply Wang (Shaohe) Lab filter
- Wu Lab (9) Apply Wu Lab filter
- Zlatic Lab (28) Apply Zlatic Lab filter
- Zuker Lab (25) 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 (49) Apply FlyLight filter
- GENIE (45) 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 (49) Apply Transcription Imaging filter
Publication Date
- 2025 (72) Apply 2025 filter
- 2024 (223) Apply 2024 filter
- 2023 (163) Apply 2023 filter
- 2022 (193) Apply 2022 filter
- 2021 (194) Apply 2021 filter
- 2020 (196) Apply 2020 filter
- 2019 (202) Apply 2019 filter
- 2018 (232) Apply 2018 filter
- 2017 (217) Apply 2017 filter
- 2016 (209) Apply 2016 filter
- 2015 (252) Apply 2015 filter
- 2014 (236) Apply 2014 filter
- 2013 (194) Apply 2013 filter
- 2012 (190) Apply 2012 filter
- 2011 (190) Apply 2011 filter
- 2010 (161) Apply 2010 filter
- 2009 (158) Apply 2009 filter
- 2008 (140) Apply 2008 filter
- 2007 (106) Apply 2007 filter
- 2006 (92) Apply 2006 filter
- 2005 (67) Apply 2005 filter
- 2004 (57) Apply 2004 filter
- 2003 (58) Apply 2003 filter
- 2002 (39) Apply 2002 filter
- 2001 (28) Apply 2001 filter
- 2000 (29) Apply 2000 filter
- 1999 (14) Apply 1999 filter
- 1998 (18) Apply 1998 filter
- 1997 (16) Apply 1997 filter
- 1996 (10) Apply 1996 filter
- 1995 (18) Apply 1995 filter
- 1994 (12) Apply 1994 filter
- 1993 (10) Apply 1993 filter
- 1992 (6) Apply 1992 filter
- 1991 (11) Apply 1991 filter
- 1990 (11) Apply 1990 filter
- 1989 (6) Apply 1989 filter
- 1988 (1) Apply 1988 filter
- 1987 (7) Apply 1987 filter
- 1986 (4) Apply 1986 filter
- 1985 (5) Apply 1985 filter
- 1984 (2) Apply 1984 filter
- 1983 (2) Apply 1983 filter
- 1982 (3) Apply 1982 filter
- 1981 (3) Apply 1981 filter
- 1980 (1) Apply 1980 filter
- 1979 (1) Apply 1979 filter
- 1976 (2) Apply 1976 filter
- 1973 (1) Apply 1973 filter
- 1970 (1) Apply 1970 filter
- 1967 (1) Apply 1967 filter
Type of Publication
4064 Publications
Showing 91-100 of 4064 resultsThe recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviours. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing-a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations.
The rewarding properties of drugs contribute to the development of abuse and addiction. We developed a new assay for investigating the motivational properties of ethanol in the genetically tractable model Drosophila melanogaster. Flies learned to associate cues with ethanol intoxication and, although transiently aversive, the experience led to a long-lasting attraction for the ethanol-paired cue, implying that intoxication is rewarding. Temporally blocking transmission in dopaminergic neurons revealed that flies require activation of these neurons to express, but not develop, conditioned preference for ethanol-associated cues. Moreover, flies acquired, consolidated and retrieved these rewarding memories using distinct sets of neurons in the mushroom body. Finally, mutations in scabrous, encoding a fibrinogen-related peptide that regulates Notch signaling, disrupted the formation of memories for ethanol reward. Our results thus establish that Drosophila can be useful for understanding the molecular, genetic and neural mechanisms underling the rewarding properties of ethanol.
Prenatal exposure to ethanol in humans results in a wide range of developmental abnormalities, including growth deficiency, developmental delay, reduced brain size, permanent neurobehavioral abnormalities and fetal death. Here we describe the use of Drosophila melanogaster as a model for exploring the effects of ethanol exposure on development and behavior. We show that developmental ethanol exposure causes reduced viability, developmental delay and reduced adult body size. We find that flies reared on ethanol-containing food have smaller brains and imaginal discs, which is due to reduced cell division rather than increased apoptosis. Additionally, we show that, as in mammals, flies reared on ethanol have altered responses to ethanol vapor exposure as adults, including increased locomotor activation, resistance to the sedating effects of the drug and reduced tolerance development upon repeated ethanol exposure. We have found that the developmental and behavioral defects are largely due to the effects of ethanol on insulin signaling; specifically, a reduction in Drosophila insulin-like peptide (Dilp) and insulin receptor expression. Transgenic expression of Dilp proteins in the larval brain suppressed both the developmental and behavioral abnormalities displayed by ethanol-reared adult flies. Our results thus establish Drosophila as a useful model system to uncover the complex etiology of fetal alcohol syndrome.
Conditional expression of hairpin constructs in Drosophila is a powerful method to disrupt the activity of single genes with a spatial and temporal resolution that is impossible, or exceedingly difficult, using classical genetic methods. We previously described a method (Ni et al. 2008) whereby RNAi constructs are targeted into the genome by the phiC31-mediated integration approach using Vermilion-AttB-Loxp-Intron-UAS-MCS (VALIUM), a vector that contains vermilion as a selectable marker, an attB sequence to allow for phiC31-targeted integration at genomic attP landing sites, two pentamers of UAS, the hsp70 core promoter, a multiple cloning site, and two introns. As the level of gene activity knockdown associated with transgenic RNAi depends on the level of expression of the hairpin constructs, we generated a number of derivatives of our initial vector, called the "VALIUM" series, to improve the efficiency of the method. Here, we report the results from the systematic analysis of these derivatives and characterize VALIUM10 as the most optimal vector of this series. A critical feature of VALIUM10 is the presence of gypsy insulator sequences that boost dramatically the level of knockdown. We document the efficacy of VALIUM as a vector to analyze the phenotype of genes expressed in the nervous system and have generated a library of 2282 constructs targeting 2043 genes that will be particularly useful for studies of the nervous system as they target, in particular, transcription factors, ion channels, and transporters.
BACKGROUND: The best studied insect-symbiont system is that of aphids and their primary bacterial endosymbiont Buchnera aphidicola. Buchnera inhabits specialized host cells called bacteriocytes, provides nutrients to the aphid and has co-speciated with its aphid hosts for the past 150 million years. We have used a single microarray to examine gene expression in the pea aphid, Acyrthosiphon pisum, and its resident Buchnera. Very little is known of gene expression in aphids, few studies have examined gene expression in Buchnera, and no study has examined simultaneously the expression profiles of a host and its symbiont. Expression profiling of aphids, in studies such as this, will be critical for assigning newly discovered A. pisum genes to functional roles. In particular, because aphids possess many genes that are absent from Drosophila and other holometabolous insect taxa, aphid genome annotation efforts cannot rely entirely on homology to the best-studied insect systems. Development of this dual-genome array represents a first attempt to characterize gene expression in this emerging model system. RESULTS: We chose to examine heat shock response because it has been well characterized both in Buchnera and in other insect species. Our results from the Buchnera of A. pisum show responses for the same gene set as an earlier study of heat shock response in Buchnera for the host aphid Schizaphis graminum. Additionally, analyses of aphid transcripts showed the expected response for homologs of known heat shock genes as well as responses for several genes with unknown functional roles. CONCLUSION: We examined gene expression under heat shock of an insect and its bacterial symbiont in a single assay using a dual-genome microarray. Further, our results indicate that microarrays are a useful tool for inferring functional roles of genes in A. pisum and other insects and suggest that the pea aphid genome may contain many gene paralogs that are differentially regulated.
A theoretical formulation for optically active sum frequency generation (OA-SFG) from isotropic chiral solutions was proposed for molecules with a chiral side chain and an intrinsically achiral chromophore. Adapting an electron correlation model first proposed by Höhn and Weigang for linear optical activity, we presented a dynamic coupling model for OA-SFG near the electronic resonance of the achiral chromophore. As a demonstration, we used this model to explain the observed OA-SFG spectra of a series of amino acids near the electronic resonance of the intrinsically achiral carboxyl group. Our model shows that the nonlinear chiroptical response comes about by the through-space correlative electronic interactions between the chiral side chain and the achiral chromophore, and its magnitude is determined by the position and orientation of the bonds that make up the chiral side chain. Using the bond polarizability values in the literature and the conformations of amino acids obtained from calculation, we were able to reproduce the relative OA-SFG strength from a series of amino acids.
Transcription factor (TF)-directed enhanceosome assembly constitutes a fundamental regulatory mechanism driving spatiotemporal gene expression programs during animal development. Despite decades of study, we know little about the dynamics or order of events animating TF assembly at cis-regulatory elements in living cells and the long-range molecular "dialog" between enhancers and promoters. Here, combining genetic, genomic, and imaging approaches, we characterize a complex long-range enhancer cluster governing Krüppel-like factor 4 (Klf4) expression in naïve pluripotency. Genome editing by CRISPR/Cas9 revealed that OCT4 and SOX2 safeguard an accessible chromatin neighborhood to assist the binding of other TFs/cofactors to the enhancer. Single-molecule live-cell imaging uncovered that two naïve pluripotency TFs, STAT3 and ESRRB, interrogate chromatin in a highly dynamic manner, in which SOX2 promotes ESRRB target search and chromatin-binding dynamics through a direct protein-tethering mechanism. Together, our results support a highly dynamic yet intrinsically ordered enhanceosome assembly to maintain the finely balanced transcription program underlying naïve pluripotency.
Far‐red emitting fluorescent labels are highly desirable for spectral multiplexing and deep tissue imaging. Here, we describe the generation of frFAST (far‐red Fluorescence Activating and absorption Shifting Tag), a 14‐kDa monomeric protein that forms a bright far‐red fluorescent assembly with (4‐hydroxy‐3‐methoxy‐phenyl)allylidene rhodanine (HPAR‐3OM). As HPAR‐3OM is essentially non‐ fluorescent in solution and in cells, frFAST can be imaged with high contrast in presence of free HPAR‐3OM, which allowed the rapid and efficient imaging of frFAST fusions in live cells, zebrafish embryo/larvae and chicken embryo. Beyond enabling genetic encoding of far‐red fluorescence, frFAST allowed the design of a far‐ red chemogenetic reporter of protein‐protein interactions, demonstrating its great potential for the design of innovative far‐red emitting biosensors.
Here we design and optimize a genetically encoded fluorescent indicator, iAChSnFR, for the ubiquitous neurotransmitter acetylcholine, based on a bacterial periplasmic binding protein. iAChSnFR shows large fluorescence changes, rapid rise and decay kinetics, and insensitivity to most cholinergic drugs. iAChSnFR revealed large transients in a variety of slice and in vivo preparations in mouse, fish, fly and worm. iAChSnFR will be useful for the study of acetylcholine in all animals.