Filter
Associated Lab
- Aguilera Castrejon Lab (15) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (56) Apply Ahrens Lab filter
- Aso Lab (39) Apply Aso Lab filter
- Baker Lab (38) Apply Baker Lab filter
- Betzig Lab (110) Apply Betzig Lab filter
- Beyene Lab (10) Apply Beyene Lab filter
- Bock Lab (17) Apply Bock Lab filter
- Branson Lab (48) 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 (12) 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 (46) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (11) Apply Egnor Lab filter
- Espinosa Medina Lab (16) Apply Espinosa Medina Lab filter
- Feliciano Lab (6) Apply Feliciano Lab filter
- Fetter Lab (41) Apply Fetter Lab filter
- Fitzgerald Lab (28) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (34) Apply Funke Lab filter
- Gonen Lab (91) Apply Gonen Lab filter
- Grigorieff Lab (62) Apply Grigorieff Lab filter
- Harris Lab (58) Apply Harris Lab filter
- Heberlein Lab (94) Apply Heberlein Lab filter
- Hermundstad Lab (22) Apply Hermundstad Lab filter
- Hess Lab (72) Apply Hess Lab filter
- Ilanges Lab (1) Apply Ilanges Lab filter
- Jayaraman Lab (44) 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 (75) Apply Keller Lab filter
- Koay Lab (16) Apply Koay Lab filter
- Lavis Lab (136) Apply Lavis Lab filter
- Lee (Albert) Lab (34) Apply Lee (Albert) Lab filter
- Leonardo Lab (23) Apply Leonardo Lab filter
- Li Lab (25) Apply Li Lab filter
- Lippincott-Schwartz Lab (161) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (5) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (59) Apply Liu (Zhe) Lab filter
- Looger Lab (137) 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 (4) Apply O'Shea Lab filter
- Otopalik Lab (13) Apply Otopalik Lab filter
- Pachitariu Lab (41) Apply Pachitariu Lab filter
- Pastalkova Lab (18) Apply Pastalkova Lab filter
- Pavlopoulos Lab (19) Apply Pavlopoulos Lab filter
- Pedram Lab (14) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (49) Apply Reiser Lab filter
- Riddiford Lab (44) Apply Riddiford Lab filter
- Romani Lab (40) Apply Romani Lab filter
- Rubin Lab (139) Apply Rubin Lab filter
- Saalfeld Lab (60) Apply Saalfeld Lab filter
- Satou Lab (16) Apply Satou Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (62) Apply Schreiter Lab filter
- Sgro Lab (20) Apply Sgro Lab filter
- Shroff Lab (23) Apply Shroff Lab filter
- Simpson Lab (23) Apply Simpson Lab filter
- Singer Lab (80) Apply Singer Lab filter
- Spruston Lab (91) Apply Spruston Lab filter
- Stern Lab (152) Apply Stern Lab filter
- Sternson Lab (54) Apply Sternson Lab filter
- Stringer Lab (29) Apply Stringer Lab filter
- Svoboda Lab (135) Apply Svoboda Lab filter
- Tebo Lab (31) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (17) Apply Tillberg Lab filter
- Tjian Lab (64) Apply Tjian Lab filter
- Truman Lab (88) Apply Truman Lab filter
- Turaga Lab (46) Apply Turaga Lab filter
- Turner Lab (35) Apply Turner Lab filter
- Vale Lab (6) Apply Vale Lab filter
- Voigts Lab (2) Apply Voigts Lab filter
- Wang (Meng) Lab (9) Apply Wang (Meng) Lab filter
- Wang (Shaohe) Lab (24) 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 (5) Apply CellMap filter
- COSEM (3) Apply COSEM 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 (51) Apply FlyEM filter
- FlyLight (46) Apply FlyLight filter
- GENIE (40) Apply GENIE filter
- Integrative Imaging (1) Apply Integrative Imaging filter
- Larval Olympiad (2) Apply Larval Olympiad filter
- MouseLight (16) Apply MouseLight filter
- NeuroSeq (1) Apply NeuroSeq filter
- ThalamoSeq (1) Apply ThalamoSeq filter
- Tool Translation Team (T3) (24) Apply Tool Translation Team (T3) filter
- Transcription Imaging (49) Apply Transcription Imaging filter
Publication Date
- 2024 (145) Apply 2024 filter
- 2023 (175) Apply 2023 filter
- 2022 (192) Apply 2022 filter
- 2021 (193) 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
3924 Publications
Showing 3541-3550 of 3924 resultsWe review recent progress in neural probes for brain recording, with a focus on the Neuropixels platform. Historically the number of neurons’ recorded simultaneously, follows a Moore’s law like behavior, with numbers doubling every 6.7 years. Using traditional techniques of probe fabrication, continuing to scale up electrode densities is very challenging. We describe a custom CMOS process technology that enables electrode counts well beyond 1000 electrodes; with the aim to characterize large neural populations with single neuron spatial precision and millisecond timing resolution. This required integrating analog and digital circuitry with the electrode array, making it a standalone integrated electrophysiology recording system. Input referred noise and power per channel is 7.5µV and <50µW respectively to ensure tissue heating <1°C. This approach enables doubling the number of measured neurons every 12 months.
In both mammalian and insect models of ethanol intoxication, high doses of ethanol induce motor impairment and eventually sedation. Sensitivity to the sedative effects of ethanol is inversely correlated with risk for alcoholism. However, the genes regulating ethanol sensitivity are largely unknown. Based on a previous genetic screen in Drosophila for ethanol sedation mutants, we identified a novel gene, tank (CG15626), the homolog of the mammalian tumor suppressor EI24/PIG8, which has a strong role in regulating ethanol sedation sensitivity. Genetic and behavioral analyses revealed that tank acts in the adult nervous system to promote ethanol sensitivity. We localized the function of tank in regulating ethanol sensitivity to neurons within the pars intercerebralis that have not been implicated previously in ethanol responses. We show that acutely manipulating the activity of all tank-expressing neurons, or of pars intercerebralis neurons in particular, alters ethanol sensitivity in a sexually dimorphic manner, since neuronal activation enhanced ethanol sedation in males, but not females. Finally, we provide anatomical evidence that tank-expressing neurons form likely synaptic connections with neurons expressing the neural sex determination factor fruitless (fru), which have been implicated recently in the regulation of ethanol sensitivity. We suggest that a functional interaction with fru neurons, many of which are sexually dimorphic, may account for the sex-specific effect induced by activating tank neurons. Overall, we have characterized a novel gene and corresponding set of neurons that regulate ethanol sensitivity in Drosophila.
Genetic studies of the fruit fly Drosophila have revealed a hierarchy of segmentation genes (maternal, gap, pair-rule and HOX) that subdivide the syncytial blastoderm into sequentially finer-scale coordinates. Within this hierarchy, the pair-rule genes translate gradients of information into periodic stripes of expression. How pair-rule genes function during the progressive mode of segmentation seen in short and intermediate-germ insects is an ongoing question. Here we report that the nuclear receptor Of’E75A is expressed with double segment periodicity in the head and thorax. In the abdomen, Of’E75A is expressed in a unique pattern during posterior elongation, and briefly resembles a sequence that is typical of pair-rule genes. Depletion of Of’E75A mRNA caused loss of a subset of odd-numbered parasegments, as well as parasegment 6. Because these parasegments straddle segment boundaries, we observe fusions between adjacent segments. Finally, expression of Of’E75A in the blastoderm requires even-skipped, which is a gap gene in Oncopeltus. These data show that the function of Of’E75A during embryogenesis shares many properties with canonical pair-rule genes in other insects. They further suggest that parasegment specification may occur through irregular and episodic pair-rule-like activity.
The Drosophila nucleosome remodeling factor (NURF) is an ISWI-containing chromatin remodeling complex that catalyzes ATP-dependent nucleosome sliding. By sliding nucleosomes, NURF has the ability to alter chromatin structure and regulate transcription. Previous studies have shown that mutation of Drosophila NURF induces melanotic tumors, implicating NURF in innate immune function. Here, we show that NURF mutants exhibit identical innate immune responses to gain-of-function mutants in the Drosophila JAK/STAT pathway. Using microarrays, we identify a common set of target genes that are activated in both mutants. In silico analysis of promoter sequences of these defines a consensus regulatory element comprising a STAT-binding sequence overlapped by a binding-site for the transcriptional repressor Ken. NURF interacts physically and genetically with Ken. Chromatin immunoprecipitation (ChIP) localizes NURF to Ken-binding sites in hemocytes, suggesting that Ken recruits NURF to repress STAT responders. Loss of NURF leads to precocious activation of STAT target genes.
The coordination of growth with nutritional status is essential for proper development and physiology. Nutritional information is mostly perceived by peripheral organs before being relayed to the brain, which modulates physiological responses. Hormonal signaling ensures this organ-to-organ communication, and the failure of endocrine regulation in humans can cause diseases including obesity and diabetes. In Drosophila melanogaster, the fat body (adipose tissue) has been suggested to play an important role in coupling growth with nutritional status. Here, we show that the peripheral tissue-derived peptide hormone CCHamide-2 (CCHa2) acts as a nutrient-dependent regulator of Drosophila insulin-like peptides (Dilps). A BAC-based transgenic reporter revealed strong expression of CCHa2 receptor (CCHa2-R) in insulin-producing cells (IPCs) in the brain. Calcium imaging of brain explants and IPC-specific CCHa2-R knockdown demonstrated that peripheral-tissue derived CCHa2 directly activates IPCs. Interestingly, genetic disruption of either CCHa2 or CCHa2-R caused almost identical defects in larval growth and developmental timing. Consistent with these phenotypes, the expression of dilp5, and the release of both Dilp2 and Dilp5, were severely reduced. Furthermore, transcription of CCHa2 is altered in response to nutritional levels, particularly of glucose. These findings demonstrate that CCHa2 and CCHa2-R form a direct link between peripheral tissues and the brain, and that this pathway is essential for the coordination of systemic growth with nutritional availability. A mammalian homologue of CCHa2-R, Bombesin receptor subtype-3 (Brs3), is an orphan receptor that is expressed in the islet β-cells; however, the role of Brs3 in insulin regulation remains elusive. Our genetic approach in Drosophila melanogaster provides the first evidence, to our knowledge, that bombesin receptor signaling with its endogenous ligand promotes insulin production.
Mapping brain function to brain structure is a fundamental task for neuroscience. For such an endeavour, the Drosophila larva is simple enough to be tractable, yet complex enough to be interesting. It features about 10,000 neurons and is capable of various taxes, kineses and Pavlovian conditioning. All its neurons are currently being mapped into a light-microscopical atlas, and Gal4 strains are being generated to experimentally access neurons one at a time. In addition, an electron microscopic reconstruction of its nervous system seems within reach. Notably, this electron microscope-based connectome is being drafted for a stage 1 larva - because stage 1 larvae are much smaller than stage 3 larvae. However, most behaviour analyses have been performed for stage 3 larvae because their larger size makes them easier to handle and observe. It is therefore warranted to either redo the electron microscopic reconstruction for a stage 3 larva or to survey the behavioural faculties of stage 1 larvae. We provide the latter. In a community-based approach we called the Ol1mpiad, we probed stage 1 Drosophila larvae for free locomotion, feeding, responsiveness to substrate vibration, gentle and nociceptive touch, burrowing, olfactory preference and thermotaxis, light avoidance, gustatory choice of various tastants plus odour-taste associative learning, as well as light/dark-electric shock associative learning. Quantitatively, stage 1 larvae show lower scores in most tasks, arguably because of their smaller size and lower speed. Qualitatively, however, stage 1 larvae perform strikingly similar to stage 3 larvae in almost all cases. These results bolster confidence in mapping brain structure and behaviour across developmental stages.
We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes- neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.
The genome of the severe acute respiratory syndrome-associated coronavirus (SARS-CoV) contains eight open reading frames (ORFs) that encode novel proteins. These accessory proteins are dispensable for in vitro and in vivo replication and thus may be important for other aspects of virus-host interactions. We investigated the functions of the largest of the accessory proteins, the ORF 3a protein, using a 3a-deficient strain of SARS-CoV. Cell death of Vero cells after infection with SARS-CoV was reduced upon deletion of ORF 3a. Electron microscopy of infected cells revealed a role for ORF 3a in SARS-CoV induced vesicle formation, a prominent feature of cells from SARS patients. In addition, we report that ORF 3a is both necessary and sufficient for SARS-CoV-induced Golgi fragmentation and that the 3a protein accumulates and localizes to vesicles containing markers for late endosomes. Finally, overexpression of ADP-ribosylation factor 1 (Arf1), a small GTPase essential for the maintenance of the Golgi apparatus, restored Golgi morphology during infection. These results establish an important role for ORF 3a in SARS-CoV-induced cell death, Golgi fragmentation, and the accumulation of intracellular vesicles.
Histological staining of wild-type and sevenless transgenic Drosophila melanogaster bearing Rh3-lacZ fusion genes permits the selective visualization of polarization-sensitive R7 and R8 photoreceptor cells located along the dorsal anterior eye margin. Diffusion of beta-galactosidase throughout these cells reveals that they project long axons to the two most peripheral synaptic target rows of the dorsal posterior medulla, defining a specialized marginal zone of this optic lobe. Comparison of the staining patterns of marginal and nonmarginal Rh3-lacZ-expressing photoreceptor cells in the same histological preparations suggest that the marginal cells possess morphologically specialized axons and synaptic terminals. These findings are discussed with reference to the neuroanatomy of the corresponding dorsal marginal eye and optic lobe regions of the larger dipterans Musca and Calliphora, and in relation to the ability of Drosophila to orient to polarized light.