Filter
Associated Lab
- Aguilera Castrejon Lab (16) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (63) 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 (41) 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 (28) 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 (47) 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 (63) 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 (21) Apply Sgro Lab filter
- Shroff Lab (30) 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 (34) 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 (50) 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 (18) 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 (3) 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 (85) Apply 2025 filter
- 2024 (222) Apply 2024 filter
- 2023 (161) 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
4074 Publications
Showing 741-750 of 4074 resultsPersistent internal states are important for maintaining survival-promoting behaviors, such as aggression. In female Drosophila melanogaster, we have previously shown that individually activating either aIPg or pC1d cell types can induce aggression. Here we investigate further the individual roles of these cholinergic, sexually dimorphic cell types, and the reciprocal connections between them, in generating a persistent aggressive internal state. We find that a brief 30-second optogenetic stimulation of aIPg neurons was sufficient to promote an aggressive internal state lasting at least 10 minutes, whereas similar stimulation of pC1d neurons did not. While we previously showed that stimulation of pC1e alone does not evoke aggression, persistent behavior could be promoted through simultaneous stimulation of pC1d and pC1e, suggesting an unexpected synergy of these cell types in establishing a persistent aggressive state. Neither aIPg nor pC1d show persistent neuronal activity themselves, implying that the persistent internal state is maintained by other mechanisms. Moreover, inactivation of pC1d did not significantly reduce aIPg-evoked persistent aggression arguing that the aggressive state did not depend on pC1d-aIPg recurrent connectivity. Our results suggest the need for alternative models to explain persistent female aggression.
The central complex (CX) plays a key role in many higher-order functions of the insect brain including navigation and activity regulation. Genetic tools for manipulating individual cell types, and knowledge of what neurotransmitters and neuromodulators they express, will be required to gain mechanistic understanding of how these functions are implemented. We generated and characterized split-GAL4 driver lines that express in individual or small subsets of about half of CX cell types. We surveyed neuropeptide and neuropeptide receptor expression in the central brain using fluorescent in situ hybridization. About half of the neuropeptides we examined were expressed in only a few cells, while the rest were expressed in dozens to hundreds of cells. Neuropeptide receptors were expressed more broadly and at lower levels. Using our GAL4 drivers to mark individual cell types, we found that 51 of the 85 CX cell types we examined expressed at least one neuropeptide and 21 expressed multiple neuropeptides. Surprisingly, all co-expressed a small neurotransmitter. Finally, we used our driver lines to identify CX cell types whose activation affects sleep, and identified other central brain cell types that link the circadian clock to the CX. The well-characterized genetic tools and information on neuropeptide and neurotransmitter expression we provide should enhance studies of the CX.
The central complex (CX) plays a key role in many higher-order functions of the insect brain including navigation and activity regulation. Genetic tools for manipulating individual cell types, and knowledge of what neurotransmitters and neuromodulators they express, will be required to gain mechanistic understanding of how these functions are implemented. We generated and characterized split-GAL4 driver lines that express in individual or small subsets of about half of CX cell types. We surveyed neuropeptide and neuropeptide receptor expression in the central brain using fluorescent in situ hybridization. About half of the neuropeptides we examined were expressed in only a few cells, while the rest were expressed in dozens to hundreds of cells. Neuropeptide receptors were expressed more broadly and at lower levels. Using our GAL4 drivers to mark individual cell types, we found that 51 of the 85 CX cell types we examined expressed at least one neuropeptide and 21 expressed multiple neuropeptides. Surprisingly, all co-expressed a small neurotransmitter. Finally, we used our driver lines to identify CX cell types whose activation affects sleep, and identified other central brain cell types that link the circadian clock to the CX. The well-characterized genetic tools and information on neuropeptide and neurotransmitter expression we provide should enhance studies of the CX.
Many tools are available to analyse genomes but are often challenging to use in a cell type-specific context. We have developed a method similar to the isolation of nuclei tagged in a specific cell type (INTACT) technique [Deal,R.B. and Henikoff,S. (2010) A simple method for gene expression and chromatin profiling of individual cell types within a tissue. Dev. Cell, 18, 1030-1040; Steiner,F.A., Talbert,P.B., Kasinathan,S., Deal,R.B. and Henikoff,S. (2012) Cell-type-specific nuclei purification from whole animals for genome-wide expression and chromatin profiling. Genome Res., doi:10.1101/gr.131748.111], first developed in plants, for use in Drosophila neurons. We profile gene expression and histone modifications in Kenyon cells and octopaminergic neurons in the adult brain. In addition to recovering known gene expression differences, we also observe significant cell type-specific chromatin modifications. In particular, a small subset of differentially expressed genes exhibits a striking anti-correlation between repressive and activating histone modifications. These genes are enriched for transcription factors, recovering those known to regulate mushroom body identity and predicting analogous regulators of octopaminergic neurons. Our results suggest that applying INTACT to specific neuronal populations can illuminate the transcriptional regulatory networks that underlie neuronal cell identity.
N-Methyl-D-aspartate receptors (NMDA-Rs) are ion channels that are important for synaptic plasticity, which is involved in learning and drug addiction. We show enzymatic targeting of an NMDA-R antagonist, MK801, to a molecularly defined neuronal population with the cell-type-selectivity of genetic methods and the temporal control of pharmacology. We find that NMDA-Rs on dopamine neurons are necessary for cocaine-induced synaptic potentiation, demonstrating that cell type-specific pharmacology can be used to dissect signaling pathways within complex brain circuits.
Multi-neuronal recordings with Ca2+ indicator dyes usually relate [Ca2+]i to action potentials (APs) assuming a stereotypical dependency between the two. However, [Ca2+]i affects and is affected by numerous complex mechanisms that differ from cell type to cell type, from cell compartment to cell compartment. Moreover, [Ca2+]i depends on the specific way a cell is activated. Here we investigate, by combining calcium imaging and on-cell patch clamp recordings, the relationship between APs (spiking) and somatic [Ca2+]i in mitral and granule cells of the olfactory bulb in Xenopus laevis tadpoles. Both cell types exhibit ongoing and odour-modulated [Ca2+]i dynamics. In mitral cells, the occurrence of APs in both spontaneous and odour-evoked situations correlates tightly to step-like [Ca2+]i increases. Moreover, odorant-induced suppression of spontaneous firing couples to a decrease in [Ca2+]i. In contrast, granule cells show a substantial number of uncorrelated events such as increases in [Ca2+]i without APs occurring or APs without any effect upon [Ca2+]i. The correlation between spiking and [Ca2+]i is low, possibly due to somatic NMDAR-mediated and subthreshold voltage-activated Ca2+ entries, and thus does not allow a reliable prediction of APs based on calcium imaging. Taken together, our results demonstrate that the relationship between somatic [Ca2+]i and APs can be cell type specific. Taking [Ca2+]i dynamics as an indicator for spiking activity is thus only reliable if the correlation has been established in the system of interest. When [Ca2+]i and APs are precisely correlated, fast calcium imaging is an extremely valuable tool for determining spatiotemporal patterns of APs in neuronal population.
Molecular and cellular processes in neurons are critical for sensing and responding to energy deficit states, such as during weight-loss. AGRP neurons are a key hypothalamic population that is activated during energy deficit and increases appetite and weight-gain. Cell type-specific transcriptomics can be used to identify pathways that counteract weight-loss, and here we report high-quality gene expression profiles of AGRP neurons from well-fed and food-deprived young adult mice. For comparison, we also analyzed POMC neurons, an intermingled population that suppresses appetite and body weight. We find that AGRP neurons are considerably more sensitive to energy deficit than POMC neurons. Furthermore, we identify cell type-specific pathways involving endoplasmic reticulum-stress, circadian signaling, ion channels, neuropeptides, and receptors. Combined with methods to validate and manipulate these pathways, this resource greatly expands molecular insight into neuronal regulation of body weight, and may be useful for devising therapeutic strategies for obesity and eating disorders.
The striatum shows general topographic organization and regional differences in behavioral functions. How corticostriatal topography differs across cortical areas and cell types to support these distinct functions is unclear. This study contrasted corticostriatal projections from two layer 5 cell types, intratelencephalic (IT-type) and pyramidal tract (PT-type) neurons, using viral vectors expressing fluorescent reporters in Cre-driver mice. Long-range corticostriatal projections from sensory and motor cortex are somatotopic, with a decreasing somatotopic specificity as injections move from sensory to motor and frontal areas. Somatotopic organization differs between IT-type and PT-type neurons, including injections in the same site, with IT-type neurons having higher somatotopic stereotypy than PT-type neurons. Furthermore, IT-type projections from interconnected cortical areas have stronger correlations in corticostriatal targeting than PT-type projections do. Thus, as predicted by a long-standing basal ganglia model, corticostriatal projections of interconnected cortical areas form parallel circuits in basal ganglia-thalamus-cortex loops.
Cellular ultrastructures for signal integration are unknown in any nervous system. The ellipsoid body (EB) of the Drosophila brain is thought to control locomotion upon integration of various modalities of sensory signals with the animal internal status. However, the expected excitatory and inhibitory input convergence that virtually all brain centres exhibit is not yet described in the EB. Based on the EB expression domains of genetic constructs from the choline acetyl transferase (Cha), glutamic acid decarboxylase (GAD) and tyrosine hydroxylase (TH) genes, we identified a new set of neurons with the characteristic ring-shaped morphology (R neurons) which are presumably cholinergic, in addition to the existing GABA-expressing neurons. The R1 morphological subtype is represented in the Cha- and TH-expressing classes. In addition, using transmission electron microscopy, we identified a novel type of synapse in the EB, which exhibits the precise array of two independent active zones over the same postsynaptic dendritic domain, that we named 'agora'. This array is compatible with a coincidence detector role, and represents ~8% of all EB synapses in Drosophila. Presumably excitatory R neurons contribute to coincident synapses. Functional silencing of EB neurons by driving genetically tetanus toxin expression either reduces walking speed or alters movement orientation depending on the targeted R neuron subset, thus revealing functional specialisations in the EB for locomotion control.
Aggressive social interactions are used to compete for limited resources and are regulated by complex sensory cues and the organism's internal state. While both sexes exhibit aggression, its neuronal underpinnings are understudied in females. Here, we identify a population of sexually dimorphic aIPg neurons in the adult central brain whose optogenetic activation increased, and genetic inactivation reduced, female aggression. Analysis of GAL4 lines identified in an unbiased screen for increased female chasing behavior revealed the involvement of another sexually dimorphic neuron, pC1d, and implicated aIPg and pC1d neurons as core nodes regulating female aggression. Connectomic analysis demonstrated that aIPg neurons and pC1d are interconnected and suggest that aIPg neurons may exert part of their effect by gating the flow of visual information to descending neurons. Our work reveals important regulatory components of the neuronal circuitry that underlies female aggressive social interactions and provides tools for their manipulation.