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 (77) 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 (168) 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 (35) 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 (92) Apply 2025 filter
- 2024 (221) Apply 2024 filter
- 2023 (160) 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
4079 Publications
Showing 211-220 of 4079 resultsGene expression depends upon the antagonistic actions of chromatin remodeling complexes. While this has been studied extensively for the enzymes that covalently modify the tails of histones, the mechanism of how ATP-dependent remodeling complexes antagonize each other to maintain the proper level of gene activity is not known. The gene encoding a large subunit of ribonucleotide reductase, RNR3, is regulated by ISW2 and SWI/SNF, complexes that repress and activate transcription, respectively. Here, we studied the functional interactions of these two complexes at RNR3. Deletion of ISW2 causes constitutive recruitment of SWI/SNF, and conditional reexpression of ISW2 causes the repositioning of nucleosomes and reduced SWI/SNF occupancy at RNR3. Thus, ISW2 is required for restriction of access of SWI/SNF to the RNR3 promoter under the uninduced condition. Interestingly, the binding of sequence-specific DNA binding factors and the general transcription machinery are unaffected by the status of ISW2, suggesting that disruption of nucleosome positioning does not cause a nonspecific increase in cross-linking of all factors to RNR3. We provide evidence that ISW2 does not act on SWI/SNF directly but excludes its occupancy by positioning nucleosomes over the promoter. Genetic disruption of nucleosome positioning by other means led to a similar phenotype, linking repressed chromatin structure to SWI/SNF exclusion. Thus, incorporation of promoters into a repressive chromatin structure is essential for prevention of the opportunistic actions of nucleosome-disrupting activities in vivo, providing a novel mechanism for maintaining tight control of gene expression.
We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.
To support cognitive function, the CA3 region of the hippocampus performs computations involving attractor dynamics. Understanding how cellular and ensemble activities of CA3 neurons enable computation is critical for elucidating the neural correlates of cognition. Here we show that CA3 comprises not only classically described pyramid cells with thorny excrescences, but also includes previously unidentified 'athorny' pyramid cells that lack mossy-fiber input. Moreover, the two neuron types have distinct morphological and physiological phenotypes and are differentially modulated by acetylcholine. To understand the contribution of these athorny pyramid neurons to circuit function, we measured cell-type-specific firing patterns during sharp-wave synchronization events in vivo and recapitulated these dynamics with an attractor network model comprising two principal cell types. Our data and simulations reveal a key role for athorny cell bursting in the initiation of sharp waves: transient network attractor states that signify the execution of pattern completion computations vital to cognitive function.
Genetically encoded voltage indicators (GEVIs) allow optical recording of membrane potential from targeted cells in vivo. However, red GEVIs that are compatible with two-photon microscopy and that can be multiplexed in vivo with green reporters like GCaMP, are currently lacking. To address this gap, we explored diverse rhodopsin proteins as GEVIs and engineered a novel GEVI, 2Photron, based on a rhodopsin from the green algae Klebsormidium nitens. 2Photron, combined with two photon ultrafast local volume excitation (ULoVE), enabled multiplexed readout of spiking and subthreshold voltage simultaneously with GCaMP calcium signals in visual cortical neurons of awake, behaving mice. These recordings revealed the cell-specific relationship of spiking and subthreshold voltage dynamics with GCaMP responses, highlighting the challenges of extracting underlying spike trains from calcium imaging.
Zika virus (ZIKV) is an emerging flavivirus that caused thousands of human infections in recent years. Compared to other human flaviviruses, ZIKV replication is not well understood. Using fluorescent, transmission electron, and focused ion beam-scanning electron microscopy, we examined ZIKV replication dynamics in Vero 76 cells and in the brains of infected laboratory mice. We observed the progressive development of a perinuclear flaviviral replication factory both in vitro and in vivo. In vitro, we illustrated the ZIKV lifecycle from particle cell entry to egress. ZIKV particles assembled and aggregated in an induced convoluted membrane structure and ZIKV strain-specific membranous vesicles. While most mature virus particles egressed via membrane budding, some particles also likely trafficked through late endosomes and egressed through membrane abscission. Interestingly, we consistently observed a novel sheet-like virus particle array consisting of a single layer of ZIKV particles. Our study further defines ZIKV replication and identifies a novel hallmark of ZIKV infection.
Recent advances in developing sum frequency generation (SFG) as a novel spectroscopic probe for molecular chirality are reviewed. The basic principle underlying the technique is briefly described, in comparison with circular dichroism (CD). The significantly better sensitivity of the technique than CD is pointed out, and the reason is discussed. Bi-naphthol (BN) and amino acids are used as representatives for two different types of chiral molecules; the measured chirality in their electronic transitions can be understood by two different molecular models, respectively, that are extensions of models developed earlier for CD. Optically active or chiral SFG from vibrational transitions are weaker, but with the help of electronic-vibrational double resonance, the vibrational spectrum of a monolayer of BN has been obtained. Generally, optically active SFG is sufficiently sensitive to be employed to probe in-situ chirality of chiral monolayers and thin films.
The biogenesis of a localization-competent mRNP begins in the nucleus. It is thought that the coordinated action of nuclear and cytoplasmic components of the localization machinery is required for the efficient export and subsequent subcellular localization of these mRNAs in the cytoplasm. Using quantitative poly(A)(+) and transcript-specific fluorescent in situ hybridization, we analyzed different nonessential nucleoporins and nuclear pore-associated proteins for their potential role in mRNA export and localization. We found that Nup60p, a nuclear pore protein located on the nucleoplasmic side of the nuclear pore complex, was required for the mRNA localization pathway. In a Δnup60 background, localized mRNAs were preferentially retained within the nucleus compared to nonlocalized transcripts. However, the export block was only partial and some transcripts could still reach the cytoplasm. Importantly, downstream processes were also affected. Localization of ASH1 and IST2 mRNAs to the bud was impaired in the Δnup60 background, suggesting that the assembly of a localization competent mRNP ("locasome") was inhibited when NUP60 was deleted. These results demonstrate transcript specificity of a nuclear mRNA retention defect and identify a specific nucleoporin as a functional component of the localization pathway in budding yeast.
Chronic stress could induce severe cognitive impairments. Despite extensive investigations in mammalian models, the underlying mechanisms remain obscure. Here, we show that chronic stress could induce dramatic learning and memory deficits in The chronic stress-induced learning deficit (CSLD) is long lasting and associated with other depression-like behaviors. We demonstrated that excessive dopaminergic activity provokes susceptibility to CSLD. Remarkably, a pair of PPL1-γ1pedc dopaminergic neurons that project to the mushroom body (MB) γ1pedc compartment play a key role in regulating susceptibility to CSLD so that stress-induced PPL1-γ1pedc hyperactivity facilitates the development of CSLD. Consistently, the mushroom body output neurons (MBON) of the γ1pedc compartment, MBON-γ1pedc>α/β neurons, are important for modulating susceptibility to CSLD. Imaging studies showed that dopaminergic activity is necessary to provoke the development of chronic stress-induced maladaptations in the MB network. Together, our data support that PPL1-γ1pedc mediates chronic stress signals to drive allostatic maladaptations in the MB network that lead to CSLD.
Dopamine is a mediator of the stimulant properties of drugs of abuse, including ethanol, in mammals and in the fruit fly Drosophila. The neural substrates for the stimulant actions of ethanol in flies are not known. We show that a subset of dopamine neurons and their targets, through the action of the D1-like dopamine receptor DopR, promote locomotor activation in response to acute ethanol exposure. A bilateral pair of dopaminergic neurons in the fly brain mediates the enhanced locomotor activity induced by ethanol exposure, and promotes locomotion when directly activated. These neurons project to the central complex ellipsoid body, a structure implicated in regulating motor behaviors. Ellipsoid body neurons are required for ethanol-induced locomotor activity and they express DopR. Elimination of DopR blunts the locomotor activating effects of ethanol, and this behavior can be restored by selective expression of DopR in the ellipsoid body. These data tie the activity of defined dopamine neurons to D1-like DopR-expressing neurons to form a neural circuit that governs acute responding to ethanol.
The importance of auditory feedback in the development of spoken language in humans is striking. Paradoxically, although auditory-feedback-dependent vocal plasticity has been shown in a variety of taxonomic groups, there is little evidence that our nearest relatives–non-human primates–require auditory feedback for the development of species-typical vocal signals. Because of the apparent lack of developmental plasticity in the vocal production system, neuroscientists have largely ignored the neural mechanisms of non-human primate vocal production and perception. Recently, the absence of evidence for vocal plasticity from developmental studies has been contrasted with evidence for vocal plasticity in adults. We argue that this new evidence makes non-human primate vocal behavior an attractive model system for neurobiological analysis.