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 (71) 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 (58) 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 (141) 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
3920 Publications
Showing 811-820 of 3920 resultsThe Influenza A virus genome consists of eight negative sense, single-stranded RNA segments. Although it has been established that most virus particles contain a single copy of each of the eight viral RNAs, the packaging selection mechanism remains poorly understood. Influenza viral RNAs are synthesized in the nucleus, exported into the cytoplasm and travel to the plasma membrane where viral budding and genome packaging occurs. Due to the difficulties in analyzing associated vRNPs while preserving information about their positions within the cell, it has remained unclear how and where during cellular trafficking the viral RNAs of different segments encounter each other. Using a multicolor single-molecule sensitivity fluorescence in situ hybridization (smFISH) approach, we have quantitatively monitored the colocalization of pairs of influenza viral RNAs in infected cells. We found that upon infection, the viral RNAs from the incoming particles travel together until they reach the nucleus. The viral RNAs were then detected in distinct locations in the nucleus; they are then exported individually and initially remain separated in the cytoplasm. At later time points, the different viral RNA segments gather together in the cytoplasm in a microtubule independent manner. Viral RNAs of different identities colocalize at a high frequency when they are associated with Rab11 positive vesicles, suggesting that Rab11 positive organelles may facilitate the association of different viral RNAs. Using engineered influenza viruses lacking the expression of HA or M2 protein, we showed that these viral proteins are not essential for the colocalization of two different viral RNAs in the cytoplasm. In sum, our smFISH results reveal that the viral RNAs travel together in the cytoplasm before their arrival at the plasma membrane budding sites. This newly characterized step of the genome packaging process demonstrates the precise spatiotemporal regulation of the infection cycle.
The GAL4-UAS system has proven its versatility in studying the function and expression patterns of neurons the Drosophila central nervous system. Although the GAL4 system has been used for 25 years, recent genetic intersectional tools have enabled genetic targeting of very small numbers of neurons aiding in the understanding of their function. This split-GAL4 system is extremely powerful for studying neuronal morphology and the neural basis of animal behavior. However, choosing lines to intersect that have overlapping patterns restricted to one to a few neurons has been cumbersome. This challenge is now growing as the collections of GAL4 driver lines has increased. Here we present a new method and software plug-in for Fiji to dramatically improve the speed of querying large databases of potential lines to intersect and aid in the split-GAL4 creation. We also provide pre-computed datasets for the Janelia GAL4 (5,738 lines) and VT GAL4 (7,429 lines) of the Drosophila central nervous system (CNS). The tool reduced our split-GAL4 creation effort dramatically.
A recent study reports a novel form of lateral inhibition between photoreceptors supporting colour vision in the vinegar fly, Drosophila melanogaster.
Wide-field motion-sensitive neurons in the lobula plate (lobula plate tangential cells, LPTCs) of the fly have been studied for decades. However, it has never been conclusively shown which cells constitute their major presynaptic elements. LPTCs are supposed to be rendered directionally selective by integrating excitatory as well as inhibitory input from many local motion detectors. Based on their stratification in the different layers of the lobula plate, the columnar cells T4 and T5 are likely candidates to provide some of this input. To study their role in motion detection, we performed whole-cell recordings from LPTCs in Drosophila with T4 and T5 cells blocked using two different genetically encoded tools. In these flies, motion responses were abolished, while flicker responses largely remained. We thus demonstrate that T4 and T5 cells indeed represent those columnar cells that provide directionally selective motion information to LPTCs. Contrary to previous assumptions, flicker responses seem to be largely mediated by a third, independent pathway. This work thus represents a further step towards elucidating the complete motion detection circuitry of the fly.
Motor systems flexibly implement diverse motor programs to pattern behavioral sequences, yet their neural underpinnings remain unclear. Here, we investigated the neural circuit mechanisms of flexible courtship behavior in Drosophila. Courting males alternately produce two types of courtship song. By recording calcium signals in the ventral nerve cord (VNC) in behaving flies, we found that different songs are produced by activating overlapping neural populations with distinct motor functions in a combinatorial manner. Recordings from the brain suggest that song is driven by two descending pathways – one defines when to sing and the other specifies what song to sing. Connectomic analysis reveals that these “when” and “what” descending pathways provide structured input to VNC neurons with different motor functions. These results suggest that dynamic changes in the activation patterns of descending pathways drive different combinations of motor modules, thereby flexibly switching between different motor actions.
Programmed genome rearrangements in the unicellular eukaryote Oxytricha trifallax produce a transcriptionally active somatic nucleus from a copy of its germline nucleus during development. This process eliminates noncoding sequences that interrupt coding regions in the germline genome, and joins over 225,000 remaining DNA segments, some of which require inversion or complex permutation to build functional genes. This dynamic genomic organization permits some single DNA segments in the germline to contribute to multiple, distinct somatic genes via alternative processing. Like alternative mRNA splicing, the combinatorial assembly of DNA segments contributes to genetic variation and facilitates the evolution of new genes. In this study, we use comparative genomic analysis to demonstrate that the emergence of alternative DNA splicing is associated with the origin of new genes. Short duplications give rise to alternative gene segments that are spliced to the shared gene segments. Alternative gene segments evolve faster than shared, constitutive segments. Genes with shared segments frequently have different expression profiles, permitting functional divergence. This study reports alternative DNA splicing as a mechanism of new gene origination, illustrating how the process of programmed genome rearrangement gives rise to evolutionary innovation.
Methods for the selective and reproducible expression of genetically encoded tools in targeted subsets of cells are required to facilitate studies of neuronal development, connectivity, and function in living animals. In the absence of techniques for synthesizing promoters that target defined cell groups, current methods exploit the regulatory elements of endogenous genes to achieve specificity of transgene expression. However, single promoters often have expression patterns too broad to pinpoint the functional roles of specific neurons. In this review, we describe emerging combinatorial techniques that make transgene expression contingent not upon a single promoter, but upon two or more promoters. Although only a few such techniques are currently available, recent developments promise rapid growth in this area in the coming years.
Integrin alpha M (ITGAM; CD11b) is a component of the macrophage-1 antigen complex, which mediates leukocyte adhesion, migration and phagocytosis as part of the immune system. We previously identified a missense polymorphism, rs1143679 (R77H), strongly associated with systemic lupus erythematosus (SLE). However, the molecular mechanisms of this variant are incompletely understood. A meta-analysis of published and novel data on 28 439 individuals with European, African, Hispanic and Asian ancestries reinforces genetic association between rs1143679 and SLE [Pmeta = 3.60 × 10(-90), odds ratio (OR) = 1.76]. Since rs1143679 is in the most active region of chromatin regulation and transcription factor binding in ITGAM, we quantitated ITGAM RNA and surface protein levels in monocytes from patients with each rs1143679 genotype. We observed that transcript levels significantly decreased for the risk allele ('A') relative to the non-risk allele ('G'), in a dose-dependent fashion: ('AA' < 'AG' < 'GG'). CD11b protein levels in patients' monocytes were directly correlated with RNA levels. Strikingly, heterozygous individuals express much lower (average 10- to 15-fold reduction) amounts of the 'A' transcript than 'G' transcript. We found that the non-risk sequence surrounding rs1143679 exhibits transcriptional enhancer activity in vivo and binds to Ku70/80, NFKB1 and EBF1 in vitro, functions that are significantly reduced with the risk allele. Mutant CD11b protein shows significantly reduced binding to fibrinogen and vitronectin, relative to non-risk, both in purified protein and in cellular models. This two-pronged contribution (nucleic acid- and protein-level) of the rs1143679 risk allele to decreasing ITGAM activity provides insight into the molecular mechanisms of its potent association with SLE.
A distance regulation method has been developed to enhance the reliability, versatility, and ease of use of near-field scanning optical microscopy (NSOM). The method relies on the detection of shear forces between the end of a near-field probe and the sample of interest. The system can be used solely for distance regulation in NSOM, for simultaneous shear force and near-field imaging, or for shear force microscopy alone. In the latter case, uncoated optical fiber probes are found to yield images with consistently high resolution.
Commentary: To exploit the evanescent field that is the source of high resolution in near-field microscopy, the probe must be exceptionally close to the sample: 10 nm away for 30-50 nm resolution. Here we introduced a distance regulation mechanism based on transverse shear forces between the end of a dithered near-field probe and the sample, which permitted even samples of modest topography to be imaged. Simple, reliable, noninvasive, and applicable to a wide range of samples from whole fixed cells to semiconductor devices, shear force microscopy was a key enabling technology for near-field optics, and soon widely implemented.
While fluorescence microscopy has proven to be an exceedingly useful tool in bioscience, it is difficult to offer simultaneous high resolution, fast speed, large volume, and good biocompatibility in a single imaging technique. Thus, when determining the image data required to quantitatively test a complex biological hypothesis, it often becomes evident that multiple imaging techniques are necessary. Recent years have seen an explosion in development of novel fluorescence microscopy techniques, each of which features a unique suite of capabilities. In this Technical Perspective, we highlight recent studies to illustrate the benefits, and often the necessity, of combining multiple fluorescence microscopy modalities. We provide guidance in choosing optimal technique combinations to effectively address a biological question. Ultimately, we aim to promote a more well-rounded approach in designing fluorescence microscopy experiments, leading to more robust quantitative insight.