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 3111-3120 of 3924 resultsMessenger RNA decay measurements are typically performed on a population of cells. However, this approach cannot reveal sufficient complexity to provide information on mechanisms that may regulate mRNA degradation, possibly on short timescales. To address this deficiency, we measured cell cycle-regulated decay in single yeast cells using single-molecule FISH. We found that two genes responsible for mitotic progression, SWI5 and CLB2, exhibit a mitosis-dependent mRNA stability switch. Their transcripts are stable until mitosis, when a precipitous decay eliminates the mRNA complement, preventing carryover into the next cycle. Remarkably, the specificity and timing of decay is entirely regulated by their promoter, independent of specific cis mRNA sequences. The mitotic exit network protein Dbf2p binds to SWI5 and CLB2 mRNAs cotranscriptionally and regulates their decay. This work reveals the promoter-dependent control of mRNA stability, a regulatory mechanism that could be employed by a variety of mRNAs and organisms.
Gene regulation relies on transcription factors (TFs) exploring the nucleus searching their targets. So far, most studies have focused on how fast TFs diffuse, underestimating the role of nuclear architecture. We implemented a single-molecule tracking assay to determine TFs dynamics. We found that c-Myc is a global explorer of the nucleus. In contrast, the positive transcription elongation factor P-TEFb is a local explorer that oversamples its environment. Consequently, each c-Myc molecule is equally available for all nuclear sites while P-TEFb reaches its targets in a position-dependent manner. Our observations are consistent with a model in which the exploration geometry of TFs is restrained by their interactions with nuclear structures and not by exclusion. The geometry-controlled kinetics of TFs target-search illustrates the influence of nuclear architecture on gene regulation, and has strong implications on how proteins react in the nucleus and how their function can be regulated in space and time.
Transcription is an inherently stochastic, noisy, and multi-step process, in which fluctuations at every step can cause variations in RNA synthesis, and affect physiology and differentiation decisions in otherwise identical cells. However, it has been an experimental challenge to directly link the stochastic events at the promoter to transcript production. Here we established a fast fluorescence in situ hybridization (fastFISH) method that takes advantage of intrinsically unstructured nucleic acid sequences to achieve exceptionally fast rates of specific hybridization (\~{}10e7 M(-1)s(-1)), and allows deterministic detection of single nascent transcripts. Using a prototypical RNA polymerase, we demonstrated the use of fastFISH to measure the kinetic rates of promoter escape, elongation, and termination in one assay at the single-molecule level, at sub-second temporal resolution. The principles of fastFISH design can be used to study stochasticity in gene regulation, to select targets for gene silencing, and to design nucleic acid nanostructures. DOI: http://dx.doi.org/10.7554/eLife.01775.001.
Fluorescent in situ hybridization (FISH) allows the quantification of single mRNAs in budding yeast using fluorescently labeled single-stranded DNA probes, a wide-field epifluorescence microscope and a spot-detection algorithm. Fixed yeast cells are attached to coverslips and hybridized with a mixture of FISH probes, each conjugated to several fluorescent dyes. Images of cells are acquired in 3D and maximally projected for single-molecule analysis. Diffraction-limited labeled mRNAs are observed as bright fluorescent spots and can be quantified using a spot-detection algorithm. FISH preserves the spatial distribution of cellular RNA distribution within the cell and the stochastic fluctuations in individual cells that can lead to phenotypic differences within a clonal population. This information, however, is lost if the RNA content is measured on a population of cells by using reverse transcriptase PCR, microarrays or high-throughput sequencing. The FISH procedure and image acquisition described here can be completed in 3 d.
Reconstruction of the axonal projection patterns of single neurons has been an important tool for understanding both the diversity of cell types in the brain and the logic of information flow between brain regions. Innovative approaches now enable the complete reconstruction of axonal projection patterns of individual neurons with vastly increased throughput. Here we review how advances in genetic, imaging, and computational techniques have been exploited for axonal reconstruction. We also discuss how new innovations could enable the integration of genetic and physiological information with axonal morphology for producing a census of cell types in the mammalian brain at scale. This article is protected by copyright. All rights reserved.
Gene expression analysis from single cells has become increasingly prominent across biological disciplines; thus, it is important to train students in these approaches. Here, we present an experimental and analysis pipeline that we developed for the Neural Systems & Behavior (NS&B) course at Marine Biological Laboratory. Our approach used the Maxwell® 16 LEV simplyRNA Tissue Kit and GoTaq® 2-Step RT-qPCR System for gene expression analysis from single neurons of the crustacean stomatogastric ganglion, a model system to study the generation of rhythmic motor patterns. We used double-stranded RNA to knockdown expression of a putative neuromodulator-activated sodium channel. We then examined the electrophysiological responses to known neuromodulators and confirmed that the response was reduced. Finally, we measured how mRNA levels of several ion channel genes changed in response. Our results provide new insights into the neural mechanisms underlying the generation and modulation of rhythmic motor patterns.
Recent developments in single-molecule localization techniques using photoactivatable fluorescent proteins have allowed the probing of single-molecule motion in a living cell with high specificity, millisecond time resolution, and nanometer spatial resolution. Analyzing the dynamics of individual molecules at high densities in this manner promises to provide new insights into the mechanisms of many biological processes, including protein heterogeneity in the plasma membrane, the dynamics of cytoskeletal flow, and clustering of receptor complexes in response to signaling cues. Here we describe the method of single-molecule tracking photoactivated localization microscopy (sptPALM) and discuss how its use can contribute to a quantitative understanding of fundamental cellular processes.
We present an approach to study macromolecular assemblies by detecting component proteins' characteristic high-resolution projection patterns, calculated from their known 3D structures, in single electron cryo-micrographs. Our method detects single apoferritin molecules in vitreous ice with high specificity and determines their orientation and location precisely. Simulations show that high spatial-frequency information and-in the presence of protein background-a whitening filter are essential for optimal detection, in particular for images taken far from focus. Experimentally, we could detect small viral RNA polymerase molecules, distributed randomly among binding locations, inside rotavirus particles. Based on the currently attainable image quality, we estimate a threshold for detection that is 150 kDa in ice and 300 kDa in 100 nm thick samples of dense biological material.
Synapses are individually operated, computational units for neural communication. To manipulate physically individual synapses in a living organism, we have developed a laser ablation technique for removing single synapses in live neurons in C. elegans that operates without apparent damage to the axon. As a complementary technique, we applied microfluidic immobilization of C. elegans to facilitate long-term fluorescence imaging and observation of neuronal development. With this technique, we directly demonstrated the existence of competition between developing synapses in the HSNL motor neuron.
Size control depends on both the regulation of growth rate and the control over when to stop growing. Studies of Drosophila melanogaster have shown that insulin and Target of Rapamycin (TOR) pathways play principal roles in controlling nutrition-dependent growth rates. A TOR-mediated nutrient sensor in the fat body detects nutrient availability, and regulates insulin signaling in peripheral tissues, which in turn controls larval growth rates. After larvae initiate metamorphosis, growth stops. For growth to stop at the correct time, larvae need to surpass a critical weight. Recently, it was found that the insulin-dependent growth of the prothoracic gland is involved in assessing when critical weight has been reached. Furthermore, mutations in DHR4, a repressor of ecdysone signaling, reduce critical weight and adult size. Thus, the mechanisms that control growth rates converge on those assessing size to ensure that the larvae attain the appropriate size at metamorphosis.