Filter
Associated Lab
- Aguilera Castrejon Lab (16) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (64) Apply Ahrens Lab filter
- Aso Lab (40) Apply Aso Lab filter
- Baker Lab (38) Apply Baker Lab filter
- Betzig Lab (113) Apply Betzig Lab filter
- Beyene Lab (13) Apply Beyene Lab filter
- Bock Lab (17) Apply Bock Lab filter
- Branson Lab (53) Apply Branson Lab filter
- Card Lab (42) Apply Card Lab filter
- Cardona Lab (64) Apply Cardona Lab filter
- Chklovskii Lab (13) Apply Chklovskii Lab filter
- Clapham Lab (15) 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 (63) 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 (149) 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 (169) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (6) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (63) 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 (7) Apply O'Shea Lab filter
- Otopalik Lab (13) Apply Otopalik Lab filter
- Pachitariu Lab (48) 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 (31) 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 (51) Apply Turaga Lab filter
- Turner Lab (38) Apply Turner Lab filter
- Vale Lab (7) Apply Vale Lab filter
- Voigts Lab (3) Apply Voigts Lab filter
- Wang (Meng) Lab (21) 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 (3) Apply FIB-SEM Technology filter
- Fly Descending Interneuron (11) 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 (46) Apply GENIE filter
- Integrative Imaging (4) 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 (124) Apply 2025 filter
- 2024 (216) 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
4106 Publications
Showing 1541-1550 of 4106 resultsHaloalkane dehalogenase (HD) catalyzes the hydrolysis of haloalkanes via a covalent enzyme-substrate intermediate. Fusing a target protein to an HD variant that cannot hydrolyze the intermediate enables labeling of the target protein with a haloalkane in cellulo. The utility of extant probes is hampered, however, by background fluorescence as well as limited membrane permeability. Here, we report on the synthesis and use of a fluorogenic affinity label that, after unmasking by an intracellular esterase, labels an HD variant in cellulo. Labeling is rapid and specific, as expected from the reliance upon enzymic catalysts and the high membrane permeance of the probe both before and after unmasking. Most notably, even high concentrations of the fluorogenic affinity label cause minimal background fluorescence without a need to wash the cells. We envision that such fluorogenic affinity labels, which enlist catalysis by two cellular enzymes, will find utility in pulse-chase experiments, high-content screening, and numerous other protocols.
Traditional small-molecule fluorophores are always fluorescent. This attribute can obscure valuable information in biological experiments. Here, we report on a versatile "latent" fluorophore that overcomes this limitation. At the core of the latent fluorophore is a derivative of rhodamine in which one nitrogen is modified as a urea. That modification enables rhodamine to retain half of its fluorescence while facilitating conjugation to a target molecule. The other nitrogen of rhodamine is modified with a "trimethyl lock", which enables fluorescence to be unmasked fully by a single user-designated chemical reaction. An esterase-reactive latent fluorophore was synthesized in high yield and attached covalently to a cationic protein. The resulting conjugate was not fluorescent in the absence of esterases. The enzymatic activity of esterases in endocytic vesicles and the cytosol educed fluorescence, enabling the time-lapse imaging of endocytosis into live human cells and thus providing unprecedented spatiotemporal resolution of this process. The modular design of this "fluorogenic label" enables the facile synthesis of an ensemble of small-molecule probes for the illumination of numerous biochemical and cell biological processes.
The spatiotemporal fluorescence imaging of biological processes requires effective tools to label intracellular biomolecules in living systems. This review presents a brief overview of recent labeling strategies that permits one to make protein and RNA strongly fluorescent using synthetic fluorogenic probes. Genetically encoded tags selectively binding the exogenously applied molecules ensure high labeling selectivity, while high imaging contrast is achieved using fluorogenic chromophores that are fluorescent only when bound to their cognate tag, and are otherwise dark. Beyond avoiding the need for removal of unbound synthetic dyes, these approaches allow the development of sophisticated imaging assays, and open exciting prospects for advanced imaging, particularly for multiplexed imaging and super-resolution microscopy.
Fluorescence imaging has become an indispensable tool in cell and molecular biology. GFP‐like fluorescent proteins have revolutionized fluorescence microscopy, giving experimenters exquisite control over the localization and specificity of tagged constructs. However, these systems present certain drawbacks and as such, alternative systems based on a fluorogenic interaction between a chromophore and a protein have been developed. While these systems are initially designed as fluorescent labels, they also present new opportunities for the development of novel labeling and detection strategies. This review focuses on new labeling protocols, actuation methods, and biosensors based on fluorogenic protein systems. This review presents recently developed fluorogenic protein‐based systems made of a protein tag incorporating an external chromophore. Beyond addressing some limitations of classical fluorescent proteins, these unique systems present characteristics than can be used to creatively push the limits of biological imaging, in particular for the development of new labeling protocols, actuation methods and biosensors.
Cellular esterases catalyze many essential biological functions by performing hydrolysis reactions on diverse substrates. The promiscuity of esterases complicates assignment of their substrate preferences and biological functions. To identify universal factors controlling esterase substrate recognition, we designed a 32-member structure-activity relationship (SAR) library of fluorogenic ester substrates and used this library to systematically interrogate esterase preference for chain length, branching patterns, and polarity to differentiate common classes of esterase substrates. Two structurally homologous bacterial esterases were screened against this library, refining their previously broad overlapping substrate specificity. esterase ybfF displayed a preference for γ-position thioethers and ethers, whereas Rv0045c from displayed a preference for branched substrates with and without thioethers. We determined that this substrate differentiation was partially controlled by individual substrate selectivity residues Tyr119 in ybfF and His187 in Rv0045c; reciprocal substitution of these residues shifted each esterase's substrate preference. This work demonstrates that the selectivity of esterases is tuned based on transition state stabilization, identifies thioethers as an underutilized functional group for esterase substrates, and provides a rapid method for differentiating structural isozymes. This SAR library could have multi-faceted future applications including in vivo imaging, biocatalyst screening, molecular fingerprinting, and inhibitor design.
For more than 100 years, the fruit fly has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula , that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.
Flies and other insects use vision to regulate their groundspeed in flight, enabling them to fly in varying wind conditions. Compared with mechanosensory modalities, however, vision requires a long processing delay ( 100 ms) that might introduce instability if operated at high gain. Flies also sense air motion with their antennae, but how this is used in flight control is unknown. We manipulated the antennal function of fruit flies by ablating their aristae, forcing them to rely on vision alone to regulate groundspeed. Arista-ablated flies in flight exhibited significantly greater groundspeed variability than intact flies. We then subjected them to a series of controlled impulsive wind gusts delivered by an air piston and experimentally manipulated antennae and visual feedback. The results show that an antenna-mediated response alters wing motion to cause flies to accelerate in the same direction as the gust. This response opposes flying into a headwind, but flies regularly fly upwind. To resolve this discrepancy, we obtained a dynamic model of the fly’s velocity regulator by fitting parameters of candidate models to our experimental data. The model suggests that the groundspeed variability of arista-ablated flies is the result of unstable feedback oscillations caused by the delay and high gain of visual feedback. The antenna response drives active damping with a shorter delay ( 20 ms) to stabilize this regulator, in exchange for increasing the effect of rapid wind disturbances. This provides insight into flies’ multimodal sensory feedback architecture and constitutes a previously unknown role for the antennae.
Rapidly and selectively modulating the activity of defined neurons in unrestrained animals is a powerful approach in investigating the circuit mechanisms that shape behavior. In Drosophila melanogaster, temperature-sensitive silencers and activators are widely used to control the activities of genetically defined neuronal cell types. A limitation of these thermogenetic approaches, however, has been their poor temporal resolution. Here we introduce FlyMAD (the fly mind-altering device), which allows thermogenetic silencing or activation within seconds or even fractions of a second. Using computer vision, FlyMAD targets an infrared laser to freely walking flies. As a proof of principle, we demonstrated the rapid silencing and activation of neurons involved in locomotion, vision and courtship. The spatial resolution of the focused beam enabled preferential targeting of neurons in the brain or ventral nerve cord. Moreover, the high temporal resolution of FlyMAD allowed us to discover distinct timing relationships for two neuronal cell types previously linked to courtship song.
Filopodia are peripheral F-actin-rich structures that enable cell sensing of the microenvironment. Fascin is an F-actin-bundling protein that plays a key role in stabilizing filopodia to support efficient adhesion and migration. Fascin is also highly up-regulated in human cancers, where it increases invasive cell behavior and correlates with poor patient prognosis. Previous studies have shown that fascin phosphorylation can regulate F-actin bundling, and that this modification can contribute to subcellular fascin localization and function. However, the factors that regulate fascin dynamics within filopodia remain poorly understood. In the current study, we used advanced live-cell imaging techniques and a fascin biosensor to demonstrate that fascin phosphorylation, localization, and binding to F-actin are highly dynamic and dependent on local cytoskeletal architecture in cells in both 2D and 3D environments. Fascin dynamics within filopodia are under the control of formins, and in particular FMNL2, that binds directly to dephosphorylated fascin. Our data provide new insight into control of fascin dynamics at the nanoscale and into the mechanisms governing rapid cytoskeletal adaptation to environmental changes. This filopodia-driven exploration stage may represent an essential regulatory step in the transition from static to migrating cancer cells.
Focal adhesions (FAs) connect inner workings of cell to the extracellular matrix to control cell adhesion, migration and mechanosensing. Previous studies demonstrated that FAs contain three vertical layers, which connect extracellular matrix to the cytoskeleton. By using super-resolution iPALM microscopy, we identify two additional nanoscale layers within FAs, specified by actin filaments bound to tropomyosin isoforms Tpm1.6 and Tpm3.2. The Tpm1.6-actin filaments, beneath the previously identified α-actinin cross-linked actin filaments, appear critical for adhesion maturation and controlled cell motility, whereas the adjacent Tpm3.2-actin filament layer beneath seems to facilitate adhesion disassembly. Mechanistically, Tpm3.2 stabilizes ACF-7/MACF1 and KANK-family proteins at adhesions, and hence targets microtubule plus-ends to FAs to catalyse their disassembly. Tpm3.2 depletion leads to disorganized microtubule network, abnormally stable FAs, and defects in tail retraction during migration. Thus, FAs are composed of distinct actin filament layers, and each may have specific roles in coupling adhesions to the cytoskeleton, or in controlling adhesion dynamics.