Filter
Associated Lab
- Aguilera Castrejon Lab (14) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (52) 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 (9) Apply Beyene Lab filter
- Bock Lab (17) Apply Bock Lab filter
- Branson Lab (48) Apply Branson Lab filter
- Card Lab (38) Apply Card Lab filter
- Cardona Lab (63) Apply Cardona Lab filter
- Chklovskii Lab (13) Apply Chklovskii Lab filter
- Clapham Lab (11) 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 (27) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (33) Apply Funke Lab filter
- Gonen Lab (91) Apply Gonen Lab filter
- Grigorieff Lab (62) Apply Grigorieff Lab filter
- Harris Lab (57) Apply Harris Lab filter
- Heberlein Lab (94) Apply Heberlein Lab filter
- Hermundstad Lab (21) Apply Hermundstad Lab filter
- Hess Lab (68) Apply Hess Lab filter
- Jayaraman Lab (43) Apply Jayaraman Lab filter
- Ji Lab (32) 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 (131) 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 (155) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (5) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (56) 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 (1) Apply Otopalik Lab filter
- Pachitariu Lab (38) Apply Pachitariu Lab filter
- Pastalkova Lab (18) Apply Pastalkova Lab filter
- Pavlopoulos Lab (19) Apply Pavlopoulos Lab filter
- Pedram Lab (12) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (48) Apply Reiser Lab filter
- Riddiford Lab (44) Apply Riddiford Lab filter
- Romani Lab (40) Apply Romani Lab filter
- Rubin Lab (138) Apply Rubin Lab filter
- Saalfeld Lab (58) Apply Saalfeld Lab filter
- Satou Lab (16) Apply Satou Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (61) Apply Schreiter Lab filter
- Sgro Lab (20) Apply Sgro Lab filter
- Shroff Lab (19) 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 (150) Apply Stern Lab filter
- Sternson Lab (54) Apply Sternson Lab filter
- Stringer Lab (24) Apply Stringer Lab filter
- Svoboda Lab (135) Apply Svoboda Lab filter
- Tebo Lab (30) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (15) 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 (1) Apply Voigts Lab filter
- Wang (Meng) Lab (6) Apply Wang (Meng) Lab filter
- Wang (Shaohe) Lab (20) 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 (1) 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 (48) Apply FlyEM filter
- FlyLight (45) Apply FlyLight filter
- GENIE (38) 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) (21) Apply Tool Translation Team (T3) filter
- Transcription Imaging (49) Apply Transcription Imaging filter
Publication Date
- 2024 (65) Apply 2024 filter
- 2023 (179) Apply 2023 filter
- 2022 (191) Apply 2022 filter
- 2021 (192) Apply 2021 filter
- 2020 (196) Apply 2020 filter
- 2019 (199) Apply 2019 filter
- 2018 (232) Apply 2018 filter
- 2017 (214) Apply 2017 filter
- 2016 (209) Apply 2016 filter
- 2015 (250) Apply 2015 filter
- 2014 (236) Apply 2014 filter
- 2013 (194) Apply 2013 filter
- 2012 (189) 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
3837 Publications
Showing 51-60 of 3837 resultsHistone-lysine N-methyltransferase 2 (KMT2) methyltransferases play critical roles in gene regulation, cell differentiation, animal development, and human diseases. KMT2 biological roles are often attributed to their methyltransferase activities on lysine 4 of histone H3 (H3K4). However, recent data indicate that KMT2 proteins also possess non-enzymatic functions. In this review, we discuss the current understanding of KMT2 family, with a focus on their enzymatic activity-dependent and -independent functions. Six mammalian KMT2 proteins of three subgroups, KMT2A/B (MLL1/2), KMT2C/D (MLL3/4), and KMT2F/G (SETD1A/B or SET1A/B), have shared and distinct protein domains, catalytic substrates, genomic localizations, and associated complex subunits. Recent studies have revealed the central role of KMT2C/D in enhancer regulation, differentiation, and development and have highlighted KMT2C/D enzymatic activity-dependent and independent roles in mouse embryonic development and cell differentiation. Catalytic dependent and independent roles for KMT2A/B and KMT2F/G in gene regulation, differentiation, and development are less understood. Finally, we provide our perspectives and lay out future research directions that may help advance the investigation on enzymatic activity-dependent and -independent biological roles and working mechanisms of KMT2 methyltransferases.
Lysosomes are active sites to integrate cellular metabolism and signal transduction. A collection of proteins enriched at lysosomes mediate these metabolic and signaling functions. Both lysosomal metabolism and lysosomal signaling have been linked with longevity regulation; however, how lysosomes adjust their protein composition to accommodate this regulation remains unclear. Using large-scale proteomic profiling, we systemically profiled lysosome- enriched proteomes in association with different longevity mechanisms. We further discovered the lysosomal recruitment of AMPK and nucleoporin proteins and their requirements for longevity in response to increased lysosomal lipolysis. Through comparative proteomic analyses of lysosomes from different tissues and labeled with different markers, we discovered lysosomal heterogeneity across tissues as well as the specific enrichment of the Ragulator complex on Cystinosin positive lysosomes. Together, this work uncovers lysosomal proteome heterogeneity at different levels and provides resources for understanding the contribution of lysosomal proteome dynamics in modulating signal transduction, organelle crosstalk and organism longevity.
Living in dynamic environments such as the social domain, where interaction with others determines the reproductive success of individuals, requires the ability to recognize opportunities to obtain natural rewards and cope with challenges that are associated with achieving them. As such, actions that promote survival and reproduction are reinforced by the brain reward system, whereas coping with the challenges associated with obtaining these rewards is mediated by stress-response pathways, the activation of which can impair health and shorten lifespan. While much research has been devoted to understanding mechanisms underlying the way by which natural rewards are processed by the reward system, less attention has been given to the consequences of failure to obtain a desirable reward. As a model system to study the impact of failure to obtain a natural reward, we used the well-established courtship suppression paradigm in Drosophila melanogaster as means to induce repeated failures to obtain sexual reward in male flies. We discovered that beyond the known reduction in courtship actions caused by interaction with non-receptive females, repeated failures to mate induce a stress response characterized by persistent motivation to obtain the sexual reward, reduced male-male social interaction, and enhanced aggression. This frustrative-like state caused by the conflict between high motivation to obtain sexual reward and the inability to fulfill their mating drive impairs the capacity of rejected males to tolerate stressors such as starvation and oxidative stress. We further show that sensitivity to starvation and enhanced social arousal is mediated by the disinhibition of a small population of neurons that express receptors for the fly homologue of neuropeptide Y. Our findings demonstrate for the first time the existence of social stress in flies and offers a framework to study mechanisms underlying the crosstalk between reward, stress, and reproduction in a simple nervous system that is highly amenable to genetic manipulation.
Anchoring goals to spatial representations enables flexible navigation in both animals and artificial agents. However, using this strategy can be challenging in novel environments, when both spatial and goal representations must be acquired quickly and simultaneously. Here, we propose a framework for how Drosophila use their internal representation of head direction to build a goal heading representation upon selective thermal reinforcement. We show that flies in a well-established operant visual learning paradigm use stochastically generated fixations and directed saccades to express heading preferences, and that compass neurons, which represent flies’ head direction, are required to modify these preferences based on reinforcement. We describe how flies’ ability to quickly map their surroundings and adapt their behavior to the rules of their environment may rest on a behavioral policy whose parameters are flexible but whose form and dependence on head direction and goal representations are genetically encoded in the modular structure of their circuits. Using a symmetric visual setting, which predictably alters the dynamics of the head direction system, enabled us to describe how interactions between the evolving representations of head direction and goal impact behavior. We show how a policy tethered to these two internal representations can facilitate rapid learning of new goal headings, drive more exploitative behavior about stronger goal headings, and ensure that separate learning processes involved in mapping the environment and forming goals within that environment remain consistent with one another. Many of the mechanisms we outline may be broadly relevant for rapidly adaptive behavior driven by internal representations.
iBiology Courses provide trainees with just-in-time learning resources to become effective researchers. These courses can help scientists build core research skills, plan their research projects and careers, and learn from scientists with diverse backgrounds.
Understanding the diversification of mammalian cell lineages is an essential to embryonic development, organ regeneration and tissue engineering. Shortly after implantation in the uterus, the pluripotent cells of the mammalian epiblast generate the three germ layers: ectoderm, mesoderm and endoderm1. Although clonal analyses suggest early specification of epiblast cells towards particular cell lineages2–4, single-cell transcriptomes do not identify lineage-specific markers in the epiblast5–11 and thus, the molecular regulation of such specification remains unknow. Here, we studied the epigenetic landscape of single epiblast cells, which revealed lineage priming towards endoderm, ectoderm or mesoderm. Unexpectedly, epiblast cells with mesodermal priming show a strong signature for the endothelial/endocardial fate, suggesting early specification of this lineage aside from other mesoderm. Through clonal analysis and live imaging, we show that endothelial precursors show early lineage divergence from the rest of mesodermal derivatives. In particular, cardiomyocytes and endocardial cells show limited lineage relationship, despite being temporally and spatially co-recruited during gastrulation. Furthermore, analysing the live tracks of single cells through unsupervised classification of cell migratory activity, we found early behavioral divergence of endothelial precursors shortly after the onset of mesoderm migration towards the cardiogenic area. These results provide a new model for the phenotypically silent specification of mammalian cell lineages in pluripotent cells of the epiblast and modify current knowledge on the sequence and timing of cardiovascular lineages diversification.
Determining cell identities in imaging sequences is an important yet challenging task. The conventional method for cell identification is via cell tracking, which is complex and can be time-consuming. In this study, we propose an innovative approach to cell identification during early C. elegans embryogenesis using machine learning. We employed random forest, MLP, and LSTM models, and tested cell classification accuracy on 3D time-lapse confocal datasets spanning the first 4 hours of embryogenesis. By leveraging a small number of spatial-temporal features of individual cells, including cell trajectory and cell fate information, our models achieve an accuracy of over 90%, even with limited data. We also determine the most important feature contributions and can interpret these features in the context of biological knowledge. Our research demonstrates the success of predicting cell identities in 4D imaging sequences directly from simple spatio-temporal features.
The visual allure of microscopy makes it an intuitively powerful research tool. Intuition, however, can easily obscure or distort the reality of the information contained in an image. Common cognitive biases, combined with institutional pressures that reward positive research results, can quickly skew a microscopy project towards upholding, rather than rigorously challenging, a hypothesis. The impact of these biases on a variety of research topics is well known. What might be less appreciated are the many forms in which bias can permeate a microscopy experiment. Even well-intentioned researchers are susceptible to bias, which must therefore be actively recognized to be mitigated. Importantly, although image quantification has increasingly become an expectation, ostensibly to confront subtle biases, it is not a guarantee against bias and cannot alone shield an experiment from cognitive distortions. Here, we provide illustrative examples of the insidiously pervasive nature of bias in microscopy experiments - from initial experimental design to image acquisition, analysis and data interpretation. We then provide suggestions that can serve as guard rails against bias.
Background Many Drosophila species use acoustic communication during courtship and studies of these communication systems have provided insight into neurobiology, behavioral ecology, ethology, and evolution. Recording Drosophila courtship sounds and associated behavior is challenging, especially at high throughput, and previously designed devices are relatively expensive and complex to assemble. Results We present construction plans for a modular system utilizing mostly off-the-shelf, relatively inexpensive components that provides simultaneous high-resolution audio and video recording of 96 isolated or paired Drosophila individuals. We provide open-source control software to record audio and video. We designed high intensity LED arrays that can be used to perform optogenetic activation and inactivation of labelled neurons. The basic design can be modified to facilitate novel study designs or to record insects larger than Drosophila. Fewer than 96 microphones can be used in the system if the full array is not required or to reduce costs. Implications Our hardware design and software provide an improved platform for reliable and comparatively inexpensive high-throughput recording of Drosophila courtship acoustic and visual behavior and perhaps for recording acoustic signals of other small animals.
Chemotactic bacteria not only navigate chemical gradients, but also shape their environments by consuming and secreting attractants. Investigating how these processes influence the dynamics of bacterial populations has been challenging because of a lack of experimental methods for measuring spatial profiles of chemoattractants in real time. Here, we use a fluorescent sensor for aspartate to directly measure bacterially generated chemoattractant gradients during collective migration. Our measurements show that the standard Patlak-Keller-Segel model for collective chemotactic bacterial migration breaks down at high cell densities. To address this, we propose modifications to the model that consider the impact of cell density on bacterial chemotaxis and attractant consumption. With these changes, the model explains our experimental data across all cell densities, offering new insight into chemotactic dynamics. Our findings highlight the significance of considering cell density effects on bacterial behavior, and the potential for fluorescent metabolite sensors to shed light on the complex emergent dynamics of bacterial communities.