Filter
Associated Lab
- Aguilera Castrejon Lab (2) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (55) Apply Ahrens Lab filter
- Aso Lab (40) Apply Aso Lab filter
- Baker Lab (19) Apply Baker Lab filter
- Betzig Lab (101) Apply Betzig Lab filter
- Beyene Lab (9) Apply Beyene Lab filter
- Bock Lab (14) Apply Bock Lab filter
- Branson Lab (51) Apply Branson Lab filter
- Card Lab (37) Apply Card Lab filter
- Cardona Lab (45) Apply Cardona Lab filter
- Chklovskii Lab (10) Apply Chklovskii Lab filter
- Clapham Lab (14) Apply Clapham Lab filter
- Cui Lab (19) Apply Cui Lab filter
- Darshan Lab (8) Apply Darshan Lab filter
- Dickson Lab (32) Apply Dickson Lab filter
- Druckmann Lab (21) Apply Druckmann Lab filter
- Dudman Lab (38) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (4) Apply Egnor Lab filter
- Espinosa Medina Lab (15) Apply Espinosa Medina Lab filter
- Feliciano Lab (8) Apply Feliciano Lab filter
- Fetter Lab (31) Apply Fetter Lab filter
- FIB-SEM Technology (1) Apply FIB-SEM Technology filter
- Fitzgerald Lab (16) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (39) Apply Funke Lab filter
- Gonen Lab (59) Apply Gonen Lab filter
- Grigorieff Lab (34) Apply Grigorieff Lab filter
- Harris Lab (53) Apply Harris Lab filter
- Heberlein Lab (13) Apply Heberlein Lab filter
- Hermundstad Lab (24) Apply Hermundstad Lab filter
- Hess Lab (74) Apply Hess Lab filter
- Ilanges Lab (2) Apply Ilanges Lab filter
- Jayaraman Lab (42) Apply Jayaraman Lab filter
- Ji Lab (33) Apply Ji Lab filter
- Johnson Lab (1) Apply Johnson Lab filter
- Karpova Lab (13) Apply Karpova Lab filter
- Keleman Lab (8) Apply Keleman Lab filter
- Keller Lab (61) Apply Keller Lab filter
- Koay Lab (2) Apply Koay Lab filter
- Lavis Lab (139) Apply Lavis Lab filter
- Lee (Albert) Lab (29) Apply Lee (Albert) Lab filter
- Leonardo Lab (19) Apply Leonardo Lab filter
- Li Lab (4) Apply Li Lab filter
- Lippincott-Schwartz Lab (100) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (2) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (59) Apply Liu (Zhe) Lab filter
- Looger Lab (137) Apply Looger Lab filter
- Magee Lab (31) Apply Magee Lab filter
- Menon Lab (12) Apply Menon Lab filter
- Murphy Lab (6) Apply Murphy Lab filter
- O'Shea Lab (6) Apply O'Shea Lab filter
- Otopalik Lab (1) Apply Otopalik Lab filter
- Pachitariu Lab (36) Apply Pachitariu Lab filter
- Pastalkova Lab (5) Apply Pastalkova Lab filter
- Pavlopoulos Lab (7) Apply Pavlopoulos Lab filter
- Pedram Lab (4) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (45) Apply Reiser Lab filter
- Riddiford Lab (20) Apply Riddiford Lab filter
- Romani Lab (31) Apply Romani Lab filter
- Rubin Lab (107) Apply Rubin Lab filter
- Saalfeld Lab (46) Apply Saalfeld Lab filter
- Satou Lab (1) Apply Satou Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (51) Apply Schreiter Lab filter
- Sgro Lab (1) Apply Sgro Lab filter
- Shroff Lab (31) Apply Shroff Lab filter
- Simpson Lab (18) Apply Simpson Lab filter
- Singer Lab (37) Apply Singer Lab filter
- Spruston Lab (58) Apply Spruston Lab filter
- Stern Lab (73) Apply Stern Lab filter
- Sternson Lab (47) Apply Sternson Lab filter
- Stringer Lab (33) Apply Stringer Lab filter
- Svoboda Lab (131) Apply Svoboda Lab filter
- Tebo Lab (9) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (18) Apply Tillberg Lab filter
- Tjian Lab (17) Apply Tjian Lab filter
- Truman Lab (58) Apply Truman Lab filter
- Turaga Lab (40) Apply Turaga Lab filter
- Turner Lab (28) Apply Turner Lab filter
- Vale Lab (8) Apply Vale Lab filter
- Voigts Lab (3) Apply Voigts Lab filter
- Wang (Meng) Lab (22) Apply Wang (Meng) Lab filter
- Wang (Shaohe) Lab (6) Apply Wang (Shaohe) Lab filter
- Wu Lab (8) Apply Wu Lab filter
- Zlatic Lab (26) Apply Zlatic Lab filter
- Zuker Lab (5) 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 (54) Apply FlyEM filter
- FlyLight (49) Apply FlyLight filter
- GENIE (47) Apply GENIE filter
- Integrative Imaging (6) 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) (27) Apply Tool Translation Team (T3) filter
- Transcription Imaging (45) Apply Transcription Imaging filter
Associated Support Team
- Project Pipeline Support (5) Apply Project Pipeline Support filter
- Anatomy and Histology (18) Apply Anatomy and Histology filter
- Cryo-Electron Microscopy (39) Apply Cryo-Electron Microscopy filter
- Electron Microscopy (17) Apply Electron Microscopy filter
- Gene Targeting and Transgenics (11) Apply Gene Targeting and Transgenics filter
- High Performance Computing (7) Apply High Performance Computing filter
- Integrative Imaging (17) Apply Integrative Imaging filter
- Invertebrate Shared Resource (40) Apply Invertebrate Shared Resource filter
- Janelia Experimental Technology (37) Apply Janelia Experimental Technology filter
- Management Team (1) Apply Management Team filter
- Molecular Genomics (15) Apply Molecular Genomics filter
- Primary & iPS Cell Culture (14) Apply Primary & iPS Cell Culture filter
- Project Technical Resources (50) Apply Project Technical Resources filter
- Quantitative Genomics (19) Apply Quantitative Genomics filter
- Scientific Computing (93) Apply Scientific Computing filter
- Viral Tools (14) Apply Viral Tools filter
- Vivarium (7) Apply Vivarium filter
Publication Date
- 2025 (160) Apply 2025 filter
- 2024 (213) Apply 2024 filter
- 2023 (158) Apply 2023 filter
- 2022 (166) Apply 2022 filter
- 2021 (175) Apply 2021 filter
- 2020 (177) Apply 2020 filter
- 2019 (177) Apply 2019 filter
- 2018 (206) Apply 2018 filter
- 2017 (186) Apply 2017 filter
- 2016 (191) Apply 2016 filter
- 2015 (195) Apply 2015 filter
- 2014 (190) Apply 2014 filter
- 2013 (136) Apply 2013 filter
- 2012 (112) Apply 2012 filter
- 2011 (98) Apply 2011 filter
- 2010 (61) Apply 2010 filter
- 2009 (56) Apply 2009 filter
- 2008 (40) Apply 2008 filter
- 2007 (21) Apply 2007 filter
- 2006 (3) Apply 2006 filter
2721 Janelia Publications
Showing 2531-2540 of 2721 resultsGoal-directed animal behaviors are typically composed of sequences of motor actions whose order and timing are critical for a successful outcome. Although numerous theoretical models for sequential action generation have been proposed, few have been supported by the identification of control neurons sufficient to elicit a sequence. Here, we identify a pair of descending neurons that coordinate a stereotyped sequence of engagement actions during Drosophila melanogaster male courtship behavior. These actions are initiated sequentially but persist cumulatively, a feature not explained by existing models of sequential behaviors. We find evidence consistent with a ramp-to-threshold mechanism, in which increasing neuronal activity elicits each action independently at successively higher activity thresholds.
Genetically-encoded calcium indicators (GECIs) facilitate imaging activity of genetically defined neuronal populations in vivo. The high intracellular GECI concentrations required for in vivo imaging are usually achieved by viral gene transfer using adeno-associated viruses. Transgenic expression of GECIs promises important advantages, including homogeneous, repeatable, and stable expression without the need for invasive virus injections. Here we present the generation and characterization of transgenic mice expressing the GECIs GCaMP6s or GCaMP6f under the Thy1 promoter. We quantified GCaMP6 expression across brain regions and neurons and compared to other transgenic mice and AAV-mediated expression. We tested three mouse lines for imaging in the visual cortex in vivo and compared their performance to mice injected with AAV expressing GCaMP6. Furthermore, we show that GCaMP6 Thy1 transgenic mice are useful for long-term, high-sensitivity imaging in behaving mice.
Calcium imaging is commonly used to measure the neural activity of large groups of neurons in mice. Genetically encoded calcium indicators (GECIs) can be delivered for this purpose using non-invasive genetic methods. Compared to viral gene transfer, transgenic targeting of GECIs provides stable long-term expression and obviates the need for invasive viral injections. Transgenic mice expressing the green GECI GCaMP6 are already widely used. Here we present the generation and characterization of transgenic mice expressing the sensitive red GECI jRGECO1a, driven by the Thy1 promoter. Four transgenic lines with different expression patterns showed sufficiently high expression for cellular in vivo imaging. We used two-photon microscopy to characterize visual responses of individual neurons in the visual cortex in vivo. The signal-to-noise ratio in transgenic mice was comparable to, or better than, mice transduced with adeno-associated virus. In addition, we show that Thy1-jRGECO1a transgenic mice are useful for transcranial population imaging and functional mapping using widefield fluorescence microscopy. We also demonstrate imaging of visual responses in retinal ganglion cells in vitro. Thy1-jRGECO1a transgenic mice are therefore a useful addition to the toolbox for imaging activity in intact neural networks.
Structured illumination microscopy (SIM) is widely used for fast, long-term, live-cell super-resolution imaging. However, SIM images can contain substantial artifacts if the sample does not conform to the underlying assumptions of the reconstruction algorithm. Here we describe a simple, easy to implement, process that can be combined with any reconstruction algorithm to alleviate many common SIM reconstruction artifacts and briefly discuss possible extensions.
Kernel regression or classification (also referred to as weighted ε-NN methods in Machine Learning) are appealing for their simplicity and therefore ubiquitous in data analysis. How- ever, practical implementations of kernel regression or classification consist of quantizing or sub-sampling data for improving time efficiency, often at the cost of prediction quality. While such tradeoffs are necessary in practice, their statistical implications are generally not well understood, hence practical implementations come with few performance guaran- tees. In particular, it is unclear whether it is possible to maintain the statistical accuracy of kernel prediction—crucial in some applications—while improving prediction time. The present work provides guiding principles for combining kernel prediction with data- quantization so as to guarantee good tradeoffs between prediction time and accuracy, and in particular so as to approximately maintain the good accuracy of vanilla kernel prediction. Furthermore, our tradeoff guarantees are worked out explicitly in terms of a tuning parameter which acts as a knob that favors either time or accuracy depending on practical needs. On one end of the knob, prediction time is of the same order as that of single-nearest- neighbor prediction (which is statistically inconsistent) while maintaining consistency; on the other end of the knob, the prediction risk is nearly minimax-optimal (in terms of the original data size) while still reducing time complexity. The analysis thus reveals the interaction between the data-quantization approach and the kernel prediction method, and most importantly gives explicit control of the tradeoff to the practitioner rather than fixing the tradeoff in advance or leaving it opaque. The theoretical results are validated on data from a range of real-world application domains; in particular we demonstrate that the theoretical knob performs as expected.
Fluorescence microscopy enables the visualization of cellular morphology, molecular distribution, ion distribution, and their dynamic behaviors during biological processes. Enhancing the signal-to-noise ratio (SNR) in fluorescence imaging improves the quantification accuracy and spatial resolution; however, achieving high SNR at fast image acquisition rates, which is often required to observe cellular dynamics, still remains a challenge. In this study, we developed a technique to rapidly freeze biological cells in milliseconds during optical microscopy observation. Compared to chemical fixation, rapid freezing provides rapid immobilization of samples while more effectively preserving the morphology and conditions of cells. This technique combines the advantages of both live-cell and cryofixation microscopy, i.e., temporal dynamics and high SNR snapshots of selected moments, and is demonstrated by fluorescence and Raman microscopy with high spatial resolution and quantification under low temperature conditions. Furthermore, we also demonstrated that intracellular calcium dynamics can be frozen rapidly and visualized using fluorescent ion indicators, suggesting that ion distribution and conformation of the probe molecules can be fixed both spatially and temporally. These results confirmed that our technique can time-deterministically suspend and visualize cellular dynamics while preserving molecular and ionic states, indicating the potential to provide detailed insights into sample dynamics with improved spatial resolution and temporal accuracy in observations.
Previous implementations of structured-illumination microscopy (SIM) were slow or designed for one-color excitation, sacrificing two unique and extremely beneficial aspects of light microscopy: live-cell imaging in multiple colors. This is especially unfortunate because, among the resolution-extending techniques, SIM is an attractive choice for live-cell imaging; it requires no special fluorophores or high light intensities to achieve twice diffraction-limited resolution in three dimensions. Furthermore, its wide-field nature makes it light-efficient and decouples the acquisition speed from the size of the lateral field of view, meaning that high frame rates over large volumes are possible. Here, we report a previously undescribed SIM setup that is fast enough to record 3D two-color datasets of living whole cells. Using rapidly programmable liquid crystal devices and a flexible 2D grid pattern algorithm to switch between excitation wavelengths quickly, we show volume rates as high as 4 s in one color and 8.5 s in two colors over tens of time points. To demonstrate the capabilities of our microscope, we image a variety of biological structures, including mitochondria, clathrin-coated vesicles, and the actin cytoskeleton, in either HeLa cells or cultured neurons.
Much of systems neuroscience posits the functional importance of brain activity patterns that lack natural scales of sizes, durations, or frequencies. The field has developed prominent, and sometimes competing, explanations for the nature of this scale-free activity. Here, we reconcile these explanations across species and modalities. First, we link estimates of excitation-inhibition (E-I) balance with time-resolved correlation of distributed brain activity. Second, we develop an unbiased method for sampling time series constrained by this time-resolved correlation. Third, we use this method to show that estimates of E-I balance account for diverse scale-free phenomena without need to attribute additional function or importance to these phenomena. Collectively, our results simplify existing explanations of scale-free brain activity and provide stringent tests on future theories that seek to transcend these explanations.
Recording transcriptional histories of a cell would enable deeper understanding of cellular developmental trajectories and responses to external perturbations. Here we describe an engineered protein fiber that incorporates diverse fluorescent marks during its growth to store a ticker tape-like history. An embedded HaloTag reporter incorporates user-supplied dyes, leading to colored stripes that map the growth of each individual fiber to wall clock time. A co-expressed eGFP tag driven by a promoter of interest records a history of transcriptional activation. High-resolution multi-spectral imaging on fixed samples reads the cellular histories, and interpolation of eGFP marks relative to HaloTag timestamps provides accurate absolute timing. We demonstrate recordings of doxycycline-induced transcription in HEK cells and cFos promoter activation in cultured neurons, with a single-cell absolute accuracy of 30-40 minutes over a 12-hour recording. The protein-based ticker tape design we present here could be generalized to achieve massively parallel single-cell recordings of diverse physiological modalities.
In the current model of endothelial barrier regulation, the tyrosine kinase SRC is purported to induce disassembly of endothelial adherens junctions (AJs) via phosphorylation of VE cadherin, and thereby increase junctional permeability. Here, using a chemical biology approach to temporally control SRC activation, we show that SRC exerts distinct time-variant effects on the endothelial barrier. We discovered that the immediate effect of SRC activation was to transiently enhance endothelial barrier function as the result of accumulation of VE cadherin at AJs and formation of morphologically distinct reticular AJs. Endothelial barrier enhancement via SRC required phosphorylation of VE cadherin at Y731. In contrast, prolonged SRC activation induced VE cadherin phosphorylation at Y685, resulting in increased endothelial permeability. Thus, time-variant SRC activation differentially phosphorylates VE cadherin and shapes AJs to fine-tune endothelial barrier function. Our work demonstrates important advantages of synthetic biology tools in dissecting complex signaling systems.