Filter
Associated Lab
- Ahrens Lab (2) Apply Ahrens Lab filter
- Betzig Lab (1) Apply Betzig Lab filter
- Beyene Lab (2) Apply Beyene Lab filter
- Card Lab (1) Apply Card Lab filter
- Cardona Lab (1) Apply Cardona Lab filter
- Clapham Lab (1) Apply Clapham Lab filter
- Freeman Lab (1) Apply Freeman Lab filter
- Funke Lab (1) Apply Funke Lab filter
- Grigorieff Lab (1) Apply Grigorieff Lab filter
- Harris Lab (1) Apply Harris Lab filter
- Hess Lab (1) Apply Hess Lab filter
- Keller Lab (2) Apply Keller Lab filter
- Koay Lab (1) Apply Koay Lab filter
- Lavis Lab (1) Apply Lavis Lab filter
- Lippincott-Schwartz Lab (1) Apply Lippincott-Schwartz Lab filter
- Looger Lab (2) Apply Looger Lab filter
- Podgorski Lab (1) Apply Podgorski Lab filter
- Reiser Lab (1) Apply Reiser Lab filter
- Rubin Lab (1) Apply Rubin Lab filter
- Scheffer Lab (1) Apply Scheffer Lab filter
- Svoboda Lab (1) Apply Svoboda Lab filter
- Tebo Lab (1) Apply Tebo Lab filter
- Truman Lab (1) Apply Truman Lab filter
- Wang (Shaohe) Lab (1) Apply Wang (Shaohe) Lab filter
- Zlatic Lab (1) Apply Zlatic Lab filter
Associated Project Team
Publication Date
- November 30, 2018 (2) Apply November 30, 2018 filter
- November 29, 2018 (1) Apply November 29, 2018 filter
- November 28, 2018 (1) Apply November 28, 2018 filter
- November 27, 2018 (1) Apply November 27, 2018 filter
- November 25, 2018 (1) Apply November 25, 2018 filter
- November 23, 2018 (1) Apply November 23, 2018 filter
- November 22, 2018 (1) Apply November 22, 2018 filter
- November 21, 2018 (1) Apply November 21, 2018 filter
- November 19, 2018 (1) Apply November 19, 2018 filter
- November 15, 2018 (1) Apply November 15, 2018 filter
- November 14, 2018 (1) Apply November 14, 2018 filter
- November 13, 2018 (3) Apply November 13, 2018 filter
- November 12, 2018 (1) Apply November 12, 2018 filter
- November 11, 2018 (1) Apply November 11, 2018 filter
- November 6, 2018 (2) Apply November 6, 2018 filter
- November 5, 2018 (2) Apply November 5, 2018 filter
- November 1, 2018 (3) Apply November 1, 2018 filter
- Remove November 2018 filter November 2018
- Remove 2018 filter 2018
Type of Publication
24 Publications
Showing 1-10 of 24 resultsWhole-brain imaging allows for comprehensive functional mapping of distributed neural pathways, but neuronal perturbation experiments are usually limited to targeting predefined regions or genetically identifiable cell types. To complement whole-brain measures of activity with brain-wide manipulations for testing causal interactions, we introduce a system that uses measuredactivity patterns to guide optical perturbations of any subset of neurons in the same fictively behaving larval zebrafish. First, a light-sheet microscope collects whole-brain data that are rapidly analyzed by a distributed computing system to generate functional brain maps. On the basis of these maps, the experimenter can then optically ablate neurons and image activity changes across the brain. We applied this method to characterize contributions of behaviorally tuned populations to the optomotor response. We extended the system to optogenetically stimulate arbitrary subsets of neurons during whole-brain imaging. These open-source methods enable delineating the contributions of neurons to brain-wide circuit dynamics and behavior in individual animals.
Binding between DIP and Dpr neuronal recognition proteins has been proposed to regulate synaptic connections between lamina and medulla neurons in the Drosophila visual system. Each lamina neuron was previously shown to express many Dprs. Here, we demonstrate, by contrast, that their synaptic partners typically express one or two DIPs, with binding specificities matched to the lamina neuron-expressed Dprs. A deeper understanding of the molecular logic of DIP/Dpr interaction requires quantitative studies on the properties of these proteins. We thus generated a quantitative affinity-based DIP/Dpr interactome for all DIP/Dpr protein family members. This revealed a broad range of affinities and identified homophilic binding for some DIPs and some Dprs. These data, along with full-length ectodomain DIP/Dpr and DIP/DIP crystal structures, led to the identification of molecular determinants of DIP/Dpr specificity. This structural knowledge, along with a comprehensive set of quantitative binding affinities, provides new tools for functional studies in vivo.
Single-particle electron cryo-microscopy and computational image classification can be used to analyze structural variability in macromolecules and their assemblies. In some cases, a particle may contain different regions that each display a range of distinct conformations. We have developed strategies, implemented within the Frealign and cisTEM image processing packages, to focus classify on specific regions of a particle and detect potential covariance. The strategies are based on masking the region of interest using either a 2-D mask applied to reference projections and particle images, or a 3-D mask applied to the 3-D volume. We show that focused classification approaches can be used to study structural covariance, a concept that is likely to gain more importance as datasets grow in size, allowing the distinction of more structural states and smaller differences between states. Finally, we apply the approaches to an experimental dataset containing the HIV-1 Transactivation Response (TAR) element RNA fused into the large bacterial ribosomal subunit to deconvolve structural mobility within localized regions of interest, and to a dataset containing assembly intermediates of the large subunit to measure structural covariance.
Macroscale fluorescence imaging is increasingly used to observe biological samples. However, it may suffer from spectral interferences that originate from ambient light or autofluorescence of the sample or its support. In this manuscript, we built a simple and inexpensive fluorescence macroscope, which has been used to evaluate the performance of Speed OPIOM (Out of Phase Imaging after Optical Modulation), which is a reference-free dynamic contrast protocol, to selectively image reversibly photoswitchable fluorophores as labels against detrimental autofluorescence and ambient light. By tuning the intensity and radial frequency of the modulated illumination to the Speed OPIOM resonance and adopting a phase-sensitive detection scheme that ensures noise rejection, we enhanced the sensitivity and the signal-to-noise ratio for fluorescence detection in blot assays by factors of 50 and 10, respectively, over direct fluorescence observation under constant illumination. Then, we overcame the strong autofluorescence of growth media that are currently used in microbiology and realized multiplexed fluorescence observation of colonies of spectrally similar fluorescent bacteria with a unique configuration of excitation and emission wavelengths. Finally, we easily discriminated fluorescent labels from the autofluorescent and reflective background in labeled leaves, even under the interference of incident light at intensities that are comparable to sunlight. The proposed approach is expected to find multiple applications, from biological assays to outdoor observations, in fluorescence macroimaging.
A complex nervous system requires precise numbers of various neuronal types produced with exquisite spatiotemporal control. This striking diversity is generated by a limited number of neural stem cells (NSC), where spatial and temporal patterning intersect. Drosophila is a genetically tractable model system that has significant advantages for studying stem cell biology and neuronal fate specification. Here we review the latest findings in the rich literature of temporal patterning of neuronal identity in the Drosophila central nervous system. Rapidly changing consecutive transcription factors expressed in NSCs specify short series of neurons with considerable differences. More slowly progressing changes are orchestrated by NSC intrinsic temporal factor gradients which integrate extrinsic signals to coordinate nervous system and organismal development.
PURPOSE: To develop switchable and tunable labels with high contrast ratio for MRI using magnetocaloric materials that have sharp first-order magnetic phase transitions at physiological temperatures and typical MRI magnetic field strengths. METHODS: A prototypical magnetocaloric material iron-rhodium (FeRh) was prepared by melt mixing, high-temperature annealing, and ice-water quenching. Temperature- and magnetic field-dependent magnetization measurements of wire-cut FeRh samples were performed on a vibrating sample magnetometer. Temperature-dependent MRI of FeRh samples was performed on a 4.7T MRI. RESULTS: Temperature-dependent MRI clearly demonstrated image contrast changes due to the sharp magnetic state transition of the FeRh samples in the MRI magnetic field (4.7T) and at a physiologically relevant temperature (~37°C). CONCLUSION: A magnetocaloric material, FeRh, was demonstrated to act as a high contrast ratio switchable MRI contrast agent due to its sharp first-order magnetic phase transition in the DC magnetic field of MRI and at physiologically relevant temperatures. A wide range of magnetocaloric materials are available that can be tuned by materials science techniques to optimize their response under MRI-appropriate conditions and be controllably switched in situ with temperature, magnetic field, or a combination of both.
Sensory navigation results from coordinated transitions between distinct behavioral programs. During chemotaxis in the larva, the detection of positive odor gradients extends runs while negative gradients promote stops and turns. This algorithm represents a foundation for the control of sensory navigation across phyla. In the present work, we identified an olfactory descending neuron, PDM-DN, which plays a pivotal role in the organization of stops and turns in response to the detection of graded changes in odor concentrations. Artificial activation of this descending neuron induces deterministic stops followed by the initiation of turning maneuvers through head casts. Using electron microscopy, we reconstructed the main pathway that connects the PDM-DN neuron to the peripheral olfactory system and to the pre-motor circuit responsible for the actuation of forward peristalsis. Our results set the stage for a detailed mechanistic analysis of the sensorimotor conversion of graded olfactory inputs into action selection to perform goal-oriented navigation.
Simultaneous recordings of large populations of neurons in behaving animals allow detailed observation of high-dimensional, complex brain activity. However, experimental approaches often focus on singular behavioral paradigms or brain areas. Here, we recorded whole-brain neuronal activity of larval zebrafish presented with a battery of visual stimuli while recording fictive motor output. We identified neurons tuned to each stimulus type and motor output and discovered groups of neurons in the anterior hindbrain that respond to different stimuli eliciting similar behavioral responses. These convergent sensorimotor representations were only weakly correlated to instantaneous motor activity, suggesting that they critically inform, but do not directly generate, behavioral choices. To catalog brain-wide activity beyond explicit sensorimotor processing, we developed an unsupervised clustering technique that organizes neurons into functional groups. These analyses enabled a broad overview of the functional organization of the brain and revealed numerous brain nuclei whose neurons exhibit concerted activity patterns.
Population recordings of calcium activity are a major source of insight into neural function. Large dataset sizes often require automated methods, but automation can introduce errors that are difficult to detect. Here we show that automatic time course estimation can sometimes lead to significant misattribution errors, in which fluorescence is ascribed to the wrong cell. Misattribution arises when the shapes of overlapping cells are imperfectly defined, or when entire cells or processes are not identified, and misattribution can even be produced by methods specifically designed to handle overlap. To diagnose this problem, we develop a transient-by-transient metric and a visualization tool that allow users to quickly assess the degree of misattribution in large populations. To filter out misattribution, we also design a robust estimator that explicitly accounts for contaminating signals in a generative model. Our methods can be combined with essentially any cell finding technique, empowering users to diagnose and correct at large scale a problem that has the potential to significantly alter scientific conclusions.