Filter
Associated Lab
- 43418 (5) Apply 43418 filter
- 43427 (4) Apply 43427 filter
- 43430 (6) Apply 43430 filter
- Ahrens Lab (4) Apply Ahrens Lab filter
- Aso Lab (3) Apply Aso Lab filter
- Betzig Lab (4) Apply Betzig Lab filter
- Beyene Lab (1) Apply Beyene Lab filter
- Branson Lab (3) Apply Branson Lab filter
- Card Lab (5) Apply Card Lab filter
- Cardona Lab (3) Apply Cardona Lab filter
- Clapham Lab (1) Apply Clapham Lab filter
- Dickson Lab (4) Apply Dickson Lab filter
- Dudman Lab (2) Apply Dudman Lab filter
- Espinosa Medina Lab (2) Apply Espinosa Medina Lab filter
- Fitzgerald Lab (3) Apply Fitzgerald Lab filter
- Funke Lab (4) Apply Funke Lab filter
- Grigorieff Lab (3) Apply Grigorieff Lab filter
- Harris Lab (1) Apply Harris Lab filter
- Heberlein Lab (2) Apply Heberlein Lab filter
- Hermundstad Lab (2) Apply Hermundstad Lab filter
- Hess Lab (5) Apply Hess Lab filter
- Jayaraman Lab (4) Apply Jayaraman Lab filter
- Johnson Lab (1) Apply Johnson Lab filter
- Keller Lab (5) Apply Keller Lab filter
- Lavis Lab (9) Apply Lavis Lab filter
- Lee (Albert) Lab (5) Apply Lee (Albert) Lab filter
- Li Lab (3) Apply Li Lab filter
- Lippincott-Schwartz Lab (8) Apply Lippincott-Schwartz Lab filter
- Liu (Zhe) Lab (7) Apply Liu (Zhe) Lab filter
- Looger Lab (7) Apply Looger Lab filter
- Pachitariu Lab (2) Apply Pachitariu Lab filter
- Pedram Lab (3) Apply Pedram Lab filter
- Podgorski Lab (5) Apply Podgorski Lab filter
- Reiser Lab (2) Apply Reiser Lab filter
- Romani Lab (2) Apply Romani Lab filter
- Rubin Lab (9) Apply Rubin Lab filter
- Saalfeld Lab (2) Apply Saalfeld Lab filter
- Scheffer Lab (1) Apply Scheffer Lab filter
- Schreiter Lab (5) Apply Schreiter Lab filter
- Sgro Lab (4) Apply Sgro Lab filter
- Spruston Lab (5) Apply Spruston Lab filter
- Stern Lab (4) Apply Stern Lab filter
- Sternson Lab (4) Apply Sternson Lab filter
- Stringer Lab (2) Apply Stringer Lab filter
- Svoboda Lab (5) Apply Svoboda Lab filter
- Tebo Lab (4) Apply Tebo Lab filter
- Truman Lab (3) Apply Truman Lab filter
- Turaga Lab (1) Apply Turaga Lab filter
- Turner Lab (3) Apply Turner Lab filter
- Zlatic Lab (2) Apply Zlatic Lab filter
Associated Project Team
- Fly Descending Interneuron (2) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (1) Apply Fly Functional Connectome filter
- FlyEM (2) Apply FlyEM filter
- FlyLight (8) Apply FlyLight filter
- GENIE (5) Apply GENIE filter
- MouseLight (1) Apply MouseLight filter
- Tool Translation Team (T3) (3) Apply Tool Translation Team (T3) filter
- Transcription Imaging (1) Apply Transcription Imaging filter
Publication Date
- Remove 2020 filter 2020
Type of Publication
191 Publications
Showing 171-180 of 191 resultsThis chapter describes many of the technologies, which have the potential to provide new insights into fundamental aspects of liver biology. Imaging live liver tissue in an animal with multiphoton microscopy coupled with photoactivatable fluorescent proteins and/or additional fluorescent proteins could be used to follow the lineage and fates of individual transplanted stem cells or developing transgenic cells in liver. Proteins or other molecules are labeled with a dye that can be excited with light source. Cells and proteins are generally too small to detect with the naked eye, relatively transparent when imaged by light microscopy, and are highly dynamic. With the increased signal to noise, isotropic and volumetric imaging and high speeds lattice light sheet allows for 3D super‐resolution microscopy, as well. Photomultiplier tubes, while capable of detecting and counting single photons, are less useful for high‐speed imaging because they normally only detect a single pixel at a time.
Molecular interactions at the cellular interface mediate organized assembly of single cells into tissues and, thus, govern the development and physiology of multicellular organisms. Here, we developed a cell-type-specific, spatiotemporally resolved approach to profile cell-surface proteomes in intact tissues. Quantitative profiling of cell-surface proteomes of Drosophila olfactory projection neurons (PNs) in pupae and adults revealed global downregulation of wiring molecules and upregulation of synaptic molecules in the transition from developing to mature PNs. A proteome-instructed in vivo screen identified 20 cell-surface molecules regulating neural circuit assembly, many of which belong to evolutionarily conserved protein families not previously linked to neural development. Genetic analysis further revealed that the lipoprotein receptor LRP1 cell-autonomously controls PN dendrite targeting, contributing to the formation of a precise olfactory map. These findings highlight the power of temporally resolved in situ cell-surface proteomic profiling in discovering regulators of brain wiring.
The fast turnover of membrane components through endocytosis and recycling allows precise control of the composition of the plasma membrane. Endocytic recycling can be rapid with some molecules returning to the plasma membrane with a <5 minutes. Existing methods to study these trafficking pathways utilize chemical, radioactive, or fluorescent labeling of cell surface receptors in pulse-chase experiments, which require tedious washing steps and manual collection of samples. Here, we introduce a live-cell endocytic recycling assay, based on a newly designed cell-impermeable, fluorogenic ligand for HaloTag: 'Janelia Fluor 635i' (JFi; i=impermeant) which allows real-time detection of membrane receptor recycling at steady state. We used this method to study the effect of iron depletion on transferrin receptor (TfR) recycling using the chelator desferrioxamine. We found this perturbation significantly increases the TfR recycling rate. The high temporal resolution and simplicity of this assay provides a clear advantage over extant methods and makes it ideal for large scale cellular imaging studies. This assay can be adapted to examine other cellular kinetic parameters such as protein turnover and biosynthetic trafficking.
Within cells, the spatial compartmentalization of thousands of distinct proteins serves a multitude of diverse biochemical needs. Correlative super-resolution (SR) fluorescence and electron microscopy (EM) can elucidate protein spatial relationships to global ultrastructure, but has suffered from tradeoffs of structure preservation, fluorescence retention, resolution, and field of view. We developed a platform for three-dimensional cryogenic SR and focused ion beam-milled block-face EM across entire vitreously frozen cells. The approach preserves ultrastructure while enabling independent SR and EM workflow optimization. We discovered unexpected protein-ultrastructure relationships in mammalian cells including intranuclear vesicles containing endoplasmic reticulum-associated proteins, web-like adhesions between cultured neurons, and chromatin domains subclassified on the basis of transcriptional activity. Our findings illustrate the value of a comprehensive multimodal view of ultrastructural variability across whole cells.
The motor cortex controls skilled arm movement by sending temporal patterns of activity to lower motor centres. Local cortical dynamics are thought to shape these patterns throughout movement execution. External inputs have been implicated in setting the initial state of the motor cortex, but they may also have a pattern-generating role. Here we dissect the contribution of local dynamics and inputs to cortical pattern generation during a prehension task in mice. Perturbing cortex to an aberrant state prevented movement initiation, but after the perturbation was released, cortex either bypassed the normal initial state and immediately generated the pattern that controls reaching or failed to generate this pattern. The difference in these two outcomes was probably a result of external inputs. We directly investigated the role of inputs by inactivating the thalamus; this perturbed cortical activity and disrupted limb kinematics at any stage of the movement. Activation of thalamocortical axon terminals at different frequencies disrupted cortical activity and arm movement in a graded manner. Simultaneous recordings revealed that both thalamic activity and the current state of cortex predicted changes in cortical activity. Thus, the pattern generator for dexterous arm movement is distributed across multiple, strongly interacting brain regions.
The anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. Here, we characterize gene expression patterns in distinct neural cell types of the visual system using genetic lines to access individual cell types, the TAPIN-seq method to measure their transcriptomes, and a probabilistic method to interpret these measurements. We used these tools to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types, available at http://www.opticlobe.com. Combining these transcriptomes with recently reported connectomes helps characterize how information is transmitted and processed across a range of scales, from individual synapses to circuit pathways. We describe examples that include identifying neurotransmitters, including cases of apparent co-release, generating functional hypotheses based on receptor expression, as well as identifying strong commonalities between different cell types.
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.
Human memory appears to be fragile and unpredictable. Free recall of random lists of words is a standard paradigm used to probe episodic memory. We proposed an associative search process that can be reduced to a deterministic walk on random graphs defined by the structure of memory representations. The corresponding graph model can be solved analytically, resulting in a novel parameter-free prediction for the average number of memory items recalled (R) out of M items in memory: R=sqrt[3πM/2]. This prediction was verified with a specially designed experimental protocol combining large-scale crowd-sourced free recall and recognition experiments with randomly assembled lists of words or common facts. Our results show that human memory can be described by universal laws derived from first principles.