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236 Publications

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    Cardona Lab
    04/12/14 | Sample drift correction following 4D confocal time-lapse imaging.
    Parslow A, Cardona A, Bryson-Richardson RJ
    Journal of Visualized Experiments: JoVE. 2014 Apr 12(86):. doi: 10.3791/51086

    The generation of four-dimensional (4D) confocal datasets; consisting of 3D image sequences over time; provides an excellent methodology to capture cellular behaviors involved in developmental processes.  The ability to track and follow cell movements is limited by sample movements that occur due to drift of the sample or, in some cases, growth during image acquisition. Tracking cells in datasets affected by drift and/or growth will incorporate these movements into any analysis of cell position. This may result in the apparent movement of static structures within the sample. Therefore prior to cell tracking, any sample drift should be corrected. Using the open source Fiji distribution (1)  of ImageJ (2,3) and the incorporated LOCI tools (4), we developed the Correct 3D drift plug-in to remove erroneous sample movement in confocal datasets. This protocol effectively compensates for sample translation or alterations in focal position by utilizing phase correlation to register each time-point of a four-dimensional confocal datasets while maintaining the ability to visualize and measure cell movements over extended time-lapse experiments.

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    04/08/14 | Cell-type-based model explaining coexpression patterns of genes in the brain.
    Grange P, Bohland JW, Okaty BW, Sugino K, Bokil H, Nelson SB, Ng L, Hawrylycz M, Mitra PP
    Proceedings of the National Academy of Sciences of the United States of America. 2014 Apr 8;111(14):5397-402. doi: 10.1073/pnas.1312098111

    Spatial patterns of gene expression in the vertebrate brain are not independent, as pairs of genes can exhibit complex patterns of coexpression. Two genes may be similarly expressed in one region, but differentially expressed in other regions. These correlations have been studied quantitatively, particularly for the Allen Atlas of the adult mouse brain, but their biological meaning remains obscure. We propose a simple model of the coexpression patterns in terms of spatial distributions of underlying cell types and establish its plausibility using independently measured cell-type-specific transcriptomes. The model allows us to predict the spatial distribution of cell types in the mouse brain.

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    04/07/14 | A contractile and counterbalancing adhesion system controls the 3D shape of crawling cells.
    Burnette DT, Shao L, Ott C, Pasapera AM, Fischer RS, Baird MA, Der Loughian C, Delanoe-Ayari H, Paszek MJ, Davidson MW, Betzig E, Lippincott-Schwartz J
    The Journal of cell biology. 2014 Apr 14;205(1):83-96. doi: 10.1083/jcb.201311104

    How adherent and contractile systems coordinate to promote cell shape changes is unclear. Here, we define a counterbalanced adhesion/contraction model for cell shape control. Live-cell microscopy data showed a crucial role for a contractile meshwork at the top of the cell, which is composed of actin arcs and myosin IIA filaments. The contractile actin meshwork is organized like muscle sarcomeres, with repeating myosin II filaments separated by the actin bundling protein α-actinin, and is mechanically coupled to noncontractile dorsal actin fibers that run from top to bottom in the cell. When the meshwork contracts, it pulls the dorsal fibers away from the substrate. This pulling force is counterbalanced by the dorsal fibers' attachment to focal adhesions, causing the fibers to bend downward and flattening the cell. This model is likely to be relevant for understanding how cells configure themselves to complex surfaces, protrude into tight spaces, and generate three-dimensional forces on the growth substrate under both healthy and diseased conditions.

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    04/04/14 | Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages
    Nunez-Iglesias J, Kennedy R, Plaza SM, Chakraborty A, William T. Katz
    Frontiers in Neuroinformatics. 2014 Apr 4;8:34. doi: 10.3389/fninf.2014.00034

    The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.

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    Gonen Lab
    04/04/14 | Protein structure determination by MicroED.
    Nannenga BL, Gonen T
    Current Opinion in Structural Biology. 2014 Apr 4;27C:24-31. doi: 10.1016/

    In this review we discuss the current advances relating to structure determination from protein microcrystals with special emphasis on the newly developed method called MicroED. This method uses a transmission electron cryo-microscope to collect electron diffraction data from extremely small 3-dimensional (3D) crystals. MicroED has been used to solve the 3D structure of the model protein lysozyme to 2.9A resolution. As the method further matures, MicroED promises to offer a unique and widely applicable approach to protein crystallography using nanocrystals.

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    04/01/14 | Flying Drosophila stabilize their vision-based velocity controller by sensing wind with their antennae.
    Fuller SB, Straw AD, Peek MY, Murray RM, Dickinson MH
    Proceedings of the National Academy of Sciences of the United States of America. 2014 Apr 1;111(13):E1182-91. doi: 10.1073/pnas.1323529111

    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.

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    04/01/14 | Genome-wide identification of Drosophila Hb9 targets reveals a pivotal role in directing the transcriptome within eight neuronal lineages, including activation of nitric oxide synthase and Fd59a/Fox-D.
    Lacin H, Rusch J, Yeh RT, Fujioka M, Wilson BA, Zhu Y, Robie AA, Mistry H, Wang T, Jaynes JB, Skeath JB
    Developmental Biology. 2014 Apr 1;388:117-33. doi: 10.1016/j.ydbio.2014.01.029

    Hb9 is a homeodomain-containing transcription factor that acts in combination with Nkx6, Lim3, and Tail-up (Islet) to guide the stereotyped differentiation, connectivity, and function of a subset of neurons in Drosophila. The role of Hb9 in directing neuronal differentiation is well documented, but the lineage of Hb9(+) neurons is only partly characterized, its regulation is poorly understood, and most of the downstream genes through which it acts remain at large. Here, we complete the lineage tracing of all embryonic Hb9(+) neurons (to eight neuronal lineages) and provide evidence that hb9, lim3, and tail-up are coordinately regulated by a common set of upstream factors. Through the parallel use of micro-array gene expression profiling and the Dam-ID method, we searched for Hb9-regulated genes, uncovering transcription factors as the most over-represented class of genes regulated by Hb9 (and Nkx6) in the CNS. By a nearly ten-to-one ratio, Hb9 represses rather than activates transcription factors, highlighting transcriptional repression of other transcription factors as a core mechanism by which Hb9 governs neuronal determination. From the small set of genes activated by Hb9, we characterized the expression and function of two - fd59a/foxd, which encodes a transcription factor, and Nitric oxide synthase. Under standard lab conditions, both genes are dispensable for Drosophila development, but Nos appears to inhibit hyper-active behavior and fd59a appears to act in octopaminergic neurons to control egg-laying behavior. Together our data clarify the mechanisms through which Hb9 governs neuronal specification and differentiation and provide an initial characterization of the expression and function of Nos and fd59a in the Drosophila CNS.

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    04/01/14 | Making Drosophila lineage-restricted drivers via patterned recombination in neuroblasts.
    Awasaki T, Kao C, Lee Y, Yang C, Huang Y, Pfeiffer BD, Luan H, Jing X, Huang Y, He Y, Schroeder MD, Kuzin A, Brody T, Zugates CT, Odenwald WF, Lee T
    Nature Neuroscience. 2014 Apr;17(4):631-7. doi: 10.1038/nn.3654

    The Drosophila cerebrum originates from about 100 neuroblasts per hemisphere, with each neuroblast producing a characteristic set of neurons. Neurons from a neuroblast are often so diverse that many neuron types remain unexplored. We developed new genetic tools that target neuroblasts and their diverse descendants, increasing our ability to study fly brain structure and development. Common enhancer-based drivers label neurons on the basis of terminal identities rather than origins, which provides limited labeling in the heterogeneous neuronal lineages. We successfully converted conventional drivers that are temporarily expressed in neuroblasts, into drivers expressed in all subsequent neuroblast progeny. One technique involves immortalizing GAL4 expression in neuroblasts and their descendants. Another depends on loss of the GAL4 repressor, GAL80, from neuroblasts during early neurogenesis. Furthermore, we expanded the diversity of MARCM-based reagents and established another site-specific mitotic recombination system. Our transgenic tools can be combined to map individual neurons in specific lineages of various genotypes.

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    04/01/14 | Rapid adaptive optical recovery of optimal resolution over large volumes.
    Wang K, Milkie DE, Saxena A, Engerer P, Misgeld T, Bronner ME, Mumm J, Betzig E
    Nature Methods. 2014 Apr;11:625-8. doi: 10.1038/nmeth.2925

    Using a descanned, laser-induced guide star and direct wavefront sensing, we demonstrate adaptive correction of complex optical aberrations at high numerical aperture (NA) and a 14-ms update rate. This correction permits us to compensate for the rapid spatial variation in aberration often encountered in biological specimens and to recover diffraction-limited imaging over large volumes (>240 mm per side). We applied this to image fine neuronal processes and subcellular dynamics within the zebrafish brain.

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    Gonen Lab
    03/30/14 | Amphotericin forms an extramembranous and fungicidal sterol sponge.
    Anderson TM, Clay MC, Cioffi AG, Diaz KA, Hisao GS, Tuttle MD, Nieuwkoop AJ, Comellas G, Maryum N, Wang S, Uno BE, Wildeman EL, Gonen T, Rienstra CM, Burke MD
    Nature Chemical Biology. 2014 Mar 30;10(5):400-6. doi: 10.1038/nchembio.1496

    For over 50 years, amphotericin has remained the powerful but highly toxic last line of defense in treating life-threatening fungal infections in humans with minimal development of microbial resistance. Understanding how this small molecule kills yeast is thus critical for guiding development of derivatives with an improved therapeutic index and other resistance-refractory antimicrobial agents. In the widely accepted ion channel model for its mechanism of cytocidal action, amphotericin forms aggregates inside lipid bilayers that permeabilize and kill cells. In contrast, we report that amphotericin exists primarily in the form of large, extramembranous aggregates that kill yeast by extracting ergosterol from lipid bilayers. These findings reveal that extraction of a polyfunctional lipid underlies the resistance-refractory antimicrobial action of amphotericin and suggests a roadmap for separating its cytocidal and membrane-permeabilizing activities. This new mechanistic understanding is also guiding development of what are to our knowledge the first derivatives of amphotericin that kill yeast but not human cells.

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