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3920 Publications

Showing 3311-3320 of 3920 results
09/19/18 | Superresolution architecture of pluripotency guarding adhesions.
Aki Stubb , Camilo Guzmán , Elisa Närvä , Jesse Aaron , Teng-Leong Chew , Markku Saari , Mitro Miihkinen , Guillaume Jacquemet , Johanna Ivaska
bioRxiv. 2018 Sep 19:. doi: 10.1101/402305

Human pluripotent stem cells (hPSC) can generate almost all adult cell lineages. While it is clear that key transcriptional programmes are important elements for maintaining pluripotency, the equally essential requirement for cell adhesion to specific extracellular matrix components remains poorly defined. Our recent observation that hPSC colonies form unusually large “cornerstone” focal adhesions (FA), distinct from parental somatic cells, that are lost following differentiation, emphasises the potential of these atypical FA as gatekeepers of pluripotency. Here, using nanopatterns, we further demonstrate that physical restriction of adhesion size, in hPSC colonies, is sufficient to trigger differentiation. Using superresolution two-colour interfero-metric photo-activated localization microscopy (iPALM), we examined the three-dimensional architecture of these cornerstone adhesions and report vertical lamination of FA proteins with three main structural peculiarities: 1) integrin β5 and talin are present at high density, at the edges of cornerstone FA, adjacent to a vertical kank-rich protein wall. 2) Vinculin localises higher than expected with respect to the substrata and displays a head-above-tail orientation, and 3) surprisingly, actin and α-actinin are present in two discrete layers, a previously undescribed localisation for these proteins. Finally, we report that depletion of kanks diminishes FA patterning, and actin organisation within the colony, indicating a key role for kanks in hPSC colony architecture.

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12/01/11 | Superresolution fluorescence imaging of mitochondrial nucleoids reveals their spatial range, limits, and membrane interaction.
Brown TA, Tkachuk AN, Shtengel G, Kopek BG, Bogenhagen DF, Hess HF, Clayton DA
Molecular and Cellular Biology. 2011 Dec;31:4994-5010. doi: 10.1128/MCB.05694-11

A fundamental objective in molecular biology is to understand how DNA is organized in concert with various proteins, RNA, and biological membranes. Mitochondria maintain and express their own DNA (mtDNA), which is arranged within structures called nucleoids. Their functions, dimensions, composition, and precise locations relative to other mitochondrial structures are poorly defined. Superresolution fluorescence microscopy techniques that exceed the previous limits of imaging within the small and highly compartmentalized mitochondria have been recently developed. We have improved and employed both two- and three-dimensional applications of photoactivated localization microscopy (PALM and iPALM, respectively) to visualize the core dimensions and relative locations of mitochondrial nucleoids at an unprecedented resolution. PALM reveals that nucleoids differ greatly in size and shape. Three-dimensional volumetric analysis indicates that, on average, the mtDNA within ellipsoidal nucleoids is extraordinarily condensed. Two-color PALM shows that the freely diffusible mitochondrial matrix protein is largely excluded from the nucleoid. In contrast, nucleoids are closely associated with the inner membrane and often appear to be wrapped around cristae or crista-like inner membrane invaginations. Determinations revealing high packing density, separation from the matrix, and tight association with the inner membrane underscore the role of mechanisms that regulate access to mtDNA and that remain largely unknown.

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01/13/14 | Superresolution imaging of biological systems using photoactivated localization microscopy.
Sengupta P, Van Engelenburg SB, Lippincott-Schwartz J
Chemical reviews. 2014 Mar 26;114(6):3189-202. doi: 10.1021/cr400614m
12/15/14 | Superresolution imaging reveals structural features of EB1 in microtubule plus-end tracking.
Xia P, Liu X, Wu B, Zhang S, Song X, Yao PY, Lippincott-Schwartz J, Yao X
Molecular biology of the cell. 2014 Dec 15;25(25):4166-73. doi: 10.1091/mbc.E14-06-1133

Visualization of specific molecules and their interactions in real time and space is essential to delineate how cellular dynamics and the signaling circuit are orchestrated. Spatial regulation of conformational dynamics and structural plasticity of protein interactions is required to rewire signaling circuitry in response to extracellular cues. We introduce a method for optically imaging intracellular protein interactions at nanometer spatial resolution in live cells, using photoactivatable complementary fluorescent (PACF) proteins. Subsets of complementary fluorescent protein molecules were activated, localized, and then bleached; this was followed by the assembly of superresolution images from aggregate position of sum interactive molecules. Using PACF, we obtained precise localization of dynamic microtubule plus-end hub protein EB1 dimers and their distinct distributions at the leading edges and in the cell bodies of migrating cells. We further delineated the structure-function relationship of EB1 by generating EB1-PACF dimers (EB1(wt):EB1(wt), EB1(wt):EB1(mt), and EB1(mt):EB1(mt)) and imaging their precise localizations in culture cells. Surprisingly, our analyses revealed critical role of a previously uncharacterized EB1 linker region in tracking microtubule plus ends in live cells. Thus PACF provides a unique approach to delineating spatial dynamics of homo- or heterodimerized proteins at the nanometer scale and establishes a platform to report the precise regulation of protein interactions in space and time in live cells.

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01/01/10 | Superresolution imaging using single-molecule localization.
Patterson G, Davidson M, Manley S, Lippincott-Schwartz J
Annual review of physical chemistry. 2010;61:345-67. doi: 10.1146/annurev.physchem.012809.103444

Superresolution imaging is a rapidly emerging new field of microscopy that dramatically improves the spatial resolution of light microscopy by over an order of magnitude (approximately 10-20-nm resolution), allowing biological processes to be described at the molecular scale. Here, we discuss a form of superresolution microscopy based on the controlled activation and sampling of sparse subsets of photoconvertible fluorescent molecules. In this single-molecule-based imaging approach, a wide variety of probes have proved valuable, ranging from genetically encodable photoactivatable fluorescent proteins to photoswitchable cyanine dyes. These have been used in diverse applications of superresolution imaging: from three-dimensional, multicolor molecule localization to tracking of nanometric structures and molecules in living cells. Single-molecule-based superresolution imaging thus offers exciting possibilities for obtaining molecular-scale information on biological events occurring at variable timescales.

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09/24/13 | Superresolution imaging with standard fluorescent probes.
Millis BA, Burnette DT, Lippincott-Schwartz J, Kachar B
Current protocols in cell biology / editorial board, Juan S. Bonifacino ... [et al.]. 2013;60:Unit 21.8.. doi: 10.1002/0471143030.cb2108s60

For more than 100 years, the ultimate resolution of a light microscope (∼ 200 nm) has been constrained by the fundamental physical phenomenon of diffraction, as described by Ernst Abbe in 1873. While this limitation is just as applicable to today's light microscopes, it is the combination of high-end optics, clever methods of sample illumination, and computational techniques that has enabled researchers to access information at an order of magnitude greater resolution than once thought possible. This combination, broadly termed superresolution microscopy, has been increasingly practical for many labs to implement from both a hardware and software standpoint, but, as with many cutting-edge techniques, it also comes with limitations. One of the current drawbacks to superresolution microscopy is the limited number of probes and conditions that have been suitable for imaging. Here, a technique termed bleaching/blinking-assisted localization microscopy (BaLM) makes use of the inherent blinking and bleaching properties of almost all fluorophores as a means to generate superresolution images.

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09/15/22 | Superresolution microscopy reveals actomyosin dynamics in medioapical arrays.
Moore RP, Fogerson SM, Tulu US, Yu JW, Cox AH, Sican MA, Li D, Legant WR, Weigel AV, Crawford JM, Betzig E, Kiehart DP
Molecular Biology of the Cell. 2022 Sep 15;33(11):ar94. doi: 10.1091/mbc.E21-11-0537

Arrays of actin filaments (F-actin) near the apical surface of epithelial cells (medioapical arrays) contribute to apical constriction and morphogenesis throughout phylogeny. Here, superresolution approaches (grazing incidence structured illumination, GI-SIM, and lattice light sheet, LLSM) microscopy resolve individual, fluorescently labeled F-actin and bipolar myosin filaments that drive amnioserosa cell shape changes during dorsal closure in . In expanded cells, F-actin and myosin form loose, apically domed meshworks at the plasma membrane. The arrays condense as cells contract, drawing the domes into the plane of the junctional belts. As condensation continues, individual filaments are no longer uniformly apparent. As cells expand, arrays of actomyosin are again resolved-some F-actin turnover likely occurs, but a large fraction of existing filaments rearrange. In morphologically isotropic cells, actin filaments are randomly oriented and during contraction are drawn together but remain essentially randomly oriented. In anisotropic cells, largely parallel actin filaments are drawn closer to one another. Our images offer unparalleled resolution of F-actin in embryonic tissue, show that medioapical arrays are tightly apposed to the plasma membrane and are continuous with meshworks of lamellar F-actin. Medioapical arrays thereby constitute modified cell cortex. In concert with other tagged array components, superresolution imaging of live specimens will offer new understanding of cortical architecture and function.

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01/01/11 | Supervised hypergraph labeling.
Parag T, Elgammal A
IEEE Conference on Computer Vision Pattern Recognition. 2011:

We address the problem of labeling individual datapoints given some knowledge about (small) subsets or groups of them. The knowledge we have for a group is the likelihood value for each group member to satisfy a certain model. This problem is equivalent to hypergraph labeling problem where each datapoint corresponds to a node and the each subset correspond to a hyperedge with likelihood value as its weight. We propose a novel method to model the label dependence using an Undirected Graphical Model and reduce the problem of hypergraph labeling into an inference problem. This paper describes the structure and necessary components of such model and proposes useful cost functions. We discuss the behavior of proposed algorithm with different forms of the cost functions, identify suitable algorithms for inference and analyze required properties when it is theoretically guaranteed to have exact solution. Examples of several real world problems are shown as applications of the proposed method.

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10/14/07 | Supervised Learning of Image Restoration with Convolutional Networks
Jain V, Murray J, Roth F, Turaga S, Zhigulin V, Briggman K, Helmstaedter M, Denk W, Seung H
IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007. 2007-10:. doi: 10.1109/ICCV.2007.4408909

Convolutional networks have achieved a great deal of success in high-level vision problems such as object recognition. Here we show that they can also be used as a general method for low-level image processing. As an example of our approach, convolutional networks are trained using gradient learning to solve the problem of restoring noisy or degraded images. For our training data, we have used electron microscopic images of neural circuitry with ground truth restorations provided by human experts. On this dataset, Markov random field (MRF), conditional random field (CRF), and anisotropic diffusion algorithms perform about the same as simple thresholding, but superior performance is obtained with a convolutional network containing over 34,000 adjustable parameters. When restored by this convolutional network, the images are clean enough to be used for segmentation, whereas the other approaches fail in this respect. We do not believe that convolutional networks are fundamentally superior to MRFs as a representation for image processing algorithms. On the contrary, the two approaches are closely related. But in practice, it is possible to train complex convolutional networks, while even simple MRF models are hindered by problems with Bayesian learning and inference procedures. Our results suggest that high model complexity is the single most important factor for good performance, and this is possible with convolutional networks.

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10/14/07 | Supervised learning of image restoration with convolutional networks.
Jain V, Murray JF, Roth F, Turaga S, Zhigulin V, Briggman KL, Helmstaedter MN, Denk W, Seung HS
IEEE 11th International Conference on Computer Vision. 2007 Oct 14;2:1-8