Percolation and cluster distribution. I. Cluster multiple labeling technique and critical concentration algorithm

Hoshen, Joseph; Kopelman, R.

doi:10.1103/physrevb.14.3438

Cited by 1,869 publications

(1,095 citation statements)

References 14 publications

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“…We extracted HIV-Gag clusters, representing assembling virions, from the list of molecular positions based on the proximity of molecules using the Hoshen-Kopelman algorithm 17 . Neighboring Gag molecules were defined as belonging to the same cluster if their intermolecular distance was less than 50 nm.…”

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confidence: 99%

Quantitative Super-Resolution Imaging Reveals Protein Stoichiometry and Nanoscale Morphology of Assembling HIV-Gag Virions

et al. 2012

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The HIV structural protein Gag assembles to form spherical particles of radius ~70 nm. During the assembly process, the number of Gag proteins increases over several orders of magnitude, from a few at nucleation to thousands at completion. The challenge in studying protein assembly lies in the fact that current methods such as standard fluorescence or electron microscopy techniques cannot access all stages of the assembly process in a cellular context. Here, we demonstrate an approach using super-resolution fluorescence imaging that permits quantitative morphological and molecular counting analysis over a wide range of protein cluster sizes. We applied this technique to the analysis of hundreds of HIV-Gag clusters at the cellular plasma membrane, thus elucidating how different fluorescent labels can change the assembly of virions. Keywords: Super-resolution imaging; protein assembly; protein counting; HIV-GagPage 2 of 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 3 Viruses represent a major class of pathogens, whose assembly in the cellular context contains important information about the complex processes governing viral infection. Viruses are nanoscale objects that assemble from small nucleation complexes to ensembles containing thousands of molecules. In the case of human immunodeficiency virus (HIV), the viral components are targeted to the plasma membrane of infected cells where they assemble and eventually form spheres ~70 nm in radius. Viral assembly is widely studied using HIV-Gag, the main structural protein of HIV, which is sufficient to drive the assembly of virus-like particles (VLPs) in the absence of other viral components 1,2 . Fluorescence imaging has been used to follow the time-dependent increase in the intensity of Gag clusters in living cells 3,4 , revealing the time scale of virion formation. Electron microscopy (EM) has elucidated the spatial arrangement of Gag in fully formed virions [5][6][7][8] . However, studying the complete assembly process requires nanoscale resolution over a large dynamic range, since the size of a cluster ranges from a few molecules to several thousand. This cannot be achieved with standard fluorescence imaging methods since they lack both the necessary resolution to determine cluster morphology, and the sensitivity to detect smaller clusters. EM-based methods in turn lack information on protein identity; thus, complexes composed of small numbers of Gag proteins are difficult to identify, precluding the first step toward quantitative analysis. As an alternative approach, super-resolution fluorescence imaging based on single molecule localization (SR) 9-11 offers nanoscale resolution of structures formed by specific proteins. Here, we use an SR-based approach to quantitatively image hundreds of forming HIV-Gag virions in different stages of cluster formation. With this inf...

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confidence: 99%

Quantitative Super-Resolution Imaging Reveals Protein Stoichiometry and Nanoscale Morphology of Assembling HIV-Gag Virions

et al. 2012

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“…They serve as testing means for new theoretical developments. Over the years, the standard MC method [21] has been improved by the finite-size scaling theory [22] and by other high-performance techniques such as histogram techniques [23,24], cluster updating algorithms [25][26][27] and Wang-Landau flat-histogram method [28]. We have now at hand these efficient techniques to deal with complex systems.…”

Section: Introductionmentioning

confidence: 99%

Re-orientation transition in molecular thin films: Potts model with dipolar interaction

Hoang¹,

Kasperski²,

Puszkarski³

et al. 2013

J. Phys.: Condens. Matter

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We study the low-temperature behavior and the phase transition of a thin film by Monte Carlo simulation. The thin film has a simple cubic lattice structure where each site is occupied by a Potts parameter which indicates the molecular orientation of the site. We take only three molecular orientations in this paper which correspond to the 3-state Potts model. The Hamiltonian of the system includes: (i) the exchange interaction Jij between nearest-neighbor sites i and j (ii) the long-range dipolar interaction of amplitude D truncated at a cutoff distance rc (iii) a single-ion perpendicular anisotropy of amplitude A. We allow Jij = Js between surface spins, and Jij = J otherwise. We show that the ground state depends on the the ratio D/A and rc. For a single layer, for a given A, there is a critical value Dc below (above) which the ground-state (GS) configuration of molecular axes is perpendicular (parallel) to the film surface. When the temperature T is increased, a re-orientation transition occurs near Dc: the low-T in-plane ordering undergoes a transition to the perpendicular ordering at a finite T , below the transition to the paramagnetic phase. The same phenomenon is observed in the case of a film with a thickness. We show that the surface phase transition can occur below or above the bulk transition depending on the ratio Js/J. Surface and bulk order parameters as well as other physical quantities are shown and discussed.

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“…They were pre-processed as follows: after applying a median filter, they were binarized at the local minimum threshold between the 2 modes of their histogram. The Hoshen-Kopelman [43] clustering algorithm was then used to remove non-connected solid voxel sets, as there can be no isolated material in the bone sample. Figure 11 shows 2 extracts from an OA and an OP sample.…”

Section: Resultsmentioning

confidence: 99%

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

et al. 2009

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International audienceIn order to prevent bone fractures due to disease and ageing of the population, and to detect problems while still in their early stages, 3D bone micro architecture needs to be investigated and characterized. Here, we have developed various image processing and simulation techniques to investigate bone micro architecture and its mechanical stiffness. We have evaluated morphological, topological and mechanical bone features using artificial intelligence methods. A clinical study is carried out on two populations of arthritic and osteoporotic bone samples. The performances of Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machines (SVM) and Genetic Algorithm (GA) in classifying the different samples have been compared. Results show that the best separation success (100 %) is achieved with Genetic Algorithm

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Percolation and cluster distribution. I. Cluster multiple labeling technique and critical concentration algorithm

Cited by 1,869 publications

References 14 publications

Quantitative Super-Resolution Imaging Reveals Protein Stoichiometry and Nanoscale Morphology of Assembling HIV-Gag Virions

Quantitative Super-Resolution Imaging Reveals Protein Stoichiometry and Nanoscale Morphology of Assembling HIV-Gag Virions

Re-orientation transition in molecular thin films: Potts model with dipolar interaction

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

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