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DiSWOP: a novel measure for cell-level protein network analysis in localized proteomics image data

BIOINFORMATICS 2014年第3期30卷 420-427页

作者： Kovacheva, Violeta N. Khan, Adnan M. Khan, Michael Epstein, David B. A. Rajpoot, Nasir M. Univ Warwick Dept Syst Biol Coventry CV4 7AL W Midlands England Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Univ Warwick Sch Life Sci Coventry CV4 7AL W Midlands England Univ Warwick Math Inst Coventry CV4 7AL W Midlands England Qatar Univ Dept Comp Sci & Engn Doha Qatar

Motivation: New bioimaging techniques have recently been proposed to visualize the colocation or interaction of several proteins within individual cells, displaying the heterogeneity of neighbouring cells within the same tissue specimen. Such techniques could hold the key to understanding complex biological systems such as the protein interactions involved in cancer. However, there is a need for new algorithmic approaches that analyze the large amounts of multi-tag bioimage data from cancerous and normal tissue specimens to begin to infer protein networks and unravel the cellular heterogeneity at a molecular level. Results: The proposed approach analyzes cell phenotypes in normal and cancerous colon tissue imaged using the robotically controlled Toponome Imaging System microscope. It involves segmenting the 4',6-diamidino-2-phenylindole-labelled image into cells and determining the cell phenotypes according to their protein-protein dependence profile. These were analyzed using two new measures, Difference in Sums of Weighted cO-dependence/Anti-co-dependence profiles (DiSWOP and DiSWAP) for overall co-expression and anti-co-expression, respectively. These novel quantities were extracted using 11 Toponome Imaging System image stacks from either cancerous or normal human colorectal specimens. This approach enables one to easily identify protein pairs that have significantly higher/lower co-expression levels in cancerous tissue samples when compared with normal colon tissue.

关键词： Algorithms Colonic Neoplasms/metabolism Humans image processing, computer-assisted/methods Phenotype Protein Interaction Mapping/methods Proteomics/methods

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Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS

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NEUROimage 2014年 95卷 90-105页

作者： Becker, S. M. A. Tabelow, K. Mohammadi, S. Weiskopf, N. Polzehl, J. Weierstrass Inst Appl Anal & Stochast Berlin Germany UCL Inst Neurol Wellcome Trust Ctr Neuroimaging London England

We present a novel multi-shell position-orientation adaptive smoothing (msPOAS) method for diffusion weighted magnetic resonance data. Smoothing in voxel and diffusion gradient space is embedded in an iterative adaptive multiscale approach. The adaptive character avoids blurring of the inherent structures and preserves discontinuities. The simultaneous treatment of all q-shells improves the stability compared to single-shell approaches such as the original POAS method. The msPOAS implementation simplifies and speeds up calculations, compared to POAS, facilitating its practical application. Simulations and heuristics support the face validity of the technique and its rigorousness. The characteristics of msPOAS were evaluated on single and multi-shell diffusion data of the human brain. Significant reduction in noise while preserving the fine structure was demonstrated for diffusion weighted images, standard DTI analysis and advanced diffusion models such as NODDI. MsPOAS effectively improves the poor signal-to-noise ratio in highly diffusion weighted multi-shell diffusion data, which is required by recent advanced diffusion micro-structure models. We demonstrate the superiority of the new method compared to other advanced denoising methods. (C) 2014 The Authors. Published by Elsevier Inc.

关键词： Algorithms Artifacts Brain Mapping/methods Brain Mapping/methods Diffusion Magnetic Resonance Imaging/methods Humans image processing, computer-assisted/methods Models, Theoretical

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The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery

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NEURON 2014年第2期84卷 262-274页

作者： Calhoun, Vince D. Miller, Robyn Pearlson, Godfrey Adali, Tulay Mind Res Network Albuquerque NM 87106 USA LBERI Albuquerque NM 87106 USA Univ New Mexico Dept ECE Albuquerque NM 87131 USA Univ Maryland Baltimore Cty Dept CSEE Baltimore MD 21250 USA Olin Neuropsychiat Res Ctr Hartford CT 06114 USA

Recent years have witnessed a rapid growth of interest in moving functional magnetic resonance imaging (fMRI) beyond simple scan-length averages and into approaches that capture time-varying properties of connectivity. In this Perspective we use the term "chronnectome" to describe metrics that allow a dynamic view of coupling. In the chronnectome, coupling refers to possibly time-varying levels of correlated or mutually informed activity between brain regions whose spatial properties may also be temporally evolving. We primarily focus on multivariate approaches developed in our group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component analysis. We also discuss the potential these approaches offer to improve characterization and understanding of brain function. There are a number of methodological directions that need to be developed further, but chronnectome approaches already show great promise for the study of both the healthy and the diseased brain.

关键词： Animals Brain/physiology Brain Mapping/methods Brain Mapping/methods Humans image processing, computer-assisted/methods Magnetic Resonance Imaging/methods Neural Pathways/physiology Time Factors

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Automatic denoising of functional MM data: Combining independent component analysis and hierarchical fusion of classifiers

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NEUROimage 2014年 90卷 449-468页

作者： Salimi-Khorshidi, Gholamreza Douaud, Gwenaelle Beckmann, Christian F. Glasser, Matthew F. Griffanti, Ludovica Smith, Stephen M. Univ Oxford Ctr Funct MRI Brain FMRIB Oxford England Radboud Univ Nijmegen Donders Inst Brain Cognit & Behav NL-6525 ED Nijmegen Netherlands Univ Twente MIRA Inst Biomed Technol & Tech Med NL-7500 AE Enschede Netherlands Washington Univ Sch Med St Louis MO USA Politecn Milan Dept Elect Informat & Bioengn I-20133 Milan Italy Fdn Don Carlo Gnocchi ONLUS MR Lab Milan Italy

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fIVIRI data (both with the application of external stimuli and with the subject "at rest"). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxelwise time series. Given the set of ICA components, if the components representing "signal" (brain activity) can be distinguished form the "noise" components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise;hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the handclassification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data,

关键词： Algorithms Artifacts Brain/physiology Brain Mapping/methods Brain Mapping/methods Humans image processing, computer-assisted/methods Magnetic Resonance Imaging Principal Component Analysis

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Magnetization transfer contrast imaging reveals amyloid pathology in Alzheimer's disease transgenic mice

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NEUROimage 2014年 87卷 111-119页

作者： Bigot, Christian Vanhoutte, Greetje Verhoye, Marleen Van der Linden, Annemie Univ Antwerp Bioimaging Lab B-2610 Antwerp Belgium

The presence of amyloid plaques in the brain is one of the pathological hallmarks of Alzheimer's disease, which might already be present in the early stage of the disease. Therefore it is important to track amyloid plaques as early as possible. In this paper, we report magnetization transfer contrast magnetic resonance imaging (MTC MRI) as a novel approach to detect amyloid plaques in vivo. Two mice models, APP/PSI and BRI, developing amyloid pathology were investigated with MTC MB], T-2 relaxation measurements and immunohistochemistry (IHC). MT-ratios of several brain regions were compared to T-2-values and correlated with quantitative IHC, revealing amyloid load and gliosis in different brain regions. APP/PS1 mice develop large compact plaques, resembling late stage Alzheimer's disease, while rather small and diffuse plaques are deposited in BRI mice, reflecting early stage of Alzheimer's disease. We found significantly higher MT-ratio's in the brain of APP/PSI mice as compared to their controls and similar trends in BR! mice. A region based MT-ratio and IHC analysis and correlations between MT-ratios and quantitative IHC indicate amyloid plaques as the main substrate for altered MT-ratios in transgenic animals. We additionally demonstrated the improved sensitivity of MTC MB] to amyloid pathology as compared to traditional T-2 relaxation measurements. Our results suggest that MTC MRI reveals extensive, and potentially even early amyloid pathology. Further unraveling the MT-effect of each pathological feature during each stage of AD might indicate MTC MB] as a useful diagnostic technique. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Alzheimer Disease/pathology Animals Brain/pathology Contrast Media Contrast Media Disease Models, Animal Female image processing, computer-assisted/methods Immunohistochemistry Immunohistochemistry Magnetic Resonance Imaging/methods Male Mice Mice, Transgenic Mice, Transgenic Plaque, Amyloid/pathology

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Phase based venous suppression in resting-state BOLD GE-fMRI

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NEUROimage 2014年 100卷 51-59页

作者： Curtis, Andrew T. Hutchison, R. Matthew Menon, Ravi S. Univ Western Ontario Robarts Res Inst Ctr Funct & Metab Mapping London ON N6A 5K8 Canada Univ Western Ontario Dept Med Biophys London ON N6A 5C1 Canada Harvard Univ Ctr Brain Sci Cambridge MA 02138 USA

Resting-state functional MRI (RS-fMRI) is a widely used method for inferring connectivity between brain regions or nodes. As with task-based fMRI, the spatial specificity of the connectivity maps can be distorted by the strong biasing effect of the BOLD signal in macroscopic veins. In RS-fMRI this effect is exacerbated by the temporal coherences of physiological origin between large veins that are widely distributed in the brain. In gradient echo based EPI, used for the vast majority of RS-fMRI, macroscopic veins that carry BOLD-related changes exhibit a strong phase response. This allows for post-processing identification and removal of venous signals using a phase regressor technique. Here, we employ this approach to suppress macrovascular venous contributions in high-field whole-brain RS-fMRI data sets, resulting in significant changes to both the spatial localization of the networks and the correlations between the network nodes. These effects were observed at both the individual and group analysis level, suggesting that venous contamination is a confounding factor for RS-fMRI studies even at relatively low image resolutions. Suppression of the macrovascular signal using the phase regression approach may therefore help to better identify, delineate, and interpret the true structure of large-scale brain networks. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Adult Brain/blood supply Brain/physiology Brain Mapping/methods Brain Mapping/methods Brain Mapping/standards Brain Mapping/standards Cerebral Veins/anatomy & histology Female Humans image processing, computer-assisted/methods image processing, computer-assisted/standards Magnetic Resonance Imaging Male Young Adult

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Viral plaque analysis on a wide field-of-view, time-lapse, on-chip imaging platform

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ANALYST 2014年第15期139卷 3727-3734页

作者： Han, Chao Yang, Changhuei CALTECH Pasadena CA 91125 USA

The observation of viral plaques is the standard method for determining the viral titer and understanding the behaviors of viruses. Here, we report the application of a wide field-of-view (FOV), time-lapse, on-chip imaging platform, termed the ePetri, for plaque analysis of murine norovirus 1 (MNV-1). The ePetri offers the ability to dynamically track plaques at the individual cell death event level over a wide FOV of 6 mm x 4 mm. As demonstration, we captured high-resolution time-lapse images of MNV-1-infected cells at 30 min intervals. We implemented a customized image-processing program containing a density-based clustering algorithm to analyze the spatial-temporal distribution of cell death events to identify plaques at their earliest stages. By using the results in a viral titer count format, we showed that our approach gives results that are comparable to conventional plaque assays. We further showed that the extra information collected by the ePetri can be used to monitor the dynamics of plaque formation and growth. Finally, we performed a demonstration experiment to show the relevance of such an experimental format for viral inhibitor study. We believe the ePetri is a simple and compact solution for the automation of viral plaque assays, plaque behavior analysis, and antiviral drug discovery and study.

关键词： Algorithms Animals Caliciviridae Infections/pathology Caliciviridae Infections/veterinary Cell Line Equipment Design image processing, computer-assisted/methods Mice/virology Norovirus/growth & development Norovirus/isolation & purification Viral Plaque Assay/instrumentation

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A Rapid and Efficient 2D/3D Nuclear Segmentation Method for Analysis of Early Mouse Embryo and Stem Cell image Data

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STEM CELL REPORTS 2014年第3期2卷 382-397页

作者： Lou, Xinghua Kang, Minjung Xenopoulos, Panagiotis Munoz-Descalzo, Silvia Hadjantonakis, Anna-Katerina Sloan Kettering Inst Dev Biol Program New York NY 10065 USA Cornell Univ Dept Biochem Cell & Mol Biol Program Weill Grad Sch Med Sci New York NY 10065 USA

Segmentation is a fundamental problem that dominates the success of microscopic image analysis. In almost 25 years of cell detection software development, there is still no single piece of commercial software that works well in practice when applied to early mouse embryo or stem cell image data. To address this need, we developed MINS (modular interactive nuclear segmentation) as a MATLAB/C++-based segmentation tool tailored for counting cells and fluorescent intensity measurements of 2D and 3D image data. Our aim was to develop a tool that is accurate and efficient yet straightforward and user friendly. The MINS pipeline comprises three major cascaded modules: detection, segmentation, and cell position classification. An extensive evaluation of MINS on both 2D and 3D images, and comparison to related tools, reveals improvements in segmentation accuracy and usability. Thus, its accuracy and ease of use will allow MINS to be implemented for routine single-cell-level image analyses.

关键词： Algorithms Animals Embryo, Mammalian/cytology Embryo, Mammalian/metabolism Embryonic Stem Cells/cytology Embryonic Stem Cells/metabolism image processing, computer-assisted/methods Imaging, Three-Dimensional/methods Mice Microscopy/methods Software

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Real-time evaluation of aggregation using confocal imaging and image analysis tools

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ANALYST 2014年第3期139卷 564-568页

作者： Hamrang, Zahra Zindy, Egor Clarke, David Pluen, Alain Univ Manchester Sch Pharm & Pharmaceut Sci Manchester Lancs England Univ Manchester Fac Life Sci Wellcome Trust Ctr Cell Matrix Res Manchester Lancs England Univ Manchester Ctr Excellence Biopharmaceut Manchester Lancs England

Real-time confocal imaging was utilised to monitor the in situ loss of BSA monomers and aggregate formation using Spatial Intensity Distribution Analysis (SpIDA) and Raster image Correlation Spectroscopy (RICS). At the proof of concept level this work has demonstrated the applicability of RICS and SpIDA for monitoring protein oligomerisation and larger aggregate formation.

关键词： Animals Cattle image processing, computer-assisted/methods Microscopy, Confocal/methods Protein Multimerization Protein Structure, Quaternary Serum Albumin, Bovine/chemistry Time Factors

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ThunderSTORM: a comprehensive imageJ plug-in for PALM and STORM data analysis and super-resolution imaging

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BIOINFORMATICS 2014年第16期30卷 2389-2390页

作者： Ovesny, Martin Krizek, Pavel Borkovec, Josef Svindrych, Zdenek K. Hagen, Guy M. Charles Univ Prague Inst Cellular Biol & Pathol Fac Med 1 Prague 12800 Czech Republic

ThunderSTORM is an open-source, interactive and modular plug-in for imageJ designed for automated processing, analysis and visualization of data acquired by single-molecule localization microscopy methods such as photo-activated localization microscopy and stochastic optical reconstruction microscopy. ThunderSTORM offers an extensive collection of processing and post-processing methods so that users can easily adapt the process of analysis to their data. ThunderSTORM also offers a set of tools for creation of simulated data and quantitative performance evaluation of localization algorithms using Monte Carlo simulations.

关键词： Algorithms computer Graphics image processing, computer-assisted/methods Microscopy/methods Microscopy, Fluorescence/methods Monte Carlo Method Software

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