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Robert F Murphy - One of the best experts on this subject based on the ideXlab platform.
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Automated interpretation of subcellular patterns in Fluorescence Microscope images for location proteomics
2015Co-Authors: Xiang Chen, Robert F Murphy, Meel VellisteAbstract:Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein’s subcellular location is essential to a complete understanding of its functions. Currently subcellular location patterns are routinely determined by visual inspection of Fluorescence Microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput Microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during and after the onset of various diseases
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improved recognition of figures containing Fluorescence Microscope images in online journal articles using graphical models
Bioinformatics, 2008Co-Authors: Yuntao Qian, Robert F MurphyAbstract:Motivation: There is extensive interest in automating the collection, organization and analysis of biological data. Data in the form of images in online literature present special challenges for such efforts. The first steps in understanding the contents of a figure are decomposing it into panels and determining the type of each panel. In biological literature, panel types include many kinds of images collected by different techniques, such as photographs of gels or images from Microscopes. We have previously described the SLIF system ( http://slif.cbi.cmu.edu) that identifies panels containing Fluorescence Microscope images among figures in online journal articles as a prelude to further analysis of the subcellular patterns in such images. This system contains a pretrained classifier that uses image features to assign a type (class) to each separate panel. However, the types of panels in a figure are often correlated, so that we can consider the class of a panel to be dependent not only on its own features but also on the types of the other panels in a figure. Results: In this article, we introduce the use of a type of probabilistic graphical model, a factor graph, to represent the structured information about the images in a figure, and permit more robust and accurate inference about their types. We obtain significant improvement over results for considering panels separately. Availability: The code and data used for the experiments described here are available from http://murphylab.web.cmu.edu/software Contact: murphy@cmu.edu
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automated interpretation of subcellular patterns in Fluorescence Microscope images for location proteomics
Cytometry Part A, 2006Co-Authors: Xiang Chen, Meel Velliste, Robert F MurphyAbstract:Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein’s subcellular location is essential to a complete understanding of its functions. Currently, subcellular location patterns are routinely determined by visual inspection of Fluorescence Microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput Microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during, and after the onset of various diseases. q 2006 International Society for Analytical Cytology Key terms: subcellular location trees; subcellular location features; pattern recognition; Fluorescence microscopy; location proteomics; cluster analysis
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Boosting accuracy of automated classification of Fluorescence Microscope images for location proteomics
BMC bioinformatics, 2004Co-Authors: Kai Huang, Robert F MurphyAbstract:Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D Fluorescence Microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D Fluorescence Microscope images. We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D Fluorescence Microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5–15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images. The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput Fluorescence Microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.
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feature reduction for improved recognition of subcellular location patterns in Fluorescence Microscope images
Biomedical optics, 2003Co-Authors: Kai Huang, Meel Velliste, Robert F MurphyAbstract:The central goal of proteomics is to clarify the mechanism by which each protein in a given cell type carries out its function. Automated protein subcellular location determination by Fluorescence microscopy can play an important role in fulfilling this goal. The subcellular location of a protein is critical to understanding its function because each subcellular compartment has a unique biochemical environment. We have previously shown that neural network classifiers using sets of numerical features computed from Fluorescence Microscope images were able to recognize all major subcellular location patterns with reasonable accuracy. Current classifiers are limited by under-determined classification boundaries due to the limited number of available images compared to the number of features. In this paper, we compare various feature reduction methods that can address this problem. Specifically, principal component analysis, kernel principal component analysis, nonlinear principal component analysis, independent component analysis, classification trees, fractal dimensionality reduction, stepwise discriminant analysis, and genetic algorithms are used to select feature subsets that are evaluated using support vector machine classifiers. The best results were obtained using stepwise discriminant analysis and we found that as few as eight features can provide good classification accuracy for all major subcellular patterns in HeLa cells.
Meel Velliste - One of the best experts on this subject based on the ideXlab platform.
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Automated interpretation of subcellular patterns in Fluorescence Microscope images for location proteomics
2015Co-Authors: Xiang Chen, Robert F Murphy, Meel VellisteAbstract:Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein’s subcellular location is essential to a complete understanding of its functions. Currently subcellular location patterns are routinely determined by visual inspection of Fluorescence Microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput Microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during and after the onset of various diseases
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automated interpretation of subcellular patterns in Fluorescence Microscope images for location proteomics
Cytometry Part A, 2006Co-Authors: Xiang Chen, Meel Velliste, Robert F MurphyAbstract:Proteomics, the large scale identification and characterization of many or all proteins expressed in a given cell type, has become a major area of biological research. In addition to information on protein sequence, structure and expression levels, knowledge of a protein’s subcellular location is essential to a complete understanding of its functions. Currently, subcellular location patterns are routinely determined by visual inspection of Fluorescence Microscope images. We review here research aimed at creating systems for automated, systematic determination of location. These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods. These methods have been shown to perform significantly better than human interpretation of the same images. When coupled with technologies for tagging large numbers of proteins and high-throughput Microscope systems, the computational methods reviewed here enable the new subfield of location proteomics. This subfield will make critical contributions in two related areas. First, it will provide structured, high-resolution information on location to enable Systems Biology efforts to simulate cell behavior from the gene level on up. Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during, and after the onset of various diseases. q 2006 International Society for Analytical Cytology Key terms: subcellular location trees; subcellular location features; pattern recognition; Fluorescence microscopy; location proteomics; cluster analysis
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feature reduction for improved recognition of subcellular location patterns in Fluorescence Microscope images
Biomedical optics, 2003Co-Authors: Kai Huang, Meel Velliste, Robert F MurphyAbstract:The central goal of proteomics is to clarify the mechanism by which each protein in a given cell type carries out its function. Automated protein subcellular location determination by Fluorescence microscopy can play an important role in fulfilling this goal. The subcellular location of a protein is critical to understanding its function because each subcellular compartment has a unique biochemical environment. We have previously shown that neural network classifiers using sets of numerical features computed from Fluorescence Microscope images were able to recognize all major subcellular location patterns with reasonable accuracy. Current classifiers are limited by under-determined classification boundaries due to the limited number of available images compared to the number of features. In this paper, we compare various feature reduction methods that can address this problem. Specifically, principal component analysis, kernel principal component analysis, nonlinear principal component analysis, independent component analysis, classification trees, fractal dimensionality reduction, stepwise discriminant analysis, and genetic algorithms are used to select feature subsets that are evaluated using support vector machine classifiers. The best results were obtained using stepwise discriminant analysis and we found that as few as eight features can provide good classification accuracy for all major subcellular patterns in HeLa cells.
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Automated determination of protein subcellular locations from 3D Fluorescence Microscope images
Proceedings IEEE International Symposium on Biomedical Imaging, 2002Co-Authors: Meel Velliste, Robert F MurphyAbstract:Knowing the subcellular location of a protein is critical to a full understanding of its function, and automated, objective methods for assigning locations are needed as part of the characterization process for the thousands of proteins expressed in each cell type. Fluorescence microscopy is the most common method used for determining subcellular location, and we have previously described automated systems that can recognize all major subcellular structures in 2D Fluorescence Microscope images. Here we show that 2D pattern recognition accuracy is dependent on the choice of the vertical position of the 2D slice through the cell and that classification of protein location patterns in 3D images results in higher accuracy than in 2D. In particular, automated analysis of 3D images provides excellent distinction between two Golgi proteins whose patterns are indistinguishable by visual examination.
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searching online journals for Fluorescence Microscope images depicting protein subcellular location patterns
Bioinformatics and Bioengineering, 2001Co-Authors: Robert F Murphy, Meel Velliste, Jie Yao, G PorrecaAbstract:There is extensive interest in automating the collection, organization and analysis of biological data. Data in the form of images present special challenges for such efforts. Since Fluorescence Microscope images are a primary source of information about the location of proteins within cells, we have set as a long-term goal the building of a knowledge base system that can interpret such images in online journals. To this end, we first developed a robot that searches online journals and finds Fluorescence Microscope images of individual cells. We then characterized the applicability of pattern classification methods we have previously used on images obtained under controlled conditions to images from different sources and to images subjected to manipulations commonly performed during publication. The results indicate the feasibility of developing search engines to find Fluorescence Microscope images depicting particular subcellular patterns.
Toshio Yanagida - One of the best experts on this subject based on the ideXlab platform.
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an artificial lipid bilayer formed on an agarose coated glass for simultaneous electrical and optical measurement of single ion channels
Biochemical and Biophysical Research Communications, 1999Co-Authors: Toru Ide, Toshio YanagidaAbstract:The purpose of this study is to develop an apparatus for simultaneous measurement of electrical and spectroscopic parameters of single ion channels. We have combined the single channel recording apparatus with an artificial lipid bilayer and a Fluorescence Microscope designed to detect single fluorescent molecules. The artificial membranes were formed on an agarose-coated glass and observed with an objective-type total internal reflection Fluorescence Microscope (TIRFM). The lateral motion of a single lipid molecule (β-BODIPY 530/550 HPC) was recorded. The lateral diffusion constant of the lipid molecule was calculated from the trajectories of single molecules as D = 8.5 ± 4.9 × 10−8 cm2/s. Ionic channels were incorporated into the membrane and current fluctuations were recorded at the single-channel level. After incorporation of Cy3-labeled alametithin molecules into the membrane, bright spots were observed moving rather slowly (D = 4.0 ± 1.6 × 10−8 cm2/s) in the membrane, simultaneously with the alametithin-channel current. These data show the possibility of the present technique for simultaneous measurement of electrical and spectroscopic parameters of single-channel activities.
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an artificial lipid bilayer formed on an agarose coated glass for simultaneous electrical and optical measurement of single ion channels
Biochemical and Biophysical Research Communications, 1999Co-Authors: Toru Ide, Toshio YanagidaAbstract:The purpose of this study is to develop an apparatus for simultaneous measurement of electrical and spectroscopic parameters of single ion channels. We have combined the single channel recording apparatus with an artificial lipid bilayer and a Fluorescence Microscope designed to detect single fluorescent molecules. The artificial membranes were formed on an agarose-coated glass and observed with an objective-type total internal reflection Fluorescence Microscope (TIRFM). The lateral motion of a single lipid molecule (beta-BODIPY 530/550 HPC) was recorded. The lateral diffusion constant of the lipid molecule was calculated from the trajectories of single molecules as D = 8.5 +/- 4.9 x 10(-8) cm(2)/s. Ionic channels were incorporated into the membrane and current fluctuations were recorded at the single-channel level. After incorporation of Cy3-labeled alametithin molecules into the membrane, bright spots were observed moving rather slowly (D = 4.0 +/- 1.6 x 10(-8) cm(2)/s) in the membrane, simultaneously with the alametithin-channel current. These data show the possibility of the present technique for simultaneous measurement of electrical and spectroscopic parameters of single-channel activities.
Kazuto Yamauchi - One of the best experts on this subject based on the ideXlab platform.
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full field x ray Fluorescence Microscope based on total reflection advanced kirkpatrick baez mirror optics
Optics Express, 2019Co-Authors: Satoshi Matsuyama, Jumpei Yamada, Yoshiki Kohmura, Makina Yabashi, Tetsuya Ishikawa, Kazuto YamauchiAbstract:A novel full-field X-ray Fluorescence Microscope based on total-reflection advanced Kirkpatrick–Baez mirror optics was developed. The total-reflection imaging mirror optics arrangement, with four reflections, has the advantage of being able to function both as a powerful low-pass energy filter, completely rejecting incident excitation X-rays, and as an achromatic optical imaging system. Isolated X-ray Fluorescence signals can be imaged, avoiding imaging-detector saturation, with low background noise. A prototype Fluorescence Microscope constructed at SPring-8 demonstrated the capability to simultaneously image elemental distributions using various X-ray Fluorescence signals (Ni, Cu, Zn, Ge, and Bi). A half-period spatial resolution of ~0.5–1 µm (1000–500 LP/mm) was achieved, owing to the achromaticity of the imaging mirrors and the photon-counting scheme of the CCD camera used for Fluorescence detection.
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development of scanning x ray Fluorescence Microscope with spatial resolution of 30 nm using kirkpatrick baez mirror optics
Review of Scientific Instruments, 2006Co-Authors: Satoshi Matsuyama, Makina Yabashi, Tetsuya Ishikawa, Hidekazu Mimura, Hirokatsu Yumoto, Yasuhisa Sano, Kazuya Yamamura, Yoshinori Nishino, K Tamasaku, Kazuto YamauchiAbstract:We developed a high-spatial-resolution scanning x-ray Fluorescence Microscope (SXFM) using Kirkpatrick-Baez mirrors. As a result of two-dimensional focusing tests at BL29XUL of SPring-8, the full width at half maximum of the focused beam was achieved to be 50×30nm2 (V×H) under the best focusing conditions. The measured beam profiles were in good agreement with simulated results. Moreover, beam size was controllable within the wide range of 30–1400nm by changing the virtual source size, although photon flux and size were in a trade-off relationship. To demonstrate SXFM performance, a fine test chart fabricated using focused ion beam system was observed to determine the best spatial resolution. The element distribution inside a logo mark of SPring-8 in the test chart, which has a minimum linewidth of approximately 50–60nm, was visualized with a spatial resolution better than 30nm using the smallest focused x-ray beam.
Dylan M Owen - One of the best experts on this subject based on the ideXlab platform.
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three dimensional total internal reflection Fluorescence nanoscopy with nanometric axial resolution by photometric localization of single molecules
Nature Communications, 2021Co-Authors: Alan M Szalai, Bruno Siarry, Jeronimo Lukin, David J Williamson, Nicolas Unsain, Alfredo Caceres, Mauricio Pilopais, Guillermo P Acuna, Damian Refojo, Dylan M OwenAbstract:Single-molecule localization microscopy enables far-field imaging with lateral resolution in the range of 10 to 20 nanometres, exploiting the fact that the centre position of a single-molecule’s image can be determined with much higher accuracy than the size of that image itself. However, attaining the same level of resolution in the axial (third) dimension remains challenging. Here, we present Supercritical Illumination Microscopy Photometric z-Localization with Enhanced Resolution (SIMPLER), a photometric method to decode the axial position of single molecules in a total internal reflection Fluorescence Microscope. SIMPLER requires no hardware modification whatsoever to a conventional total internal reflection Fluorescence Microscope and complements any 2D single-molecule localization microscopy method to deliver 3D images with nearly isotropic nanometric resolution. Performance examples include SIMPLER-direct stochastic optical reconstruction microscopy images of the nuclear pore complex with sub-20 nm axial localization precision and visualization of microtubule cross-sections through SIMPLER-DNA points accumulation for imaging in nanoscale topography with sub-10 nm axial localization precision. Achieving high axial resolution is challenging in single-molecule localization microscopy. Here, the authors present a photometric method to decode the axial position of single molecules in a total internal reflection Fluorescence Microscope without hardware modification, and show nearly isotropic nanometric resolution.
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three dimensional total internal reflection Fluorescence nanoscopy with nanometric axial resolution by photometric localization of single molecules
Nature Communications, 2021Co-Authors: Alan M Szalai, Bruno Siarry, Jeronimo Lukin, David J Williamson, Nicolas Unsain, Alfredo Caceres, Mauricio Pilopais, Guillermo P Acuna, Damian Refojo, Dylan M OwenAbstract:Single-molecule localization microscopy enables far-field imaging with lateral resolution in the range of 10 to 20 nanometres, exploiting the fact that the centre position of a single-molecule's image can be determined with much higher accuracy than the size of that image itself. However, attaining the same level of resolution in the axial (third) dimension remains challenging. Here, we present Supercritical Illumination Microscopy Photometric z-Localization with Enhanced Resolution (SIMPLER), a photometric method to decode the axial position of single molecules in a total internal reflection Fluorescence Microscope. SIMPLER requires no hardware modification whatsoever to a conventional total internal reflection Fluorescence Microscope and complements any 2D single-molecule localization microscopy method to deliver 3D images with nearly isotropic nanometric resolution. Performance examples include SIMPLER-direct stochastic optical reconstruction microscopy images of the nuclear pore complex with sub-20 nm axial localization precision and visualization of microtubule cross-sections through SIMPLER-DNA points accumulation for imaging in nanoscale topography with sub-10 nm axial localization precision.