Estimating Microtubule Distributions from 2D Immunofluorescence Microscopy Images Reveals Differences among Human Cultured Cell Lines
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{"title"=>"Estimating Microtubule Distributions from 2D Immunofluorescence Microscopy Images Reveals Differences among Human Cultured Cell Lines", "type"=>"journal", "authors"=>[{"first_name"=>"Jieyue", "last_name"=>"Li", "scopus_author_id"=>"55295720200"}, {"first_name"=>"Aabid", "last_name"=>"Shariff", "scopus_author_id"=>"35189570800"}, {"first_name"=>"Mikaela", "last_name"=>"Wiking", "scopus_author_id"=>"46161849700"}, {"first_name"=>"Emma", "last_name"=>"Lundberg", "scopus_author_id"=>"15765657800"}, {"first_name"=>"Gustavo K.", "last_name"=>"Rohde", "scopus_author_id"=>"7003382291"}, {"first_name"=>"Robert F.", "last_name"=>"Murphy", "scopus_author_id"=>"7403470459"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"366164712", "scopus"=>"2-s2.0-84870341521", "doi"=>"10.1371/journal.pone.0050292", "sgr"=>"84870341521", "issn"=>"19326203", "pmid"=>"23209697"}, "id"=>"4b85077c-97e4-337f-9bcc-fac8cfaf7ec8", "abstract"=>"Microtubules are filamentous structures that are involved in several important cellular processes, including cell division, cellular structure and mechanics, and intracellular transportation. Little is known about potential differences in microtubule distributions within and across cell lines. Here we describe a method to estimate information pertaining to 3D microtubule distributions from 2D fluorescence images. Our method allows for quantitative comparisons of microtubule distribution parameters (number of microtubules, mean length) between different cell lines. Among eleven cell lines compared, some showed differences that could be accounted for by differences in the total amount of tubulin per cell while others showed statistically significant differences in the balance between number and length of microtubules. We also observed that some cell lines that visually appear different in their microtubule distributions are quite similar when the model parameters are considered. The method is expected to be generally useful for comparing microtubule distributions between cell lines and for a given cell line after various perturbations. The results are also expected to enable analysis of the differences in gene expression underlying the observed differences in microtubule distributions among cell types.", "link"=>"http://www.mendeley.com/research/estimating-microtubule-distributions-2d-immunofluorescence-microscopy-images-reveals-differences-amo", "reader_count"=>30, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>3, "Researcher"=>4, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Student > Master"=>6, "Student > Bachelor"=>4, "Professor"=>1}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>3, "Researcher"=>4, "Student > Ph. D. Student"=>9, "Student > Postgraduate"=>3, "Student > Master"=>6, "Student > Bachelor"=>4, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>20, "Physics and Astronomy"=>2, "Chemistry"=>2, "Psychology"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Chemistry"=>{"Chemistry"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>20}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "United States"=>1, "France"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/535333"], "description"=>"<p>The framework contains two sub-systems, one for generating 3D synthetic images of distributions of microtubules (A), and one for estimating and comparing the model parameters of distribution of microtubules from real 2D images of eleven cell lines (B).</p>", "links"=>[], "tags"=>["overview", "introduced"], "article_id"=>205826, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.g002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_overview_of_the_framework_introduced_in_this_paper_/205826", "title"=>"An overview of the framework introduced in this paper.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 19:36:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/536173"], "description"=>"<p>Numbers shown for the parameters are MAPEs between the values used to synthesize an image in the validation bed and the estimated values obtained from matching of that image in the testing libraries.</p>", "links"=>[], "tags"=>["accuracies", "parameters", "synthetic", "2d", "images", "simulation"], "article_id"=>206661, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.t002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Estimated_accuracies_of_recovery_of_model_parameters_from_synthetic_2D_images_in_the_simulation_experiment_/206661", "title"=>"Estimated accuracies of recovery of model parameters from synthetic 2D images in the simulation experiment.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 19:41:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/535507"], "description"=>"<p>(<b>A</b>) Example of a real 2D cell image (tubulin channel) and its approximate bottom shape. (B) Cartoon of an X-Z projection of a cell on a substrate. (C) Example of a generated 3D cell shape containing 8 stacks (1.6 microns). (D) Illustration of inputs and outputs for the procedure.</p>", "links"=>[], "tags"=>["3d", "geometry", "2d", "slices", "microtubule", "nucleus"], "article_id"=>205995, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.g003", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generation_of_3D_cell_geometry_cell_shape_and_nuclear_shape_from_real_2D_slices_of_the_microtubule_and_nucleus_channels_/205995", "title"=>"Generation of 3D cell geometry (cell shape and nuclear shape) from real 2D slices of the microtubule and nucleus channels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 19:37:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/536245"], "description"=>"<p>The values in the second row are MAPEs of the recoveries of parameters from the 2D slices, assuming that the parameter estimates from the 3D images are correct.</p>", "links"=>[], "tags"=>["parameters", "microtubules", "3d", "hela", "images", "2d"], "article_id"=>206732, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.t001", "stats"=>{"downloads"=>1, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparisons_of_estimated_parameters_of_distribution_of_microtubules_between_original_3D_HeLa_images_and_their_2D_central_slices_/206732", "title"=>"Comparisons of estimated parameters of distribution of microtubules between original 3D HeLa images and their 2D central slices.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 19:41:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/536204"], "description"=>"<p>The lower triangular part of the table is for the testing of equality of the bivariate mean of the distribution of two estimated microtubule parameters (number of microtubules and mean of length) between cell lines using Hotelling's <i>T<sup>2</sup></i> test. The upper part (Italic) is for testing of the equality of the bivariate mean of the distribution of the first two principal components (learned from and representing the multivariate distribution of features on real cells). The rows and columns of the table are sorted according to the tree from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050292#pone-0050292-g007\" target=\"_blank\"><b>Figure 7 (A)</b></a>. The <i>p</i>-values are adjusted according to the family-wise Bonferroni correction for multiple testing. The “<i>*</i>” denotes cell lines which differ at significance level <i>alpha</i> = 0.05. The number in the parenthesis of the first column is the number of cells from each cell line.</p>", "links"=>[], "tags"=>["parameters"], "article_id"=>206704, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.t003", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_tests_of_the_model_parameters_and_the_features_between_cell_lines_/206704", "title"=>"Statistical tests of the model parameters and the features between cell lines.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 19:41:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/536025"], "description"=>"<p>The correlation coefficient for each cell line is shown in the legend.</p>", "links"=>[], "tags"=>["polymerized", "tubulin", "microtubules", "fluorescence", "images"], "article_id"=>206513, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.g008", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plot_of_the_estimated_total_amount_of_polymerized_tubulin_the_product_of_the_estimate_number_of_microtubules_and_the_mean_length_versus_the_total_tubulin_fluorescence_intensity_of_real_images_from_eleven_cell_lines_/206513", "title"=>"Scatter plot of the estimated total amount of polymerized tubulin (the product of the estimate number of microtubules and the mean length) versus the total tubulin fluorescence intensity of real images from eleven cell lines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 19:40:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/535930"], "description"=>"<p>The trees were built on the pairwise <i>Hotelling's T<sup>2</sup></i> statistics from (A) the testing of the bivariate distributions of the estimated number of microtubules and mean length and (B) from the testing of the bivariate distributions of the first two principal components of the multivariate features computed from the real images.</p>", "links"=>[], "tags"=>["clustering", "trees"], "article_id"=>206419, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.g007", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hierarchical_clustering_trees_of_eleven_cell_lines_/206419", "title"=>"Hierarchical clustering trees of eleven cell lines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 19:39:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/536088"], "description"=>"<p>The correlation coefficient for each cell line is shown in the legend.</p>", "links"=>[], "tags"=>["polymerized", "tubulin", "cytosolic", "cells"], "article_id"=>206580, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.g009", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_plot_of_the_estimated_total_amount_of_polymerized_tubulin_versus_the_area_of_cytosolic_space_sum_of_pixels_for_real_cells_from_eleven_cell_lines_/206580", "title"=>"Scatter plot of the estimated total amount of polymerized tubulin versus the area of cytosolic space (sum of pixels) for real cells from eleven cell lines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 19:40:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/535738"], "description"=>"<p>There are two sets of three columns for the model parameters (number of microtubules, mean of the length distribution and collinearity) in each row. The cell lines (from top to bottom) are U-251MG, A-549, MCF-7, Hep-G2, A-431 and HeLa in the left column, and CaCo2, PC-3, RT-4, Hek-293, and U-20S in the right.</p>", "links"=>[], "tags"=>["distributions", "parameters", "2d", "images"], "article_id"=>206228, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.g005", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Frequency_distributions_of_all_estimated_parameters_from_real_2D_images_for_all_cell_lines_/206228", "title"=>"Frequency distributions of all estimated parameters from real 2D images for all cell lines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 19:38:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/535840"], "description"=>"<p>The ellipses are centered at the bivariate means of the two parameters and contain about 67% to 80% of the cells for a particular cell line (at most 1.5 standard deviations from the means).</p>", "links"=>[], "tags"=>["bivariate", "distributions", "parameters"], "article_id"=>206332, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.g006", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_bivariate_distributions_of_the_estimated_model_parameters_of_the_eleven_cell_lines_/206332", "title"=>"Comparison of the bivariate distributions of the estimated model parameters of the eleven cell lines.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 19:39:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/535619"], "description"=>"<p>2D real images are shown on the left, and center slices of the best-matching 3D synthetic images are shown on the right. (A) A-431 cell line, Number of microtubules = 250, Mean of length distribution = 30 microns, Collinearity = 0.97000; (B) U-2OS cell line, Number of microtubules = 250, Mean of length distribution = 30 microns, Collinearity = 0.98466; (C) U-251MG cell line, Number of microtubules = 250, Mean of length distribution = 20 microns, Collinearity = 0.99610.</p>", "links"=>[], "tags"=>["estimating", "parameters", "matching", "simulated"], "article_id"=>206109, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.g004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_for_estimating_parameters_values_by_matching_to_simulated_images_/206109", "title"=>"Examples for estimating parameters values by matching to simulated images.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 19:38:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/535270"], "description"=>"<p>Each microtubule starts from the centrosome, and randomly grows to the second point on the lateral surface of a cone whose aperture is 2α. Then the microtubule grows the same way until it hits the cell or nuclear shape boundary and is not able to step further within the cytosolic area. At this time, we relax the collinearity requirement but still confine the next direction under the local constraint α<sub>local</sub>. Moreover, we also keep on checking a consecutive multiple (30) steps, and require that there are less than or equal to 3 pairwise vector angles that are larger than the global constraint α<sub>global</sub>. Beginning with an empty (black) cytosolic area (shaped by cell and nuclear boundary), we add one to the intensity of the pixel which a microtubule crosses. In this paper, we used every step of growth to be 0.2 microns (1 pixel). For the two constraints on the collinearity which controls the curvature of each microtubule and the local and global rebounding issues, we used α<sub>local</sub> to be 63.9 degrees and α<sub>global</sub> to be 120 degrees. The figure only illustrates the procedure of growth in 2D for better visualization but can be easily imagined to extend to 3D.</p>", "links"=>[], "tags"=>["generating", "microtubules"], "article_id"=>205758, "categories"=>["Biotechnology", "Biochemistry", "Cell Biology", "Genetics", "Biological Sciences"], "users"=>["Jieyue Li", "Aabid Shariff", "Mikaela Wiking", "Emma Lundberg", "Gustavo K. Rohde", "Robert F. Murphy"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050292.g001", "stats"=>{"downloads"=>0, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Growth_model_for_generating_microtubules_dependent_on_cell_and_nuclear_shapes_/205758", "title"=>"Growth model for generating microtubules dependent on cell and nuclear shapes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 19:36:22"}

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Relative Metric

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