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{"title"=>"A new method to address unmet needs for extracting individual cell migration features from a large number of cells embedded in 3D volumes", "type"=>"journal", "authors"=>[{"first_name"=>"Ivan", "last_name"=>"Adanja", "scopus_author_id"=>"24766356400"}, {"first_name"=>"Véronique", "last_name"=>"Megalizzi", "scopus_author_id"=>"16316607300"}, {"first_name"=>"Olivier", "last_name"=>"Debeir", "scopus_author_id"=>"55902408700"}, {"first_name"=>"Christine", "last_name"=>"Decaestecker", "scopus_author_id"=>"7005106147"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-79960315823", "sgr"=>"79960315823", "pmid"=>"21789244", "issn"=>"19326203", "pui"=>"362136978", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0022263"}, "id"=>"f469a7ea-7015-3fd2-bd23-186d99624105", "abstract"=>"Background: In vitro cell observation has been widely used by biologists and pharmacologists for screening molecule-induced effects on cancer cells. Computer-assisted time-lapse microscopy enables automated live cell imaging in vitro, enabling cell behavior characterization through image analysis, in particular regarding cell migration. In this context, 3D cell assays in transparent matrix gels have been developed to provide more realistic in vitro 3D environments for monitoring cell migration (fundamentally different from cell motility behavior observed in 2D), which is related to the spread of cancer and metastases. Methodology/Principal Findings: In this paper we propose an improved automated tracking method that is designed to robustly and individually follow a large number of unlabeled cells observed under phase-contrast microscopy in 3D gels. The method automatically detects and tracks individual cells across a sequence of acquired volumes, using a template matching filtering method that in turn allows for robust detection and mean-shift tracking. The robustness of the method results from detecting and managing the cases where two cell (mean-shift) trackers converge to the same point. The resulting trajectories quantify cell migration through statistical analysis of 3D trajectory descriptors. We manually validated the method and observed efficient cell detection and a low tracking error rate (6%). We also applied the method in a real biological experiment where the pro-migratory effects of hyaluronic acid (HA) were analyzed on brain cancer cells. Using collagen gels with increased HA proportions, we were able to evidence a dose-response effect on cell migration abilities. Conclusions/Significance: The developed method enables biomedical researchers to automatically and robustly quantify the pro-or anti-migratory effects of different experimental conditions on unlabeled cell cultures in a 3D environment.", "link"=>"http://www.mendeley.com/research/new-method-address-unmet-needs-extracting-individual-cell-migration-features-large-number-cells-embe", "reader_count"=>28, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>12, "Other"=>2, "Student > Master"=>2, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>12, "Other"=>2, "Student > Master"=>2, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>4, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Materials Science"=>1, "Agricultural and Biological Sciences"=>13, "Medicine and Dentistry"=>1, "Design"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>1, "Chemistry"=>1, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>4}, "Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>13}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Belgium"=>1}, "group_count"=>0}

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• http://doi.org/10.1371/journal.pone.0186465
• http://doi.org/10.1038/s41598-017-14458-x
• http://doi.org/10.1117/1.JMI.5.2.024005
• http://doi.org/10.1186/s12859-015-0759-x
• http://doi.org/10.1016/j.tcb.2015.09.003

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/756248"], "description"=>"<p>The Z-slices of the phase-contrast (top) and correlated (bottom) volumes are centered on phase-contrast interferences falsely detected as a cell (blue dot). The actual cells are visible in the phase-contrast slice as dark elongations touching.</p>", "links"=>[], "tags"=>["detection", "phase-contrast"], "article_id"=>426625, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.g007", "stats"=>{"downloads"=>3, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_False_cell_detection_due_to_phase_contrast_interferences_/426625", "title"=>"False cell detection due to phase-contrast interferences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-15 01:50:25"}
• {"files"=>["https://ndownloader.figshare.com/files/755804"], "description"=>"<p>The gray boxes are significant improvements with regards to our preliminary work <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022263#pone.0022263-Adanja1\" target=\"_blank\">[2]</a>.</p>", "links"=>[], "tags"=>["schematic", "tracking", "algorithm"], "article_id"=>426175, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_schematic_representation_of_the_tracking_algorithm_steps_/426175", "title"=>"A schematic representation of the tracking algorithm steps.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-15 01:42:55"}
• {"files"=>["https://ndownloader.figshare.com/files/755965"], "description"=>"<p>Illustration of (a part of) a single Z-slice containing 3 cells in focus (pointed by arrows) submitted to the different segmentation steps (correlated phase-contrast, mask, softMax and the final segmented volume). The magnified region of interest shown in the upper left corner of each image is centered on the cell located in the middle of the image.</p>", "links"=>[], "tags"=>["detection", "correlated"], "article_id"=>426340, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cell_detection_in_a_correlated_volume_/426340", "title"=>"Cell detection in a correlated volume.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-15 01:45:40"}
• {"files"=>["https://ndownloader.figshare.com/files/756572"], "description"=>"<p>Collagen was provided by PureColTM (Nutacon, Netherlands) and hyaluronic acid was kindly provided by Auriga International (Braine-l'Alleud, Belgium).</p>", "links"=>[], "tags"=>["gel", "compositions"], "article_id"=>426950, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.t002", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_3D_gel_compositions_in_the_different_experiments_/426950", "title"=>"3D gel compositions in the different experiments.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-07-15 01:55:50"}
• {"files"=>["https://ndownloader.figshare.com/files/756350"], "description"=>"<p>Set1 collects trajectories which are generated from the 1st volume without additional use of the cell detection step in the next volumes. Set2 collects all the other cell trajectories which are initialized later in the sequence thanks to this detection step. The graph shows the number of active trajectories (i.e., which are not stopped because of a collision with another cell tracker) in each set, and their union, over time.</p>", "links"=>[], "tags"=>["trajectories"], "article_id"=>426727, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.g008", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_active_cell_trajectories_across_a_sequence_/426727", "title"=>"Number of active cell trajectories across a sequence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-15 01:52:07"}
• {"files"=>["https://ndownloader.figshare.com/files/756546"], "description"=>"<p>Details of the analysis are provided in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022263#pone.0022263.s001\" target=\"_blank\">File S1</a>.</p>", "links"=>[], "tags"=>["3d"], "article_id"=>426921, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.t001", "stats"=>{"downloads"=>3, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Related_works_on_3D_cell_tracking_/426921", "title"=>"Related works on 3D cell tracking.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-07-15 01:55:21"}
• {"files"=>["https://ndownloader.figshare.com/files/756033"], "description"=>"<p>The timeline bellow the schematic frames presents the start and end points of the 4 trajectories obtained in this situation.</p>", "links"=>[], "tags"=>["tracker", "paths"], "article_id"=>426411, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.g004", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_illustration_of_tracker_management_when_two_cell_paths_intersect_/426411", "title"=>"Schematic illustration of tracker management when two cell paths intersect.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-15 01:46:51"}
• {"files"=>["https://ndownloader.figshare.com/files/756165"], "description"=>"<p>An elongated cell (blue dot) spawns a second parasite tracker (red dot) on a body extension (on the cell tail before its retraction when migrating). The secondary tracker is discarded as it converges onto the cell's original tracker within a couple of frames. The images show Z-slices centered on the cell in the phase-contrast (1st row) and correlated (2nd row) volumes. The timeline below the images is similar to that in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022263#pone-0022263-g004\" target=\"_blank\">figure 4</a>.</p>", "links"=>[], "tags"=>["elongated"], "article_id"=>426541, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.g006", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Tracking_of_an_elongated_cell_/426541", "title"=>"Tracking of an elongated cell.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-15 01:49:01"}
• {"files"=>["https://ndownloader.figshare.com/files/381120", "https://ndownloader.figshare.com/files/381176", "https://ndownloader.figshare.com/files/381202", "https://ndownloader.figshare.com/files/381225", "https://ndownloader.figshare.com/files/381247", "https://ndownloader.figshare.com/files/381263", "https://ndownloader.figshare.com/files/381296"], "description"=>"<div><h3>Background</h3><p>In vitro cell observation has been widely used by biologists and pharmacologists for screening molecule-induced effects on cancer cells. Computer-assisted time-lapse microscopy enables automated live cell imaging in vitro, enabling cell behavior characterization through image analysis, in particular regarding cell migration. In this context, 3D cell assays in transparent matrix gels have been developed to provide more realistic in vitro 3D environments for monitoring cell migration (fundamentally different from cell motility behavior observed in 2D), which is related to the spread of cancer and metastases.</p> <h3>Methodology/Principal Findings</h3><p>In this paper we propose an improved automated tracking method that is designed to robustly and individually follow a large number of unlabeled cells observed under phase-contrast microscopy in 3D gels. The method automatically detects and tracks individual cells across a sequence of acquired volumes, using a template matching filtering method that in turn allows for robust detection and mean-shift tracking. The robustness of the method results from detecting and managing the cases where two cell (mean-shift) trackers converge to the same point. The resulting trajectories quantify cell migration through statistical analysis of 3D trajectory descriptors. We manually validated the method and observed efficient cell detection and a low tracking error rate (6%). We also applied the method in a real biological experiment where the pro-migratory effects of hyaluronic acid (HA) were analyzed on brain cancer cells. Using collagen gels with increased HA proportions, we were able to evidence a dose-response effect on cell migration abilities.</p> <h3>Conclusions/Significance</h3><p>The developed method enables biomedical researchers to automatically and robustly quantify the pro- or anti-migratory effects of different experimental conditions on unlabeled cell cultures in a 3D environment.</p> </div>", "links"=>[], "tags"=>["unmet", "needs", "extracting", "features", "cells", "embedded", "3d", "volumes"], "article_id"=>135286, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0022263.s001", "https://dx.doi.org/10.1371/journal.pone.0022263.s002", "https://dx.doi.org/10.1371/journal.pone.0022263.s003", "https://dx.doi.org/10.1371/journal.pone.0022263.s004", "https://dx.doi.org/10.1371/journal.pone.0022263.s005", "https://dx.doi.org/10.1371/journal.pone.0022263.s006", "https://dx.doi.org/10.1371/journal.pone.0022263.s007"], "stats"=>{"downloads"=>12, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_New_Method_to_Address_Unmet_Needs_for_Extracting_Individual_Cell_Migration_Features_from_a_Large_Number_of_Cells_Embedded_in_3D_Volumes/135286", "title"=>"A New Method to Address Unmet Needs for Extracting Individual Cell Migration Features from a Large Number of Cells Embedded in 3D Volumes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-07-15 01:28:06"}
• {"files"=>["https://ndownloader.figshare.com/files/755865"], "description"=>"<p>(a) Cell and collagen gel aspects in an original phase-contrast Z-slice with a zoom showing the collagen fibers. (b) Schematic presentation of the 3D time-lapse sequence acquisition. Detailed cell aspect in (c) the original phase-contrast volume (after contrast enhancement) and (d) the correlated volume, illustrated by several Z-slices, a XZ cut plane (vertical slice) and an intensity isosurface 3D view.</p>", "links"=>[], "tags"=>["3d", "collagen"], "article_id"=>426239, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.g002", "stats"=>{"downloads"=>3, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cell_appearance_in_3D_collagen_gels_/426239", "title"=>"Cell appearance in 3D collagen gels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-15 01:43:59"}
• {"files"=>["https://ndownloader.figshare.com/files/756081"], "description"=>"<p>The new cells result from (frame 2) a cell division or (frame 3) a cell entry into the observed volume. The trajectories are rendered on (1a–3a) the average intensity Z projections from the correlated volumes and (1b–3b) the corresponding intensity isosurfaces (3D view).</p>", "links"=>[], "tags"=>["cells"], "article_id"=>426461, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.g005", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Detection_of_new_cells_in_a_volume_sequence_/426461", "title"=>"Detection of new cells in a volume sequence.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-15 01:47:41"}
• {"files"=>["https://ndownloader.figshare.com/files/756445"], "description"=>"<p>(a) These trajectories are observed on the complete sequences in the absence (p42h0) or presence (p24h18) of 24% of HA in the collagen gel (b) The features (labeled on the Y axes) quantify the migration abilities of U373 cells in 3D gels which included a progressive increase of HA amount (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022263#pone-0022263-t002\" target=\"_blank\">Table 2</a>). The data distributions are presented by their median (square), inter-quartile range (box) and non-outlier range (bars).</p>", "links"=>[], "tags"=>["cancer", "trajectories", "extracted", "characterizing", "3d"], "article_id"=>426815, "categories"=>["Information And Computing Sciences", "Biotechnology", "Developmental Biology", "Pharmacology"], "users"=>["Ivan Adanja", "Véronique Megalizzi", "Olivier Debeir", "Christine Decaestecker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0022263.g009", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_U373_cancer_cell_trajectories_and_the_extracted_features_characterizing_3D_migration_behavior_/426815", "title"=>"U373 cancer cell trajectories and the extracted features characterizing 3D migration behavior.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-07-15 01:53:35"}

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Relative Metric

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F1000Prime