Whole Organism High-Content Screening by Label-Free, Image-Based Bayesian Classification for Parasitic Diseases
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{"title"=>"Whole organism high-content screening by label-free, image-based bayesian classification for parasitic diseases", "type"=>"journal", "authors"=>[{"first_name"=>"Ross A.", "last_name"=>"Paveley", "scopus_author_id"=>"15761073000"}, {"first_name"=>"Nuha R.", "last_name"=>"Mansour", "scopus_author_id"=>"23987087100"}, {"first_name"=>"Irene", "last_name"=>"Hallyburton", "scopus_author_id"=>"6504538306"}, {"first_name"=>"Leo S.", "last_name"=>"Bleicher", "scopus_author_id"=>"57190155727"}, {"first_name"=>"Alex E.", "last_name"=>"Benn", "scopus_author_id"=>"57132599800"}, {"first_name"=>"Ivana", "last_name"=>"Mikic", "scopus_author_id"=>"6602500284"}, {"first_name"=>"Alessandra", "last_name"=>"Guidi", "scopus_author_id"=>"36883900900"}, {"first_name"=>"Ian H.", "last_name"=>"Gilbert", "scopus_author_id"=>"7006693300"}, {"first_name"=>"Andrew L.", "last_name"=>"Hopkins", "scopus_author_id"=>"7102307405"}, {"first_name"=>"Quentin D.", "last_name"=>"Bickle", "scopus_author_id"=>"7003745671"}], "year"=>2012, "source"=>"PLoS Neglected Tropical Diseases", "identifiers"=>{"pmid"=>"22860151", "doi"=>"10.1371/journal.pntd.0001762", "sgr"=>"84864615611", "scopus"=>"2-s2.0-84864615611", "issn"=>"19352727", "pui"=>"365384895"}, "id"=>"b04ea89e-b513-377e-bc86-34cf499a8371", "abstract"=>"Sole reliance on one drug, Praziquantel, for treatment and control of schistosomiasis raises concerns about development of widespread resistance, prompting renewed interest in the discovery of new anthelmintics. To discover new leads we designed an automated label-free, high content-based, high throughput screen (HTS) to assess drug-induced effects on in vitro cultured larvae (schistosomula) using bright-field imaging. Automatic image analysis and Bayesian prediction models define morphological damage, hit/non-hit prediction and larval phenotype characterization. Motility was also assessed from time-lapse images. In screening a 10,041 compound library the HTS correctly detected 99.8% of the hits scored visually. A proportion of these larval hits were also active in an adult worm ex-vivo screen and are the subject of ongoing studies. The method allows, for the first time, screening of large compound collections against schistosomes and the methods are adaptable to other whole organism and cell-based screening by morphology and motility phenotyping.", "link"=>"http://www.mendeley.com/research/whole-organism-highcontent-screening-labelfree-imagebased-bayesian-classification-parasitic-diseases", "reader_count"=>52, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Student > Doctoral Student"=>3, "Researcher"=>13, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>5, "Student > Bachelor"=>4, "Lecturer"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Student > Doctoral Student"=>3, "Researcher"=>13, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>1, "Student > Master"=>5, "Other"=>5, "Student > Bachelor"=>4, "Lecturer"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Agricultural and Biological Sciences"=>29, "Medicine and Dentistry"=>2, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Chemistry"=>9, "Computer Science"=>1, "Immunology and Microbiology"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>9}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>29}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Unspecified"=>{"Unspecified"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}}, "reader_count_by_country"=>{"United States"=>2, "Brazil"=>5, "Denmark"=>1, "United Kingdom"=>1, "Switzerland"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/314153", "https://ndownloader.figshare.com/files/314243", "https://ndownloader.figshare.com/files/314275", "https://ndownloader.figshare.com/files/314420"], "description"=>"<div><p>Sole reliance on one drug, Praziquantel, for treatment and control of schistosomiasis raises concerns about development of widespread resistance, prompting renewed interest in the discovery of new anthelmintics. To discover new leads we designed an automated label-free, high content-based, high throughput screen (HTS) to assess drug-induced effects on <em>in vitro</em> cultured larvae (schistosomula) using bright-field imaging. Automatic image analysis and Bayesian prediction models define morphological damage, hit/non-hit prediction and larval phenotype characterization. Motility was also assessed from time-lapse images. In screening a 10,041 compound library the HTS correctly detected 99.8% of the hits scored visually. A proportion of these larval hits were also active in an adult worm <em>ex-vivo</em> screen and are the subject of ongoing studies. The method allows, for the first time, screening of large compound collections against schistosomes and the methods are adaptable to other whole organism and cell-based screening by morphology and motility phenotyping.</p> </div>", "links"=>[], "tags"=>["high-content", "image-based", "bayesian", "classification", "parasitic", "diseases"], "article_id"=>121949, "categories"=>["Biotechnology", "Science Policy", "Pharmacology"], "users"=>["Ross A. Paveley", "Nuha R. Mansour", "Irene Hallyburton", "Leo S. Bleicher", "Alex E. Benn", "Ivana Mikic", "Alessandra Guidi", "Ian H. Gilbert", "Andrew L. Hopkins", "Quentin D. Bickle"], "doi"=>["https://dx.doi.org/10.1371/journal.pntd.0001762.s001", "https://dx.doi.org/10.1371/journal.pntd.0001762.s002", "https://dx.doi.org/10.1371/journal.pntd.0001762.s003", "https://dx.doi.org/10.1371/journal.pntd.0001762.s004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Whole_Organism_High_Content_Screening_by_Label_Free_Image_Based_Bayesian_Classification_for_Parasitic_Diseases/121949", "title"=>"Whole Organism High-Content Screening by Label-Free, Image-Based Bayesian Classification for Parasitic Diseases", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-07-31 00:32:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/598753"], "description"=>"<p>Images for analysis were captured on an Images Xpress<sup>micro</sup> (<b>A</b>)<b>.</b> Initially an adaptive threshold was applied to each individual pixel within the image to highlight objects within the image (<b>B</b>)<b>.</b> Objects highlighted by the threshold where then closed to encapsulate whole larvae using basic greyscale morphology. For each object, background and centre points were calculated so watershed segmentation could be applied to enhance segmentation of the larvae. Filters based on area, perimeter and form factor were then applied to the mask to remove any objects too large or small to be individual larvae taking into account whether 10× or 4× images were being analysed (<b>C</b>). The individual objects were then traced (<b>D</b>) before the mask (red) was applied to the original image (<b>E</b>). The completed mask was then broken down to separate objects and applied to the original images so individual larvae could be segmented from the background.</p>", "links"=>[], "tags"=>["segmentation", "praziquantel", "treated"], "article_id"=>269234, "categories"=>["Biotechnology", "Science Policy", "Pharmacology"], "users"=>["Ross A. Paveley", "Nuha R. Mansour", "Irene Hallyburton", "Leo S. Bleicher", "Alex E. Benn", "Ivana Mikic", "Alessandra Guidi", "Ian H. Gilbert", "Andrew L. Hopkins", "Quentin D. Bickle"], "doi"=>["https://dx.doi.org/10.1371/journal.pntd.0001762.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Representative_example_of_the_segmentation_of_control_and_praziquantel_treated_schistosomula_/269234", "title"=>"Representative example of the segmentation of control and praziquantel treated schistosomula.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-31 02:33:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/598921"], "description"=>"<p>(<b>A</b>) Phenotype scores produced by the Bayesian prediction model from 5 replicate plates containing controls (M169/DMSO) or anti-schistosome drugs (10 µg/ml), praziquantel (PZQ), oltipraz (OPZ), methylclonazepam (MCZ), Ro15-5458 (Ro15), oxamniquine (OX) and dihydroartemisinin (DHA). N = 120 wells per condition and significance values were measured by non-parametric one-way ANOVA with individual drug treatment being compared to the DMSO controls wells by a Dunn's comparison post test, *** = p<0.001. NS; not significant. (<b>B</b>) Z factor analysis based on the distribution of the negative (DMSO; circle) and positive controls (OPZ; triangle) from 15 analyzed plates of anti-schistosome compounds (dotted lines represent the upper and lower thresholds). (<b>C</b>) Typical larval phenotypes and phenotype scores induced by the anti-schistosome drug treatments or media (M169) and solvent (DMSO) controls. (<b>D</b>) Drug treatment class prediction for wells treated with the anti-schistosome drugs and the mean number of correctly predicted larvae per well.</p>", "links"=>[], "tags"=>["larval", "phenotype", "anti-schistosome"], "article_id"=>269407, "categories"=>["Biotechnology", "Science Policy", "Pharmacology"], "users"=>["Ross A. Paveley", "Nuha R. Mansour", "Irene Hallyburton", "Leo S. Bleicher", "Alex E. Benn", "Ivana Mikic", "Alessandra Guidi", "Ian H. Gilbert", "Andrew L. Hopkins", "Quentin D. Bickle"], "doi"=>["https://dx.doi.org/10.1371/journal.pntd.0001762.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Automated_image_analysis_of_larval_phenotype_following_treatment_with_anti_schistosome_drugs_/269407", "title"=>"Automated image analysis of larval phenotype following treatment with anti-schistosome drugs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-31 02:36:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/599101"], "description"=>"<p>Original images were firstly segmented as described <a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001762#pntd-0001762-g001\" target=\"_blank\"><b>Figure 1</b></a> and then the cumulative change in area for each individual larva measured over 5 time frames. The scores for individual larvae were averaged to generate a motility score per well. (<b>A</b>) Illustration of differences in larval motility between a high motility well (DMSO) with low overlap between larval location in successive time frames (red boundaries), a medium motility well (PZQ; praziquantel) and low motility wells with almost perfect overlap between successive time frame boundaries (DHA; dihydroartemisinin & OPZ; oltipraz). (<b>B</b>) Representative motility scores from 5 plates of anti-schistosome compounds (10 µg/ml), n = 120 wells per condition and significance values were measured by non-parametric one-way ANOVA with individual drug treatments being compared to the DMSO controls wells using a Dunn's comparison post test, *** = p<0.001. NS; not significant. (<b>C</b>) Z factor analysis based on the distribution of the negative (DMSO; open circle) and positive controls (OPZ; closed triangle) from 15 plates (dotted lines represent upper and lower thresholds).</p>", "links"=>[], "tags"=>["larval", "motility", "anti-schistosome"], "article_id"=>269585, "categories"=>["Biotechnology", "Science Policy", "Pharmacology"], "users"=>["Ross A. Paveley", "Nuha R. Mansour", "Irene Hallyburton", "Leo S. Bleicher", "Alex E. Benn", "Ivana Mikic", "Alessandra Guidi", "Ian H. Gilbert", "Andrew L. Hopkins", "Quentin D. Bickle"], "doi"=>["https://dx.doi.org/10.1371/journal.pntd.0001762.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Automated_image_analysis_of_larval_motility_following_treatment_with_anti_schistosome_compounds_/269585", "title"=>"Automated image analysis of larval motility following treatment with anti-schistosome compounds.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-31 02:39:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/599256"], "description"=>"<p>(<b>A</b>) Combined phenotype and motility scores for larvae treated with 10 µM praziquantel (PZQ; red), oltipraz (OPZ; black), methylclonazepam (MCZ; orange), Ro15-5458 (Ro15; blue), oxamniquine (OX; purple) dihydroartemisinin (DHA; yellow) and controls (M169; aqua & DMSO; green). (<b>B</b>) Visual assessment of hits (red) or non-hits (blue) for the wells shown in (<b>A</b>) <b>with a</b> visual hit defined as ≥70% damaged or dead schistosomula within a well <a href=\"http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001762#pntd.0001762-Mansour1\" target=\"_blank\">[21]</a>. Based on this, the dotted line shows the thresholds selected for definition of the hit region (−0.15 for the phenotype score and −0.35 for the motility score). N = 1500 wells across 5 plates containing anti-schistosome drugs.</p>", "links"=>[], "tags"=>["phenotype", "motility", "compared", "larvae", "treated", "anti-schistosome"], "article_id"=>269732, "categories"=>["Biotechnology", "Science Policy", "Pharmacology"], "users"=>["Ross A. Paveley", "Nuha R. Mansour", "Irene Hallyburton", "Leo S. Bleicher", "Alex E. Benn", "Ivana Mikic", "Alessandra Guidi", "Ian H. Gilbert", "Andrew L. Hopkins", "Quentin D. Bickle"], "doi"=>["https://dx.doi.org/10.1371/journal.pntd.0001762.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Combined_phenotype_and_motility_analysis_compared_with_visual_hit_assessment_for_larvae_treated_with_known_anti_schistosome_compounds_/269732", "title"=>"Combined phenotype and motility analysis compared with visual hit assessment for larvae treated with known anti-schistosome compounds.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-31 02:42:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/599419"], "description"=>"<p>(<b>A</b>) Combined phenotype and motility analysis of wells treated with 10 µM of the compounds. Visually assessed hits are shown in red, non-hits in blue and non-hits with compound crystals in the well in yellow. The hit region for automated image analysis as determined above using the anti-schistosome compounds is delineated by the dashed lines. (<b>B</b>) Performance of the Automated image analysis categorization compared to the visual assessments. (<b>C</b>) Z factor analysis of phenotype (open) and motility (closed) per plate of the Dundee compound library.</p>", "links"=>[], "tags"=>["larval", "phenotype", "motility", "dundee"], "article_id"=>269902, "categories"=>["Biotechnology", "Science Policy", "Pharmacology"], "users"=>["Ross A. Paveley", "Nuha R. Mansour", "Irene Hallyburton", "Leo S. Bleicher", "Alex E. Benn", "Ivana Mikic", "Alessandra Guidi", "Ian H. Gilbert", "Andrew L. Hopkins", "Quentin D. Bickle"], "doi"=>["https://dx.doi.org/10.1371/journal.pntd.0001762.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Analysis_of_larval_phenotype_and_motility_for_the_Dundee_10_041_Compound_Library_/269902", "title"=>"Analysis of larval phenotype and motility for the Dundee 10,041 Compound Library.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-31 02:45:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/599535"], "description"=>"<p>Phenotype (<b>A</b>) and motility (<b>B</b>) results from initial screening (Phenotype/Motility score 1) and repeat screening (Phenotype/Motility score 2). Hits which repeated (red), non-hits which repeated (blue) and tests which did not repeat (yellow).</p>", "links"=>[], "tags"=>["non-hit"], "article_id"=>270021, "categories"=>["Biotechnology", "Science Policy", "Pharmacology"], "users"=>["Ross A. Paveley", "Nuha R. Mansour", "Irene Hallyburton", "Leo S. Bleicher", "Alex E. Benn", "Ivana Mikic", "Alessandra Guidi", "Ian H. Gilbert", "Andrew L. Hopkins", "Quentin D. Bickle"], "doi"=>["https://dx.doi.org/10.1371/journal.pntd.0001762.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reconfirmation_screen_of_selected_hit_and_non_hit_compounds_/270021", "title"=>"Reconfirmation screen of selected hit and non-hit compounds.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-31 00:00:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/599656"], "description"=>"<p>Larval phenotype and motility scores for the compounds which were tested in the secondary adult assay. Adult hits are depicted in red and non-hits in grey.</p>", "links"=>[], "tags"=>["larval", "hits", "dundee"], "article_id"=>270137, "categories"=>["Biotechnology", "Science Policy", "Pharmacology"], "users"=>["Ross A. Paveley", "Nuha R. Mansour", "Irene Hallyburton", "Leo S. Bleicher", "Alex E. Benn", "Ivana Mikic", "Alessandra Guidi", "Ian H. Gilbert", "Andrew L. Hopkins", "Quentin D. Bickle"], "doi"=>["https://dx.doi.org/10.1371/journal.pntd.0001762.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Secondary_adult_hit_screening_of_larval_hits_for_the_Dundee_10_041_Compound_Library_/270137", "title"=>"Secondary adult hit screening of larval hits for the Dundee 10,041 Compound Library.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-07-31 00:02:17"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2012-01-01T00:00:00Z", "end_date"=>"2012-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Developmental biology", "average_usage"=>[313, 562, 690, 796, 893, 986, 1079, 1173, 1257, 1349, 1429, 1506, 1586, 1661, 1730, 1803, 1876, 1945, 2017, 2091, 2163, 2225, 2294, 2362, 2430]}, {"subject_area"=>"/Biology and life sciences/Genetics", "average_usage"=>[333, 576, 707, 814, 908, 1004, 1104, 1197, 1280, 1370, 1449, 1531, 1603, 1673, 1742, 1817, 1886, 1954, 2025, 2098, 2171, 2234, 2304, 2365, 2431]}, {"subject_area"=>"/Medicine and health sciences/Pharmaceutics", "average_usage"=>[305, 566, 705, 814, 911, 1028, 1121, 1228, 1325, 1435, 1532, 1608, 1690, 1769, 1843, 1921, 1997, 2089, 2155, 2225, 2285, 2361, 2433, 2496, 2560]}, {"subject_area"=>"/People and places", "average_usage"=>[330, 562, 680, 783, 875, 964, 1062, 1152, 1240, 1327, 1406, 1483, 1561, 1632, 1701, 1770, 1849, 1922, 1993, 2059, 2131, 2197, 2271, 2335, 2403]}, {"subject_area"=>"/People and places/Population groupings", "average_usage"=>[325, 565, 687, 787, 880, 971, 1065, 1156, 1244, 1339, 1424, 1502, 1582, 1654, 1724, 1794, 1855, 1925, 1997, 2058, 2132, 2207, 2285, 2347, 2412]}]}
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