Quantitative, Architectural Analysis of Immune Cell Subsets in Tumor-Draining Lymph Nodes from Breast Cancer Patients and Healthy Lymph Nodes
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{"title"=>"Quantitative, architectural analysis of immune cell subsets in tumor-draining lymph nodes from breast cancer patients and healthy lymph nodes", "type"=>"journal", "authors"=>[{"first_name"=>"A. Francesca", "last_name"=>"Setiadi", "scopus_author_id"=>"36638437800"}, {"first_name"=>"Nelson C.", "last_name"=>"Ray", "scopus_author_id"=>"36551409500"}, {"first_name"=>"Holbrook E.", "last_name"=>"Kohrt", "scopus_author_id"=>"8071331700"}, {"first_name"=>"Adam", "last_name"=>"Kapelner", "scopus_author_id"=>"22834943200"}, {"first_name"=>"Valeria", "last_name"=>"Carcamo-Cavazos", "scopus_author_id"=>"36551078500"}, {"first_name"=>"Edina B.", "last_name"=>"Levic", "scopus_author_id"=>"36551179700"}, {"first_name"=>"Sina", "last_name"=>"Yadegarynia", "scopus_author_id"=>"36551672700"}, {"first_name"=>"Chris M.", "last_name"=>"van der Loos", "scopus_author_id"=>"7004020188"}, {"first_name"=>"Erich J.", "last_name"=>"Schwartz", "scopus_author_id"=>"7201902154"}, {"first_name"=>"Susan", "last_name"=>"Holmes", "scopus_author_id"=>"7203030601"}, {"first_name"=>"Peter P.", "last_name"=>"Lee", "scopus_author_id"=>"7406120600"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"20811638", "sgr"=>"78049493470", "doi"=>"10.1371/journal.pone.0012420", "scopus"=>"2-s2.0-78049493470", "pui"=>"359754299", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "issn"=>"19326203"}, "id"=>"4f40aba6-9c80-39ee-b85d-1507893a569e", "abstract"=>"BACKGROUND: To date, pathological examination of specimens remains largely qualitative. Quantitative measures of tissue spatial features are generally not captured. To gain additional mechanistic and prognostic insights, a need for quantitative architectural analysis arises in studying immune cell-cancer interactions within the tumor microenvironment and tumor-draining lymph nodes (TDLNs).\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: We present a novel, quantitative image analysis approach incorporating 1) multi-color tissue staining, 2) high-resolution, automated whole-section imaging, 3) custom image analysis software that identifies cell types and locations, and 4) spatial statistical analysis. As a proof of concept, we applied this approach to study the architectural patterns of T and B cells within tumor-draining lymph nodes from breast cancer patients versus healthy lymph nodes. We found that the spatial grouping patterns of T and B cells differed between healthy and breast cancer lymph nodes, and this could be attributed to the lack of B cell localization in the extrafollicular region of the TDLNs.\\n\\nCONCLUSIONS/SIGNIFICANCE: Our integrative approach has made quantitative analysis of complex visual data possible. Our results highlight spatial alterations of immune cells within lymph nodes from breast cancer patients as an independent variable from numerical changes. This opens up new areas of investigations in research and medicine. Future application of this approach will lead to a better understanding of immune changes in the tumor microenvironment and TDLNs, and how they affect clinical outcomes.", "link"=>"http://www.mendeley.com/research/quantitative-architectural-analysis-immune-cell-subsets-tumordraining-lymph-nodes-breast-cancer-pati", "reader_count"=>53, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>12, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>4, "Other"=>6, "Student > Master"=>7, "Student > Bachelor"=>2, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>12, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>12, "Student > Postgraduate"=>4, "Other"=>6, "Student > Master"=>7, "Student > Bachelor"=>2, "Professor"=>5}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>2, "Medicine and Dentistry"=>13, "Agricultural and Biological Sciences"=>23, "Physics and Astronomy"=>1, "Computer Science"=>4, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>13}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>23}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>1, "Denmark"=>1, "Macedonia"=>1, "Switzerland"=>1, "Germany"=>1, "India"=>1}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/833172"], "description"=>"<p>Stage 1: multicolor staining of tissue sections. Stage 2: high-resolution spectral imaging and automated scanning of the entire tissue section. Stage 3: machine-learning-based cell identification by GemIdent. Stage 4: numerical and spatial statistical analyses, such as cell densities, distances and distributions.</p>", "links"=>[], "tags"=>["immunology/immune response", "oncology/breast cancer", "pathology/immunology"], "article_id"=>503529, "categories"=>["Immunology", "Plant Biology"], "users"=>["A. Francesca Setiadi", "Nelson C. Ray", "Holbrook E. Kohrt", "Adam Kapelner", "Valeria Carcamo-Cavazos", "Edina B. Levic", "Sina Yadegarynia", "Chris M. van der Loos", "Erich J. Schwartz", "Susan Holmes", "Peter P. Lee"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012420.g001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Integrated_image_analysis_approach_/503529", "title"=>"Integrated image analysis approach.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-25 00:58:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/415305", "https://ndownloader.figshare.com/files/415318", "https://ndownloader.figshare.com/files/415336", "https://ndownloader.figshare.com/files/415348"], "description"=>"<div><h3>Background</h3><p>To date, pathological examination of specimens remains largely qualitative. Quantitative measures of tissue spatial features are generally not captured. To gain additional mechanistic and prognostic insights, a need for quantitative architectural analysis arises in studying immune cell-cancer interactions within the tumor microenvironment and tumor-draining lymph nodes (TDLNs).</p> <h3>Methodology/Principal Findings</h3><p>We present a novel, quantitative image analysis approach incorporating 1) multi-color tissue staining, 2) high-resolution, automated whole-section imaging, 3) custom image analysis software that identifies cell types and locations, and 4) spatial statistical analysis. As a proof of concept, we applied this approach to study the architectural patterns of T and B cells within tumor-draining lymph nodes from breast cancer patients versus healthy lymph nodes. We found that the spatial grouping patterns of T and B cells differed between healthy and breast cancer lymph nodes, and this could be attributed to the lack of B cell localization in the extrafollicular region of the TDLNs.</p> <h3>Conclusions/Significance</h3><p>Our integrative approach has made quantitative analysis of complex visual data possible. Our results highlight spatial alterations of immune cells within lymph nodes from breast cancer patients as an independent variable from numerical changes. This opens up new areas of investigations in research and medicine. Future application of this approach will lead to a better understanding of immune changes in the tumor microenvironment and TDLNs, and how they affect clinical outcomes.</p> </div>", "links"=>[], "tags"=>["architectural", "subsets", "tumor-draining", "lymph", "nodes", "cancer", "patients"], "article_id"=>141998, "categories"=>["Immunology", "Cell Biology"], "users"=>["A. Francesca Setiadi", "Nelson C. Ray", "Holbrook E. Kohrt", "Adam Kapelner", "Valeria Carcamo-Cavazos", "Edina B. Levic", "Sina Yadegarynia", "Chris M. van der Loos", "Erich J. Schwartz", "Susan Holmes", "Peter P. Lee"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0012420.s001", "https://dx.doi.org/10.1371/journal.pone.0012420.s002", "https://dx.doi.org/10.1371/journal.pone.0012420.s003", "https://dx.doi.org/10.1371/journal.pone.0012420.s004"], "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Quantitative_Architectural_Analysis_of_Immune_Cell_Subsets_in_Tumor_Draining_Lymph_Nodes_from_Breast_Cancer_Patients_and_Healthy_Lymph_Nodes/141998", "title"=>"Quantitative, Architectural Analysis of Immune Cell Subsets in Tumor-Draining Lymph Nodes from Breast Cancer Patients and Healthy Lymph Nodes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-08-25 00:33:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/833657"], "description"=>"<p>The proportion of B cell isolates is the highest in the HLNs. On whole-section images: blue: T cells; brown: B cells; red: tumor cells. On density plots: red dots: B cell isolates (lowest 15% of density); yellow and green: B cell aggregates (highest 85% of density); lines: tumor contours.</p>", "links"=>[], "tags"=>["whole-section", "images", "plots", "cells"], "article_id"=>504017, "categories"=>["Immunology", "Plant Biology"], "users"=>["A. Francesca Setiadi", "Nelson C. Ray", "Holbrook E. Kohrt", "Adam Kapelner", "Valeria Carcamo-Cavazos", "Edina B. Levic", "Sina Yadegarynia", "Chris M. van der Loos", "Erich J. Schwartz", "Susan Holmes", "Peter P. Lee"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012420.g005", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Representative_whole_section_images_and_density_plots_of_B_cells_in_an_ALN_ALN_8722_and_HLN_/504017", "title"=>"Representative whole-section images and density plots of B cells in an ALN+, ALN− and HLN.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-25 01:06:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/833813"], "description"=>"<p>Comparison of the percentages of lymph nodes found with matching spatial grouping patterns of T and B cells and the average proportion of B cell isolates in tumor-free regions.</p>", "links"=>[], "tags"=>["percentages", "lymph", "nodes", "matching", "spatial", "grouping", "patterns", "cells", "isolates", "tumor-free"], "article_id"=>504157, "categories"=>["Immunology", "Plant Biology"], "users"=>["A. Francesca Setiadi", "Nelson C. Ray", "Holbrook E. Kohrt", "Adam Kapelner", "Valeria Carcamo-Cavazos", "Edina B. Levic", "Sina Yadegarynia", "Chris M. van der Loos", "Erich J. Schwartz", "Susan Holmes", "Peter P. Lee"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012420.t001", "stats"=>{"downloads"=>0, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_percentages_of_lymph_nodes_found_with_matching_spatial_grouping_patterns_of_T_and_B_cells_and_the_average_proportion_of_B_cell_isolates_in_tumor_free_regions_/504157", "title"=>"Comparison of the percentages of lymph nodes found with matching spatial grouping patterns of T and B cells and the average proportion of B cell isolates in tumor-free regions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-08-25 01:09:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/833525"], "description"=>"<p>(A) A representative cross-section from lymph nodes with similar T and B cell spatial grouping patterns, as indicated by the L function analysis. (B) A representative cross-section from lymph nodes more clustered B than T cells, as indicated by the L function analysis.</p>", "links"=>[], "tags"=>["images", "corresponding", "maps"], "article_id"=>503886, "categories"=>["Immunology", "Plant Biology"], "users"=>["A. Francesca Setiadi", "Nelson C. Ray", "Holbrook E. Kohrt", "Adam Kapelner", "Valeria Carcamo-Cavazos", "Edina B. Levic", "Sina Yadegarynia", "Chris M. van der Loos", "Erich J. Schwartz", "Susan Holmes", "Peter P. Lee"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012420.g004", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Whole_section_images_and_corresponding_maps_of_T_and_B_cell_distributions_/503886", "title"=>"Whole-section images and corresponding maps of T and B cell distributions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-25 01:04:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/833394"], "description"=>"<p>Each line represents data from 1 lymph node. The shorter the interpoint distance at which the B cells' L function exited above the confidence envelope, the more clustered B cells are compared to the T cells. Below the interpoint distance of 100 µm, the number of lines that represent the L function of B cells outside of the T cells' envelope is as follows: 9 out of 10 (90%) ALNs+, 6 out of 10 ALNs−, 8 out of 15 (53%) SLNs+ and 3 out of 7 (43%) HLNs. The counts indicated how much more clustered B cells are compared to T cells, and were determined as follows: we evaluated the L-function of the B cells for interpoint distances of 0 to 100 µm in 2 µm increments and compared it with the confidence envelope of the L-function for the T cells. For each interpoint distance increment of 2 µm for which the L-function of the B cells lay above the envelope, the value of the counts increased by one. The starting point was set at 0.</p>", "links"=>[], "tags"=>["illustrating", "tdlns", "hlns", "cells", "clustered"], "article_id"=>503749, "categories"=>["Immunology", "Plant Biology"], "users"=>["A. Francesca Setiadi", "Nelson C. Ray", "Holbrook E. Kohrt", "Adam Kapelner", "Valeria Carcamo-Cavazos", "Edina B. Levic", "Sina Yadegarynia", "Chris M. van der Loos", "Erich J. Schwartz", "Susan Holmes", "Peter P. Lee"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012420.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Plots_illustrating_the_number_of_TDLNs_and_HLNs_in_which_B_cells_were_found_to_be_more_clustered_than_the_T_cells_/503749", "title"=>"Plots illustrating the number of TDLNs and HLNs in which B cells were found to be more clustered than the T cells.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-25 01:02:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/833291"], "description"=>"<p>(A) In most TDLNs, B cells are more clustered than the T cells, as indicated by upward deflection of the L function of B cells above the confidence envelope. (B) In the majority of the HLNs, T and B cells have a similar tendency to group or disperse at various interpoint distances, as indicated by the L function of B cells that remains within the confidence envelope.</p>", "links"=>[], "tags"=>["plots", "cells", "tdlns"], "article_id"=>503649, "categories"=>["Immunology", "Plant Biology"], "users"=>["A. Francesca Setiadi", "Nelson C. Ray", "Holbrook E. Kohrt", "Adam Kapelner", "Valeria Carcamo-Cavazos", "Edina B. Levic", "Sina Yadegarynia", "Chris M. van der Loos", "Erich J. Schwartz", "Susan Holmes", "Peter P. Lee"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012420.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Representative_L_function_plots_of_T_and_B_cells_in_TDLNs_and_HLNs_/503649", "title"=>"Representative L function plots of T and B cells in TDLNs and HLNs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-25 01:00:49"}

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