Graph-based description of tertiary lymphoid organs at single-cell level
| Autor: | Nicolas Brieu, Peter Braubach, Michael Meyer-Hermann, Friedrich Feuerhake, Katharina Nekolla, Germain Forestier, Ralf Schönmeyer, Nadine S. Schaadt |
|---|---|
| Přispěvatelé: | BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany., Hannover Medical School [Hannover] (MHH), Definiens AG, Institut de Recherche en Informatique Mathématiques Automatique Signal (IRIMAS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), TU Braunschweig, Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig] |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
lymphocytes B Cells Support Vector Machine Medical Doctors Computer science Health Care Providers Infographics support vector machines [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Lymphocytic Infiltrate Machine Learning White Blood Cells 0302 clinical medicine Single-cell analysis Animal Cells Medicine and Health Sciences Medical Personnel Lymphocytes Biology (General) Cancer immunology Neurons Ecology Degree (graph theory) T Cells 3. Good health Professions Computational Theory and Mathematics Feature (computer vision) Modeling and Simulation Dendritic Structure [SDV.IMM]Life Sciences [q-bio]/Immunology Female Cellular Types Single-Cell Analysis Graphs Research Article Computer and Information Sciences QH301-705.5 Lymphoid Tissue Immune Cells Immunology T cells Breast Neoplasms dentritic structure 03 medical and health sciences Cellular and Molecular Neuroscience immune cells Artificial Intelligence Support Vector Machines [SDV.MHEP.AHA]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO] Genetics Humans Cluster analysis Antibody-Producing Cells Molecular Biology pathologists Ecology Evolution Behavior and Systematics B cells Blood Cells business.industry Delaunay triangulation Data Visualization Biology and Life Sciences Pattern recognition Cell Biology Neuronal Dendrites Support vector machine Pathologists Health Care 030104 developmental biology Cellular Neuroscience People and Places Population Groupings Artificial intelligence business 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | PLoS computational biology PLoS Computational Biology PLoS Computational Biology, Vol 16, Iss 2, p e1007385 (2020) PLoS Computational Biology, Public Library of Science, 2020, 16 (2), pp.e1007385. ⟨10.1371/journal.pcbi.1007385⟩ |
| ISSN: | 1553-734X 1553-7358 |
| Popis: | Our aim is to complement observer-dependent approaches of immune cell evaluation in microscopy images with reproducible measures for spatial composition of lymphocytic infiltrates. Analyzing such patterns of inflammation is becoming increasingly important for therapeutic decisions, for example in transplantation medicine or cancer immunology. We developed a graph-based assessment of lymphocyte clustering in full whole slide images. Based on cell coordinates detected in the full image, a Delaunay triangulation and distance criteria are used to build neighborhood graphs. The composition of nodes and edges are used for classification, e.g. using a support vector machine. We describe the variability of these infiltrates on CD3/CD20 duplex staining in renal biopsies of long-term functioning allografts, in breast cancer cases, and in lung tissue of cystic fibrosis patients. The assessment includes automated cell detection, identification of regions of interest, and classification of lymphocytic clusters according to their degree of organization. We propose a neighborhood feature which considers the occurrence of edges with a certain type in the graph to distinguish between phenotypically different immune infiltrates. Our work addresses a medical need and provides a scalable framework that can be easily adjusted to the requirements of different research questions. Author summary In this study, we developed a workflow to detect and classify immune infiltrates in giga-pixel microscopy images. It allowed us to measure the degree of organization in lymphocyte clusters with graph-based features and finally to distinguish between tertiary lymphoid organs and other infiltrates. As a clinically relevant use case, we applied it to three different types of tissues and diseases. Our method addresses the need for observer-independent, large-scale evaluation of immune cell patterns and is a prerequisite to capture spatial composition of immune cells that can be used to parameterize mathematical models in systems immunology. |
| Databáze: | OpenAIRE |
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